NY Creates, the Albany-based New York Center for Research, Economic Advancement, Technology, Engineering, and Science, has announced its participation in the newly funded Manufacturable and Resilient superconducting QUantum Information Systems, or MARQUIS, Quantum Leap Challenge Institute. The institute is a flagship program of the U.S. National Science Foundation and is led by Princeton University. According to the agency, MARQUIS is receiving $27.9 million in NSF funding over five years, and NY Creates will receive approximately $1.25 million of that total to support research, technology scaling, and workforce development activities aimed at advancing quantum computing technologies.

The central obstacle MARQUIS confronts is one that has shadowed superconducting quantum computing since its earliest days: the materials and fabrication techniques underpinning today’s qubits have barely evolved in decades. Nathalie de Leon, professor of electrical and computer engineering at Princeton University and director of the new institute, described the problem bluntly. “The whole community has been using essentially the same materials technology for about a quarter century,” she said. “That technology has worked well for experimental prototypes and small-scale systems. But to build quantum computers at a scientifically useful scale, the most basic elements must be reinvented.” She added that having a few key experts in the field who know what the right waypoints are and how to think about it is really crucial.

Understanding why this reinvention is necessary requires a brief look at how superconducting quantum computers work. At the heart of these machines are qubits, the quantum analogues of the bits in classical computing. Unlike ordinary bits, which must be either zero or one, qubits can exist in superpositions of both states simultaneously, and they can become entangled with one another in ways that allow certain calculations to be performed with dramatically fewer resources than classical machines would require. Superconducting qubits, the approach pursued by MARQUIS, rely on tiny electrical circuits cooled to temperatures near absolute zero, where resistance vanishes and quantum effects dominate. These circuits incorporate Josephson junctions, nanoscale sandwich structures of superconducting materials separated by insulating barriers, which act as the nonlinear elements that make quantum computation possible.

The trouble is that these junctions and the materials surrounding them are exquisitely sensitive. Microscopic defects, impurities, and inconsistencies introduced during fabrication can cause decoherence, the process by which fragile quantum states leak information to their environment and collapse into useless classical noise. As researchers scale from processors with dozens or hundreds of qubits to the thousands or millions needed for scientifically and commercially meaningful computation, the variability of fabrication becomes an increasingly punishing bottleneck. A process that works acceptably for a handful of devices in a university cleanroom may fail catastrophically when applied across an entire wafer of thousands of components.

This is precisely where NY Creates brings distinctive value to the consortium. The organization operates advanced 300-millimeter semiconductor research and development infrastructure, the wafer format that dominates industrial chip manufacturing worldwide. As part of MARQUIS, the NY Creates team, led by Dr. Satyavolu Papa Rao, Senior Director of Emerging Technologies and Research at the organization, will work with institute partners to test innovative materials and processes at the 300-millimeter wafer scale, targeting mid-scale superconducting quantum processors. Working at wafer scale means that new materials and fabrication recipes can be evaluated under conditions that closely resemble real industrial production, rather than the small-batch conditions typical of academic laboratories.

“Creates is uniquely positioned to help bridge the gap between breakthrough quantum research and scalable manufacturing,” Papa Rao said. “Through our advanced 300mm semiconductor R&D infrastructure and deep industry partnerships, we will work alongside MARQUIS collaborators to evaluate, translate, and scale promising quantum materials and processes, helping to accelerate the development of manufacturable quantum technologies while strengthening the nation’s quantum workforce and semiconductor innovation ecosystem.”

Beyond testing new materials, the NY Creates team will collaborate with MARQUIS researchers to identify critical data and process insights that could improve the scalability of materials and fabrication approaches for quantum devices within industry-standard CMOS-compatible manufacturing environments. CMOS, the complementary metal-oxide-semiconductor technology that underpins virtually all modern electronics, represents the gold standard for manufacturability. Ensuring that quantum device innovations remain compatible with CMOS-style processes is a strategic bet: it means that whatever succeeds in the laboratory has a plausible path into the enormous existing infrastructure of the semiconductor industry, rather than requiring an entirely new fabrication ecosystem to be built from scratch.

The institute’s ambitions extend well beyond fabrication alone. NY Creates will also support MARQUIS efforts to develop databases of materials, processes, and device structures that can help researchers better understand the factors influencing quantum device performance, decoherence, and process variability. Such databases could prove transformative for the field, which has historically relied on scattered, hard-to-compare experimental results. Systematic, shared data on how specific material combinations and processing steps affect qubit coherence times and yield would allow researchers to iterate far more quickly, applying the kind of data-driven optimization that revolutionized classical semiconductor development over past decades.

Workforce development forms a third pillar of NY Creates’ contribution, and it addresses a widely recognized vulnerability in the American quantum enterprise. The quantum industry is growing faster than the supply of people trained to work in it, and the specialized skills required, spanning quantum physics, cryogenic engineering, materials science, and precision fabrication, are scarce. Through MARQUIS, NY Creates will work with institute partners to expand opportunities for students and postdoctoral researchers to gain hands-on experience with advanced semiconductor manufacturing environments through periodic site visits and collaborative learning experiences. The goal is to cultivate a generation of scientists, engineers, and technologists who are equally comfortable with quantum theory and with the practical realities of high-volume chip fabrication.

The breadth of the consortium reflects the scale of the challenge. The Princeton-led MARQUIS institute brings together researchers from Princeton University, Cornell University, the Massachusetts Institute of Technology, the University of California at Santa Barbara, Stanford University, Dartmouth College, NY Creates, Michigan State University, and the University of Iowa. These institutions contribute expertise across materials science, quantum devices, and semiconductor processing, spanning two dozen laboratories in total. The initiative is further supported by an advisory board that includes representatives from Google Quantum AI, NVIDIA, Applied Materials, Oxford Instruments, Bluefors, KU Leuven/imec, and MIT Lincoln Laboratory, a lineup that spans the leading quantum computing companies, the dominant suppliers of semiconductor fabrication equipment, and the major providers of cryogenic systems essential to superconducting quantum hardware.

Dave Anderson, President and CEO of NY Creates, framed the award as a milestone for both the organization and the region. “This award reflects the growing importance of integrating world-class semiconductor manufacturing expertise with leading-edge quantum research,” Anderson said. “As a partner in this exceptional consortium of leading universities, industry partners, and research institutions, Creates will help advance technologies that are critical to the future of quantum computing and America’s scientific and economic competitiveness. The MARQUIS initiative also reinforces New York’s leadership in semiconductor innovation while creating valuable opportunities to educate and train the next generation of scientists, engineers, and technologists who will drive quantum-based economic growth.”

The stakes for the United States are considerable. Quantum computing is widely viewed as a strategic technology, one with potential implications for national security, pharmaceutical development, financial modeling, and materials design. Nations around the world, including China and members of the European Union, have committed billions of dollars to quantum research and development, and leadership in the field is increasingly seen as inseparable from broader economic and technological competitiveness. By pairing the fundamental scientific expertise of its leading universities with the manufacturing know-how embedded in organizations like NY Creates, the MARQUIS institute represents a deliberate attempt to ensure that American quantum discoveries do not stall at the prototype stage but instead flow into scalable, domestic production.

MARQUIS is one of several Quantum Leap Challenge Institutes established by the National Science Foundation as part of a broader federal push in quantum science, and it embodies a philosophy that is gaining traction across the field: the bottleneck to useful quantum computers is no longer only a matter of physics but also of engineering discipline, manufacturing rigor, and human capital. If the institute succeeds in reinventing the basic elements of superconducting qubits and embedding them in CMOS-compatible, wafer-scale processes, the consequences could ripple far beyond the laboratory. Quantum processors that can be fabricated reliably, in quantity, and with predictable performance would move the field decisively closer to the day when quantum machines tackle problems no classical computer ever could, and the groundwork for that transition is now being laid, one wafer at a time, in Albany and its partner institutions across the country.

Subject of Research: Manufacturable superconducting quantum computing hardware and fabrication

Article Title: NY Creates joins Princeton-led NSF Quantum Institute to advance next-generation quantum computing

Article References: NY Creates joins Princeton-led NSF Quantum Institute to advance next-generation quantum computing. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: quantum computing, superconducting qubits, MARQUIS, National Science Foundation, Princeton University, NY Creates, semiconductor manufacturing, 300mm wafer, CMOS compatibility, decoherence, workforce development, Josephson junctions

The research team, led by Professor Masayuki Tanaka and Professor Masatoshi Okutomi from the Department of Systems and Control Engineering at Science Tokyo, addressed one of the most persistent failure modes in multi-object tracking: the identity switch. When a camera loses sight of a person because of occlusion by another person, an object, or any other obstruction, the tracking pipeline often treats the reappearing individual as a brand-new subject and assigns a fresh identifier. Multiply this across several cameras and the result is a fragmented record in which the same person appears as multiple phantom individuals. Maintaining a consistent identity across camera views therefore remains a central challenge for anyone building systems that must follow people through real spaces, from security operators to transportation planners.

The key insight behind the new method is that two complementary clues can be combined to solve the association problem. The first clue is geometric. When two cameras observe the same scene from different positions, the mathematical relationship between their views is captured by what computer vision researchers call epipolar geometry. This geometry constrains where an object seen in one image can possibly appear in the other: the candidate location must lie along a specific line, known as the epipolar line, determined by the relative positions and orientations of the two cameras. The researchers exploit this constraint by calculating an epipolar distance for each potential match, a measure of how far a candidate deviates from the geometrically consistent region. Candidates that fall too far from the expected line can be eliminated outright, dramatically shrinking the pool of possible matches before any visual comparison is attempted.

The second clue is appearance. Once geometry has narrowed the field, the system compares visual features extracted from the images of the remaining candidates. These features, drawn from pre-trained models, encode what a person looks like, their clothing, build, and other visible characteristics, allowing the system to distinguish between multiple people who all happen to lie along the same epipolar line. The order of operations matters. By using epipolar geometry to verify spatial consistency first and only then applying appearance-based matching, the system combines the strengths of both signals: geometry rules out physically impossible pairings, while appearance resolves the remaining ambiguity. As Tanaka explains, combining epipolar geometry with appearance similarity allows the geometric relationship between cameras to constrain possible matches, after which visual information distinguishes between them.

A particularly attractive feature of the approach is that it does not require building a new tracking system from scratch. Instead, it is designed to plug into existing single-camera tracking pipelines. Each camera independently detects and tracks people, producing short sequences of detections called tracklets. These tracklets are often fragmented, broken whenever a person is temporarily lost from view. The proposed method then performs cross-camera association, deciding which tracklet fragments from different cameras most likely belong to the same individual. Because the association step relies on the known geometric relationships between cameras together with pre-trained appearance features, it does not require additional training for each new environment. Tanaka notes that this means cross-camera track association can be deployed without environment-specific retraining, a significant practical advantage over approaches that must be tuned to the particular layout and lighting of every installation.

To evaluate the method rigorously, the team tested it on two established multi-camera tracking benchmarks: MMPTrack and CAMPUS. Both datasets contain synchronized video from multiple camera views and are specifically designed to measure how well tracking systems preserve person identities over time. The primary metric for identity consistency is IDF1, which scores how faithfully a system maintains the correct identity of each person across frames. The researchers also reported results on Higher Order Tracking Accuracy, or HOTA, a metric that jointly assesses two distinct capabilities: how accurately people are detected in the first place, and how consistently their identities are tracked once detected. Reporting both metrics matters because a system can excel at one while failing at the other, and real-world deployments need both to succeed simultaneously.

The results showed clear gains on identity preservation. On MMPTrack, the proposed method achieved an average IDF1 score of 65.48, compared with 62.30 for MCTR, an existing multi-camera tracking method. On HOTA, the new approach scored 56.92, essentially matching the 55.77 achieved by MCTR, indicating that the improvement came specifically from better identity association rather than from changes in detection behavior. On the CAMPUS benchmark, the method reached an average IDF1 of 47.37, outperforming ByteTrack, a well-known single-camera tracking method, which scored 44.72. Taken together, the numbers suggest that adding geometric and appearance-based cross-camera association on top of standard single-camera trackers yields measurable improvements in exactly the area where multi-camera systems struggle most: keeping identities stable across views.

The evaluation also surfaced an honest limitation that the researchers themselves highlight. Severe occlusion can cause people to be missed entirely during detection, meaning no tracklet is generated for the association stage to work with. If a person is never detected in one of the camera views, no amount of clever matching can link them across cameras, because there is simply nothing to link. This observation underscores an important structural point about multi-camera tracking: reliable performance depends not only on accurately matching observations across views but also on consistently detecting people in the first place. Detection and association are chained together, and a weakness in the first link caps the performance of the second, no matter how sophisticated the matching algorithm becomes.

Looking forward, the researchers see two natural directions for extending the work. The first is improving person detection itself, particularly under the severe occlusion conditions that currently cause missed detections. The second is refining cross-camera track association so that it remains robust in increasingly crowded and complex environments, where many people move through overlapping fields of view and occlusions are frequent rather than exceptional. Progress on both fronts could extend the approach to settings that are far more challenging than current benchmarks, such as dense pedestrian zones, transit hubs during peak hours, or large public events where hundreds of people cross camera boundaries every minute.

The potential applications extend well beyond the laboratory. Any system that must maintain consistent identities across multiple viewpoints stands to benefit, including security monitoring, transportation management, facility operations, and pedestrian-flow analysis. In security contexts, stable identities mean that a person of interest remains a single coherent record as they move between cameras, rather than dissolving into a confusing set of fragments. In transportation and facility management, accurate pedestrian-flow statistics depend on counting each person once, not several times over, which requires precisely the kind of cross-camera identity consistency this method provides. By combining the physical rigor of epipolar geometry with the discriminative power of modern appearance features, and by doing so in a way that integrates with existing tracking infrastructure without retraining, the Science Tokyo team has offered the field a template for multi-camera tracking that is both technically principled and practically deployable.

Subject of Research: Multi-camera multi-object tracking using epipolar distance and appearance similarity

Article Title: Improving identity-tracking across multiple cameras with geometry and appearance

Article References: Improving identity-tracking across multiple cameras with geometry and appearance. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: multi-camera tracking, computer vision, epipolar geometry, appearance similarity, identity switches, tracklet association, IDF1, HOTA, person re-identification, occlusion, surveillance, Institute of Science Tokyo

The study, led by corresponding author Tracy Vaillancourt of the Faculty of Education at the University of Ottawa, examined whether what the researchers describe as affective generative artificial intelligence use, meaning the use of AI tools for emotional rather than practical purposes, was associated with poorer mental health among youth. The central question was whether this behavior merely overlaps with known correlates of distress, such as loneliness or a diminished sense of mattering to others, or whether it represents something separate that clinicians and educators should pay attention to in its own right. The findings point to the latter: emotional reliance on AI emerged as a unique marker of psychological distress, independent of both mattering and loneliness.

This distinction matters because loneliness and a lack of mattering have long been recognized as powerful predictors of poor mental health outcomes in young people. Loneliness captures the painful gap between the social connection a person wants and the connection they actually experience, while mattering reflects the sense of being significant to others, of being noticed and valued by the people around them. Both constructs are closely tied to depression, anxiety, and suicidal ideation in adolescents. If turning to AI for emotional support were simply a symptom of loneliness or of feeling invisible, it would arguably add little new information. The fact that the association with distress persists even after accounting for these factors suggests that affective AI use is tapping into something additional about a young person’s inner life.

The researchers frame their conclusions around a practical and increasingly urgent distinction: the difference between functional AI assistance and the use of algorithmic interfaces as a digital refuge for emotional needs. Functional assistance covers the many legitimate and often beneficial ways young people interact with generative AI, such as getting help with homework, brainstorming ideas, answering factual questions, or learning new skills. Affective use, by contrast, involves bringing emotional burdens to a machine, asking a chatbot for comfort, confiding in it about painful feelings, or treating it as a substitute source of understanding and support. The study suggests that clinical frameworks and digital literacy programs should draw a clear line between these two patterns of behavior, because they may carry very different implications for a child’s wellbeing.

The timing of this research is significant. Generative AI tools have moved from research laboratories into everyday life at remarkable speed, and children and adolescents are among their most enthusiastic adopters. Conversational agents are now available around the clock, respond instantly, never judge, and can simulate empathy with convincing fluency. For a young person who feels isolated, misunderstood, or burdened, the appeal of such an interlocutor is easy to understand. Yet the same qualities that make these systems attractive also raise questions about what it means when a developing mind begins to route its emotional needs through an algorithm rather than through family, friends, teachers, or mental health professionals.

From a clinical standpoint, the study’s findings carry implications for how practitioners assess and respond to psychological distress in youth. If asking an AI for emotional support is a unique marker of distress, then simply asking a young patient whether they use AI tools, and how they use them, could provide clinicians with diagnostically meaningful information that might otherwise go uncollected. A teenager who reports using chatbots primarily for schoolwork presents a very different picture from one who reports turning to them when feeling sad, anxious, or alone. The authors argue that clinical frameworks should be updated to make this distinction explicit, so that affective AI use is recognized and explored rather than overlooked or lumped together with benign technology use.

The implications extend beyond the clinic into schools and public health more broadly. Digital literacy programs have traditionally focused on topics such as online safety, privacy, misinformation, and screen time. The present findings suggest that these programs should also help young people understand the difference between using AI as a tool and using it as an emotional refuge. Teaching children and adolescents to recognize when they are seeking comfort from a machine, and to reflect on what that impulse might be telling them about their own emotional state, could turn a passive risk factor into an opportunity for self-awareness. Equally, such programs could help adults, including parents and teachers, understand that a child’s growing attachment to conversational AI may be less about fascination with technology and more about unmet emotional needs.

The study is published alongside an editorial and an editor’s note in the JAMA Network, reflecting the significance that the journal’s editors attach to the topic. Cross-sectional research of this kind captures a snapshot in time, which means it can identify associations but cannot establish whether emotional distress drives young people toward AI, whether heavy affective AI use contributes to distress, or whether both patterns are shaped by other underlying factors. The authors are careful to describe the relationship as a marker rather than a cause, a distinction that matters for interpretation. What the design does establish is that the association is real and independent: even when loneliness and mattering are held constant, young people who seek emotional support from generative AI show higher levels of psychological distress.

That independence is the study’s most striking contribution. It suggests that affective AI use is not reducible to the social deficits that mental health professionals already screen for. A young person might report feeling connected to others and valued by them, yet still turn to an algorithm for emotional support, and that behavior alone would flag elevated distress. This makes the behavior potentially valuable as an early signal, something visible on the surface of everyday technology use that hints at struggles happening beneath it. In an era when parents and educators often struggle to detect internalizing problems in children, precisely because conditions such as depression and anxiety tend to be hidden rather than disruptive, a marker embedded in routine digital behavior could prove genuinely useful.

The broader conversation about AI and youth mental health is only beginning, and this study adds an important empirical data point to a debate that has so far been dominated by speculation. As generative AI becomes more emotionally sophisticated, more personalized, and more deeply woven into the daily routines of young people, the questions raised by this research will only grow in urgency. The authors’ central message is measured rather than alarmist: the goal is not to condemn AI use among youth, much of which is functional and harmless, but to recognize that when children and adolescents begin treating algorithmic interfaces as a refuge for their emotional lives, adults should pay attention. Distinguishing between a young person using AI to finish an assignment and one using it to fill an emotional void may become one of the more consequential skills for the clinicians, educators, and families raising children in the age of artificial intelligence.

Subject of Research: The association between affective use of generative artificial intelligence and psychological distress in children and adolescents

Article Title: Affective generative artificial intelligence use and youth mental health

Article References: Affective generative artificial intelligence use and youth mental health. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: generative AI, youth mental health, psychological distress, loneliness, mattering, JAMA Pediatrics, adolescents, digital literacy, chatbots, emotional support, cross-sectional study, clinical frameworks

The difficulty stems from the sheer number of factors that shape collective dynamics. The behavior of any single component in a network depends not only on its own internal dynamics but also on how many connections it has, how strong those connections are, what kinds of interactions its neighbors exert upon it, and how long it takes for information or influence to propagate through the network. Structural complexity and time delays are known to matter individually, but how they combine to determine the boundary between stability and oscillation has remained poorly understood, particularly for large systems whose architecture resists straightforward analysis.

A new study published in National Science Research addresses this gap with an analytical framework that brings these ingredients together in a single, tractable description. The work shows how network complexity, propagation delays and interaction types jointly determine the transition between two contrasting dynamical regimes: amplitude death, in which coupling suppresses oscillations and the network settles into a steady state, and sustained oscillations, in which rhythmic activity persists indefinitely across the system. By deriving explicit relationships among these quantities, the framework turns a question that previously could only be probed case by case through simulation into one that can be answered analytically.

The mathematical backbone of the study is the network of coupled Stuart-Landau oscillators, a widely used and well-understood model for oscillatory dynamics. Each oscillator in such a network behaves, in isolation, like a simple limit-cycle system whose amplitude and phase evolve according to well-characterized equations. When many such oscillators are coupled together, the collective behavior depends delicately on the coupling structure. This makes the Stuart-Landau framework an ideal testing ground: it is simple enough to permit rigorous analysis, yet rich enough to display the full range of collective phenomena observed in real networks, from complete synchronization to the complete suppression of activity known as amplitude death.

Working within this setting, the researchers derived a relationship between the effective complexity of the network and the critical delay at which a previously stable system begins to oscillate. The resulting picture is striking. Increasing network complexity generally reduces the amount of propagation delay needed to trigger oscillations, meaning that richer, more highly connected architectures are intrinsically closer to the oscillatory regime. In sufficiently complex networks, the analysis shows, sustained oscillations can emerge even in the complete absence of propagation delay. Complexity alone, in other words, can be enough to destabilize a steady state that would remain perfectly stable in a simpler network with identical components and coupling strengths.

The framework goes further by examining how the nature of the interactions between nodes reshapes this transition boundary. The researchers considered four interaction types: cooperative, in which connections reinforce one another; competitive, in which connections oppose one another; mixed, combining both; and random. Each type was found to modify the transition boundary in the plane spanned by complexity and delay. The ordering that emerges is clear and systematic. Cooperative interactions promote the onset of sustained oscillations most readily, requiring the lowest critical complexity level for rhythmic activity to appear. Competitive, mixed and random interactions demand progressively higher critical complexity levels before sustained oscillations can emerge, so the same network architecture that oscillates readily under cooperative coupling may remain steady under competitive coupling.

This interaction-dependent hierarchy carries practical implications for any field in which network design matters. In engineering contexts such as power grids or communication networks, where oscillations can be destructive, the results suggest that the sign and structure of interactions should be treated as a design parameter on equal footing with topology and delay management. In biological contexts, where cooperative interactions are common, the findings offer a possible explanation for why rhythmic behavior arises so readily in neural circuits, gene regulatory networks and ecological communities: the interaction structure itself may lower the barrier to oscillation, allowing rhythms to emerge without requiring long propagation delays or extreme architectural complexity.

A central strength of the study lies in its effort to verify that the predicted transitions survive contact with the physical world rather than existing only in idealized computation. To this end, the researchers constructed a digital-analog hardware emulation platform. A microcontroller updated the network dynamics in real time, while external electronic circuits converted selected network states into measurable voltage signals. This hardware-in-the-loop approach deliberately exposed the theoretical predictions to the imperfections of real instrumentation, including finite sampling rates, signal quantization, transistor switching and other implementation artifacts that are absent from purely numerical experiments.

The observed hardware transitions matched the predicted critical delays, providing evidence that the framework captures a robust physical phenomenon rather than a fragile artifact of floating-point arithmetic. The agreement across three independent levels of scrutiny, namely analytical derivation, numerical simulation and hardware emulation, indicates that the complexity-delay transition remains observable in systems subject to the sampling, quantization and noise inherent in practical implementations. For researchers who wish to apply these results to real engineered or biological systems, this robustness is arguably as important as the analytical results themselves, because real networks never satisfy idealized assumptions exactly.

To demonstrate applicability beyond synthetic architectures, the researchers further applied their framework to the connectome of the nematode worm Caenorhabditis elegans, one of the most completely mapped neural networks in biology. The analysis of this biologically derived topology illustrated how the theoretical tools can be carried over to empirical network data, opening a path toward assessing whether the oscillatory tendencies of real neural systems can be anticipated from their structural complexity, interaction types and signal propagation delays alone. Taken together, the study offers a unified lens on a question that touches neuroscience, ecology, epidemiology and engineering alike: when does a network hold steady, and when does it begin to sing? By showing that complexity, delay and interaction type jointly draw the boundary, and by validating that boundary in hardware and in a real connectome, the work provides both a conceptual map and a practical toolkit for navigating the transition between silence and rhythm in complex systems.

Subject of Research: Transitions between amplitude death and sustained oscillations in complex networks of coupled oscillators

Article Title: What shape the oscillatory transitions in complex networks?

Article References: What shape the oscillatory transitions in complex networks?. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: complex networks, oscillations, amplitude death, Stuart-Landau oscillators, propagation delays, network complexity, cooperative interactions, competitive interactions, coupled oscillators, hardware emulation, C. elegans, dynamical systems

Brenda Betancourt, a Term Associate Professor of Statistics in George Mason’s College of Engineering and Computing, has received $286,130 from the U.S. Food and Drug Administration for a project titled Regulatory Grade Frameworks for Assessing Record Linkage Uncertainty in Real-World Evidence. The funding began in September 2026 and runs through September 2028. Over those two years, Betancourt will evaluate how uncertainty in record linkage affects the reliability, interpretability, and evidentiary confidence of analyses based on linked real-world data that are used to support FDA regulatory decision-making. In other words, the project asks a deceptively simple question: if the links between records are not certain, how certain can the conclusions drawn from them be?

The stakes are considerable. Record linkage is the invisible plumbing of real-world evidence. When a researcher wants to know whether patients exposed to a medication later experienced a particular adverse event, the exposure records typically live in one database, perhaps pharmacy claims, while the outcome records live in another, perhaps a hospital discharge registry. Joining those datasets requires deciding which rows refer to the same individual, often without a shared unique identifier such as a national patient number. Probabilistic matching algorithms compare names, dates of birth, addresses, and other fields, assigning scores that indicate how likely two records are to describe the same person. Every one of those assignments carries a probability of error, and those errors propagate silently into every downstream estimate.

Statisticians have long understood that linkage errors can bias results in either direction. False matches, in which records from two different people are merged, can dilute genuine signals or manufacture spurious associations. Missed matches, in which records from the same person remain split, can fragment a patient’s medical history and undercount exposures or outcomes. The direction and magnitude of the bias depend on the nature of the analysis, the matching variables, and the population being studied. What has been missing, particularly in the regulatory context, is a standardized framework that quantifies this uncertainty in a way that meets the evidentiary bar regulators demand when a decision affects public health.

That is the gap Betancourt’s project is designed to fill. The phrase regulatory grade in the project title signals the ambition: not merely to measure linkage uncertainty in an academic sense, but to develop frameworks rigorous and transparent enough to be used in submissions that inform FDA decisions. Regulatory science operates under constraints that academic research often does not. Methods must be reproducible, their assumptions explicit, their outputs interpretable by reviewers who must weigh evidence under statutory standards. A framework that cannot communicate how much of a reported effect might be attributable to linkage error is of limited use to an agency deciding whether to approve, restrict, or warn about a therapy.

The mathematical core of the problem is rich. Probabilistic linkage models, descendants of the classical Fellegi-Sunter framework, estimate the likelihood that a pair of records matches based on agreement and disagreement patterns across comparison fields. More modern approaches bring in Bayesian modeling, clustering, and machine learning classifiers, each producing not a single deterministic assignment but a posterior distribution over possible linkages. The challenge is propagation: how does uncertainty in those assignments flow through data cleaning, cohort construction, statistical modeling, and ultimately into confidence intervals and p-values for the regulatory question at hand? Ignoring that propagation can yield analyses that appear far more precise than they actually are, a phenomenon statisticians describe as understated uncertainty.

Several strategies exist in principle for handling this propagation. One is to condition on the most probable linkage and then adjust estimates using sensitivity analyses that vary the assumed error rates. Another is multiple imputation over plausible linkages, generating several alternative versions of the linked dataset and combining the resulting estimates so that the final inference reflects linkage ambiguity. A third embeds the linkage model and the analysis model in a joint Bayesian framework, allowing uncertainty to be integrated out rather than fixed. Each approach carries computational costs and modeling assumptions, and each performs differently depending on data quality, record volume, and the discriminative power of the matching fields. A central task for the project is evaluating when these methods deliver trustworthy inference and where they fall short.

The timing of the work reflects a broader shift in how evidence reaches regulators. The FDA has invested heavily in real-world evidence programs, including the RWE framework established under the twenty-first Century Cures Act, which directed the agency to clarify how real-world data can support approvals and label changes, particularly in areas such as rare diseases, oncology, and post-market safety surveillance where randomized trials are impractical or unethical. As the volume of linked real-world analyses grows, so does the need for standards governing every step of the pipeline. Linkage is among the earliest and least visible of those steps, which makes its errors especially insidious: they are baked in before most analysts ever see the data.

For patients and clinicians, the practical payoff of this research is confidence. If a safety signal emerges from linked claims and hospital data, clinicians and patients deserve to know whether that signal could plausibly be an artifact of mismatched records. Conversely, if a real-world analysis finds no elevated risk, regulators should know how robust that null finding is to plausible linkage errors. Quantified linkage uncertainty turns these questions from matters of intuition into matters of measurement, allowing reviewers to weigh evidence with a clearer sense of its fragility or strength. In an era when real-world evidence increasingly shapes drug labels, coverage decisions, and clinical guidelines, that clarity is not a technical luxury but a foundation of trustworthy medicine.

The project also highlights the growing role of statisticians in regulatory science and the position of universities near the federal policy apparatus in shaping it. George Mason, Virginia’s largest public research university, enrolls more than 40,000 students and sits near Washington, D.C., placing its researchers in close proximity to the agencies whose standards define the evidentiary landscape. Over the next two years, Betancourt’s work will contribute to a question that touches every linked dataset behind every regulatory submission: how sure can we be that the records we joined belong together, and how should that residual doubt be reflected in the evidence we act upon. The answer may help determine how much trust real-world evidence ultimately earns.

Subject of Research: Quantifying record linkage uncertainty in real-world evidence for FDA regulatory decision-making

Article Title: Betancourt studying regulatory grade frameworks for assessing record linkage uncertainty in real-world evidence

Article References: Betancourt studying regulatory grade frameworks for assessing record linkage uncertainty in real-world evidence. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: record linkage, real-world evidence, FDA, regulatory science, statistics, data uncertainty, George Mason University, probabilistic matching, pharmacovigilance, electronic health records, Bayesian methods, drug safety

The breakthrough, published in the prestigious international journal Science, was led by Prof. Jianhua Hao, Head of the Department of Physics and Materials, Chair Professor of Materials Physics and Devices and Associate Director of the PolyU-Wuhan Technology and Innovation Research Institute at PolyU. The collaboration included researchers from the National University of Singapore, The Hong Kong University of Science and Technology, Peking University, and the Singapore University of Technology and Design. Dr. Zehan Wu, Research Assistant Professor in the same department, is the first author of the research article. Their work addresses what the International Roadmap for Devices and Systems (IRDS) has identified as one of the most pressing bottlenecks in modern microelectronics, and it does so using a device architecture that the roadmap itself has flagged as the most promising successor to today’s dominant transistor design.

To appreciate why this matters, it helps to understand how the transistors inside every laptop, smartphone, and data centre actually work. Integrated circuits are built from complementary metal–oxide–semiconductor field-effect transistors, or MOSFETs, which switch electrical current by raising or lowering an energy barrier. In the OFF state, charges face a barrier they cannot cross; when a gating voltage is applied, charges gain enough thermal energy to spill over the top of that barrier in a process called thermionic emission. The steepness of this transition, measured by a figure known as the subthreshold swing (SS), determines how much voltage is needed to flip the transistor from OFF to ON. Here nature intervenes: because thermionic emission depends on the thermal distribution of electron energies, the subthreshold swing of any MOSFET cannot fall below 60 millivolts per decade of current at room temperature. That is the Boltzmann limit, and it is not an engineering shortfall but a consequence of statistical mechanics itself.

The consequences of this limit are profound. As chipmakers pack more transistors onto each processor, the 60 mV decade⁻¹ floor forces operating voltages to remain far higher than ideal, which means wasted power, wasted heat, and a hard ceiling on energy efficiency. Advanced MOSFETs typically require gate-voltage ranges of around 800 millivolts to operate, and every millivolt of that overhead is multiplied billions of times across a modern chip. For the artificial intelligence accelerators now driving enormous growth in global electricity consumption, the inability to switch transistors at lower voltages has become one of the defining engineering challenges of the decade. Breaking the 60 mV barrier is therefore not an incremental improvement but a potential inflection point for the entire industry.

The escape route identified by physicists is quantum tunnelling. Instead of forcing electrons to climb over an energy barrier, a tunnelling field-effect transistor (TFET) allows them to pass straight through it, exploiting the quantum-mechanical probability that particles can traverse barriers that would classically be impenetrable. Because tunnelling probability depends on the barrier’s width and height rather than on the thermal energy of the electrons, a TFET can in principle achieve subthreshold swings steeper than 60 mV decade⁻¹ even at room temperature. The IRDS has singled out TFETs as the most promising alternative to MOSFETs precisely for this reason. In practice, however, experimental TFETs have struggled for years with a frustrating trade-off: devices that achieved steep switching typically delivered painfully low output currents, undermining their usefulness in real circuits.

Prof. Hao’s team solved this problem with an elegantly engineered material system. Using pulsed laser deposition (PLD), a technique in which intense laser pulses vaporise a target material so that it recondenses as an ultra-thin film, the researchers fabricated an alternating heterostructure of two-dimensional bismuth (Bi) and indium selenide (InSe) layers. The choice of bismuth is the crucial insight. In its bulk form, bismuth is a semi-metal, a material that is neither a proper conductor nor a proper semiconductor. But when reduced to two-dimensional form with precise control over the layer structure at the nanoscale, bismuth transforms into a semiconductor. This transformation allowed the team to engineer an ideal energy band alignment between the bismuth and the indium selenide, creating exactly the conditions needed for charge carriers to tunnel efficiently from one material into the other through the quantum tunnelling mechanism.

The performance figures reported in the study are striking. The resulting Bi/InSe TFET achieved subthreshold swing values well below the 60 mV decade⁻¹ thermionic limit, and it maintained this steep switching behaviour across six orders of magnitude of current, a range that matters enormously for practical digital logic, where transistors must remain reliably OFF across many conditions rather than only at a single operating point. Remarkably, the device accomplished all of this at room temperature, on standard centimetre-scale silicon substrates, without exotic cryogenic cooling or specialised wafer treatments. The operating gate-voltage range required was only 160 millivolts, a fraction of the roughly 800 millivolts demanded by advanced MOSFETs, translating directly into the kind of power savings that next-generation computing demands.

Just as importantly, the device resolved the long-standing weakness of previous experimental TFETs. The PolyU-led team demonstrated a high output current of up to several microamps per micrometre alongside an exceptionally high ON/OFF current ratio. Output current is not a vanity metric: it determines whether a single transistor can drive multiple downstream logic gates, a capability engineers call fan-out, and whether signals propagate through a circuit with minimal delay. A steep-switching transistor that cannot deliver adequate current would force designers into cumbersome workarounds and erode the very efficiency gains it promises. By combining steep slopes with robust current drive, the Bi/InSe device demonstrates compatibility with existing integrated-circuit design practices and even the potential for a generational upgrade of current chips rather than a disruptive replacement.

The manufacturing dimension of the work may prove as consequential as the physics. Pulsed laser deposition has long been valued in research laboratories for its precision, but questions have lingered about whether it can serve high-precision, wafer-scale production of two-dimensional materials. This study demonstrates the practical viability of PLD for exactly that role, producing uniform ultra-thin heterostructures on silicon substrates that are the backbone of the existing semiconductor industry. Because the device integrates seamlessly with traditional silicon-based manufacturing processes, the researchers argue that their approach provides a scalable roadmap toward energy-efficient microchips, rather than a laboratory curiosity that would require an entirely new industrial ecosystem to deploy. That compatibility with ultra-short channel lengths, which two-dimensional materials are particularly well suited to support, positions the technology for the transistor geometries that future process nodes will demand.

The implications extend well beyond the laboratory bench. Ultra-low-power, high-performance integrated circuits built on tunnelling transistors could reshape the energy economics of artificial intelligence, where specialised hardware already consumes power on the scale of entire cities, and where every reduction in switching voltage compounds across billions of operations per second. Prof. Hao has emphasised that the technology paves the way for the ICs essential to emerging AI chips and advanced semiconductor applications, and the publication of the findings in Science signals that the broader research community regards the result as a landmark. For decades, the Boltzmann limit has stood as an immovable fact of life for chip designers, a boundary drawn by thermodynamics itself. By replacing thermionic emission with quantum tunnelling in a carefully crafted two-dimensional heterostructure, the PolyU-led team has shown that the boundary can be crossed, and in doing so has brought a long-awaited experimental technology a decisive step closer to commercial reality.

Subject of Research: A quantum-tunnelling field-effect transistor based on two-dimensional bismuth and indium selenide heterostructures that overcomes the Boltzmann switching limit in integrated circuits

Article Title: PolyU develops quantum-tunnelling field-effect transistor to overcome barriers to integrated-circuit chip development

Article References: PolyU develops quantum-tunnelling field-effect transistor to overcome barriers to integrated-circuit chip development. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: quantum tunnelling, tunnelling field-effect transistor, TFET, two-dimensional materials, bismuth, indium selenide, Boltzmann limit, subthreshold swing, pulsed laser deposition, integrated circuits, low-power electronics, AI chips

The significance of the achievement becomes clear when the scaling requirements of quantum computing are considered. Neutral-atom quantum computers, which trap arrays of individual atoms in vacuum and use each atom as a qubit, are expected to operate with arrays of roughly 10,000 atoms. That sounds enormous, but fault-tolerant universal quantum computers, machines capable of correcting their own errors and running genuinely useful algorithms, are expected to require more than one million physical qubits. Error correction is the reason for this overhead: fragile quantum states must be protected by encoding logical qubits across many physical ones. No single processor is likely to reach that scale on its own, so researchers have increasingly looked toward networking, distributing entangled photons between multiple quantum processors so that they can work together. That vision demands a photonic interface capable of linking many qubits in parallel, exactly what the Osaka-led team set out to build.

Earlier attempts at multiplexing, the practice of handling many optical channels simultaneously, relied mainly on bundles of parallel optical fibers and were limited to only a handful of channels. The approach suffered from insufficient integration density, and the wide spacing between atoms that fiber-based schemes required made them poorly matched to conventional neutral-atom quantum computers, in which atoms are typically held just a few micrometers apart. The new interface takes a different route. The researchers incorporated an integrated optical waveguide array, a chip-like structure containing many closely spaced light-guiding channels, and demonstrated parallel photon delivery and detection from a neutral-atom array. Photons emitted from ten atoms spaced at micrometer-scale intervals were coupled into ten parallel channels of a 32-channel waveguide array, sent through optical fibers, and detected in parallel, all while preserving the quantum correlations that make the photons useful for networking.

The experiment went beyond simply counting detected photons. The team confirmed that inter-channel crosstalk, the unwanted leakage of light or signal between neighboring channels, was negligible, a critical property for any system that must handle many independent quantum channels at once. They also verified correlations between the quantum states of the atoms and the polarization states of the photons they emitted. Such atom-photon correlations are the raw material of entanglement between a stationary qubit, the atom, and a flying qubit, the photon, and they underpin schemes for multiplexed atom-photon entanglement and, ultimately, for connecting quantum processors across a network. The researchers report that the approach should be scalable to approximately 100 parallel channels, a figure that hints at the kind of density future networked architectures will need.

Detecting single photons reliably is itself a formidable technical challenge, and the Japanese collaboration brought together complementary expertise to solve it. Photon detection in the experiment was performed using a multi-channel superconducting nanostrip photon detector system. The underlying detector technology was developed by Shigehito Miki, Director of the Superconductive ICT Device Laboratory at NICT’s Kobe Frontier Research Center within the Advanced ICT Research Institute, and the complete research system for this experiment was newly built by Hideki Shimoi, a manager at the Electron Tube Division of Hamamatsu Photonics K.K. Superconducting nanostrip detectors are prized in quantum optics because they combine high detection efficiency with very low noise, registering the arrival of individual photons as tiny voltage pulses when the photons break superconducting current paths chilled to cryogenic temperatures.

The institutional division of labor reflects how modern quantum engineering projects increasingly span academia, national research institutes, and industry. The University of Osaka handled overall coordination and implementation of the research. NICT provided the superconducting nanostrip photon detector technology and carried out part of the device fabrication process, using facilities at its Advanced ICT Device R&D Promotion Center. Hamamatsu Photonics, a company with decades of experience in photodetection, developed the detector system specifically for this experiment. Yamamoto framed the collaboration as a full-stack effort: through research and development spanning from neutral-atom arrays to superconducting nanostrip photon detector systems, the team achieved the first demonstration of a multiplexed optical interface, he said, adding that the group will now scale up the degree of multiplexing and work toward connecting neutral-atom quantum computers, accelerating progress toward a fault-tolerant networked quantum computer.

The funding landscape behind the project is equally telling. The research was conducted as part of the Japan Science and Technology Agency’s Moonshot Research and Development Program under Moonshot Goal 6, which aims for the realization of a fault-tolerant universal quantum computer that will revolutionize economy, industry, and security by 2050. The work falls specifically within the projects on fault-tolerant networked quantum computers and on a quantum cyberspace built from networked quantum computers. Additional support came from the JST Program on Open Innovation Platform for Industry-academia Co-creation through a Quantum Software Research Hub, the JST Adopting Sustainable Partnerships for Innovative Research Ecosystem program, and Ministry of Internal Affairs and Communications R&D projects for priority information and communication technologies. The breadth of public investment underscores how strategically important Japan considers the networking pathway to quantum computing.

For the broader field, the demonstration represents an important step toward networked quantum computers with the scalability needed for fault-tolerant universal quantum computing. The architectural analogy that comes to mind is the modern data center, where many computing modules work together as a single system, connected by high-bandwidth links. In the quantum version, multiple quantum processors would be interconnected through parallel photonic links, with entangled photons shuttling quantum information between modules. A multiplexed interface is essential to that picture because a single channel would throttle the connection between processors just as a single wire would cripple a classical cluster. By showing that ten atoms can each have their photons routed through dedicated waveguide channels with negligible crosstalk and preserved quantum correlations, the team has provided a template that others can now push toward the hundred-channel scale and beyond.

Challenges remain before photonic interconnects can knit together million-qubit machines. The waveguide array demonstrated here contains 32 channels, of which ten were used, and extending the technology to the roughly 100 channels the researchers anticipate will require further advances in fabrication, coupling efficiency, and detector multiplexing. The interface must also ultimately preserve entanglement between atoms and photons across the full collection-and-detection chain, not merely the correlations measured in this experiment. Yet the trajectory is clear: integrated photonics, neutral-atom arrays, and superconducting detectors are converging into a coherent hardware stack for quantum networking. As Yamamoto and his colleagues refine their multiplexed interface, the dream of quantum data centers, in which racks of atomic processors exchange entanglement the way classical servers exchange data packets, moves from whiteboard concept toward laboratory reality, one carefully guided photon at a time.

Subject of Research: Multiplexed quantum photonic interfaces for networking neutral-atom quantum computers

Article Title: A multiplexed quantum photonic interface for neutral-atom quantum computers

Article References: A multiplexed quantum photonic interface for neutral-atom quantum computers. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: quantum computing, neutral atoms, photonic interface, waveguide array, multiplexing, quantum networking, entanglement, superconducting nanostrip detectors, fault tolerance, qubits, University of Osaka, Optica

The Heidelberg Laureate Forum, organized annually by the Heidelberg Laureate Forum Foundation, has built its reputation on a simple but powerful format: rather than a conventional conference, it is designed as a networking event where hierarchy is deliberately softened. Young researchers do not merely listen to talks; they eat with laureates, question them in small sessions and present their own work to an audience that includes some of the most decorated minds in their fields. The scientific partners of the forum are the Heidelberg Institute for Theoretical Studies and Heidelberg University, and the event is strongly supported by the award-granting institutions themselves: the Association for Computing Machinery, the International Mathematical Union and the Norwegian Academy of Science and Letters. The foundation behind it, the Klaus Tschira Stiftung, was established to promote the natural sciences, mathematics and computer science in Germany.

The technical heart of this year’s program is a series of lectures spanning the full breadth of both disciplines. Among the most anticipated is a joint lecture by Charles H. Bennett and Gilles Brassard, who together received the 2026 Turing Award for what the award citation describes as their essential role in igniting and shaping the quantum revolution in computer science and in information and communications technology. Bennett and Brassard are foundational figures in quantum cryptography and quantum information theory, fields that have moved from speculative physics to working prototypes of quantum communication networks. Their joint appearance offers attendees a rare chance to hear a first-hand account of how an entire research area was created, and how ideas about the limits of information in a quantum world became an engineering discipline.

Two of this year’s Fields Medal recipients will also take the lectern. Yu Deng will speak on critical problems in statistical physics and partial differential equations, an area where rigorous mathematical analysis meets models of collective behavior in physical systems. Jacob Tsimerman, meanwhile, will address AI safety and mathematics, a topic that sits at the intersection of number-theoretic depth and one of the most urgent practical questions of the decade. Their lectures illustrate how the Fields Medal, traditionally associated with pure mathematics, now connects directly to questions about statistical mechanics, dynamical systems and the trustworthiness of machine learning systems. Further lectures will be given by cryptographer Yael Tauman Kalai, recipient of the 2022 ACM Prize in Computing, by Shayan Oveis Gharan, the 2026 IMU Abacus Medal recipient, and by the noted mathematician Efim Zelmanov, who received the Fields Medal in 1994.

The forum’s connection to the wider world of science prizes is underscored by the annual Lindau Lecture, which signifies the close bond between the Heidelberg Laureate Forum and the Lindau Nobel Laureate Meetings. This year’s lecture will be held by Brian P. Schmidt, who received the Nobel Prize in Physics for the discovery of the accelerating expansion of the Universe through observations of distant supernovae. Schmidt will discuss the state of the Universe, examining what humanity currently understands about the cosmos and what may be learned over the coming decades. For the young mathematicians and computer scientists in the audience, the lecture is a reminder that their disciplines supply the statistical machinery, the algorithms and the computational infrastructure on which modern observational cosmology depends.

One of the most consequential sessions of the week will be an in-depth panel discussion on AI in mathematical research. The panel will be led by Tony Feng of UC Berkeley and Google DeepMind and will include Michael Harris of Columbia University, Ursula Martin, emeritus professor at the University of Oxford, Geordie Williamson of the University of Sydney, and Jacob Tsimerman, the 2026 Fields Medalist from the University of Toronto. The panelists will reflect on the consequences of rapidly evolving AI models with increased reasoning capabilities, the possibilities these systems may unlock for mathematical discovery, and the concerns they raise for the practice and culture of the discipline. The composition of the panel is itself notable: it pairs working mathematicians with a researcher embedded in an industrial AI lab, acknowledging that the frontier of machine-assisted proof and conjecture generation now runs through both universities and technology companies.

The 13th forum will also debut a first in the event’s history: an Un-Conference, a participant-driven format in which discussions focus on relevant, emerging topics shaped by the interests and expertise of the attendees themselves. Organizers describe it as a programmatic bottom-up approach that allows for dynamic and free-flowing work groups, in contrast to the fixed schedules of traditional meetings. For young researchers, this format lowers the barrier to testing half-formed ideas in front of peers and laureates alike, and it reflects a broader trend in scientific meetings toward giving early-career participants genuine agenda-setting power rather than reserving them a passive audience role.

The 200 young researchers selected for this year’s forum were chosen from a global applicant pool and represent what organizers call some of the brightest young minds in mathematics and computer science. Their own science will be on display throughout the week: 20 participants will present their research in the Next Gen Session, and a further 30 will showcase cutting-edge projects as posters. These sessions are not peripheral additions to the program; they are where the laureates in attendance can scout the directions in which the fields are moving, from theoretical computer science and cryptography to applied machine learning and pure mathematics. The forum’s design treats the exchange as bidirectional, with established prizewinners and emerging researchers each gaining something from the contact.

Access to the forum extends far beyond the lecture halls in Heidelberg. Lectures and panel discussions will be livestreamed on the HLF website and made available afterwards on the forum’s YouTube channel. A team of bloggers will report on various program points on the HLFF Blog, and attendees’ experiences can be followed across social media platforms under the hashtag #HLF26. The event will also be accompanied by episodes of the HLFF Vlog, published on the HLF website and YouTube channel, offering behind-the-scenes glimpses of the program, speakers and attendees. Journalists covering the event will have access to a repository of official photographs via the forum’s Flickr page. Journalists wishing to attend in person can apply for accreditation until September 9 through the HLF’s online registration tool, registering as a non-travel grant journalist and submitting the accreditation application.

As the 13th edition opens, the Heidelberg Laureate Forum once more demonstrates why it has become a fixture of the scientific calendar. In a single week, the quantum information pioneers who redefined what computation means, Fields Medalists probing statistical physics and the mathematics of AI safety, a Nobel laureate surveying the fate of the cosmos, and a panel weighing what reasoning machines will do to mathematical research will all share one stage with 200 early-career scientists. The forum’s blend of formal lectures, panel debates, poster sessions and participant-driven formats is built to accelerate exactly the kind of cross-generational, cross-disciplinary exchange that neither journals nor virtual seminars replicate well. For the young researchers attending, the week in Heidelberg may shape collaborations and careers for decades; for the wider scientific community, the livestreamed discussions offer a public window onto where mathematics and computer science are heading next.

Subject of Research: The 13th Heidelberg Laureate Forum, a networking meeting between mathematics and computer science laureates and young researchers

Article Title: The 13th Heidelberg Laureate Forum begins September 13

Article References: The 13th Heidelberg Laureate Forum begins September 13. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Heidelberg Laureate Forum, Abel Prize, Turing Award, Fields Medal, IMU Abacus Medal, ACM Prize in Computing, mathematics, computer science, quantum computing, artificial intelligence, young researchers, Nevanlinna Prize

The research team drew on data from the Centers for Disease Control and Prevention’s National Violent Death Reporting System, a surveillance program that links death certificates, medical examiner findings, and law enforcement reports to build a richer picture of violent deaths than any single source can provide. From 2018 to 2023, the system captured 115,873 homicide deaths. The investigators sorted these into three categories: single-victim homicides, multi-victim homicides involving two or three victims, and mass homicides defined as events with four or more victims killed, excluding the perpetrator. This classification allowed them to compare the three groups across victim demographics, suspect characteristics, weapons used, locations, and the circumstances that precipitated each event.

The scale of the numbers alone is sobering. The overwhelming majority of victims, 90.5 percent or 104,858 people, died in single-victim homicides. Yet nearly one in twelve victims lost their lives in multi-victim events: 10,123 people were killed across 4,789 incidents, representing 8.7 percent of all homicide deaths in the dataset. That proportion is higher than what an earlier study using the same reporting system found for 2003 to 2017. The authors caution that the increase may reflect the expanded geographic coverage of the reporting system as more states joined over time, or it may signal genuine shifts in national homicide patterns, which rose during the corresponding period. Mass homicides, defined as events with four or more fatalities, accounted for less than 1 percent of deaths but claimed 892 lives across 178 incidents, with individual cases ranging from 4 to 51 victims.

Where these killings happened varied in striking ways. Mass and multi-victim homicides frequently occurred in homes, while single-victim homicides most often took place in public settings such as streets or vehicles. This geographic split carries practical implications: residential violence may be less visible to bystanders and less accessible to conventional street-level interventions, while public-location killings may demand different environmental and policing strategies. The finding that multi-victim events cluster in domestic settings aligns with the study’s central observation about intimate partner violence as a precipitating circumstance.

Demographic patterns were largely consistent with previous research, but the contrasts between categories were pronounced. Mass homicides more often involved non-Hispanic White victims and children, whereas single- and multi-victim homicides more often involved non-Hispanic Black victims and suspects who were acquaintances or friends of those killed. Nearly half of the victims in both multi-victim and single-victim homicides were aged 18 to 34 years, compared with 29.8 percent of mass homicide victims in that age range. Across all three categories, both victims and suspects were primarily male, underscoring the persistent gendered character of lethal violence in the United States.

The racial and ethnic distributions of victims and suspects differed notably by category. The highest proportion of mass homicide victims were non-Hispanic White, at 39.7 percent, while the highest proportions of multi-victim and single-victim homicide victims were non-Hispanic Black, at 47.8 percent and 54.8 percent respectively. Among suspects, non-Hispanic White individuals accounted for 32.2 percent and non-Hispanic Black individuals for 30.1 percent of mass homicide suspects, while in multi-victim and single-victim homicides the highest proportions of suspects were non-Hispanic Black, at 37.6 percent and 36.8 percent respectively. These distributions, the study suggests, reflect the distinct social contexts in which different types of homicide unfold rather than a single uniform phenomenon.

Firearms dominated as the method of injury across every category, reinforcing the central role of guns in American lethal violence. Firearms were involved in 75.8 percent of mass homicides, 86.3 percent of multi-victim homicides, and 76.4 percent of single-victim homicides. The consistency of this finding across victim counts suggests that any firearm-related prevention measure could theoretically affect all categories of homicide, although the study’s authors emphasize that tailored approaches remain essential given the differing circumstances surrounding each type of event.

The precipitating circumstances revealed perhaps the most actionable patterns. Intimate partner violence was a common precipitator in nearly one-third of mass homicides, at 31.0 percent, and was also frequently associated with multi-victim homicides at 16.9 percent and single-victim homicides at 15.0 percent. Family relationship problems and a recent or impending crisis within the preceding two weeks were common circumstances in mass and multi-victim killings. For multi-victim and single-victim homicides, arguments and criminal activity were frequent precipitators. Taken together, these findings indicate that relationship conflict and acute personal crises are not marginal factors but central drivers of lethal violence across the spectrum, from a single death to the deadliest mass events.

The study’s authors argue that these differences are key to developing tailored prevention strategies. A policy aimed at reducing argument-driven street violence, for example, may require community-based conflict mediation and violence interruption programs, while addressing intimate partner violence as a precursor to mass casualty events may call for stronger risk assessment, lethal violence screening in domestic violence services, and coordinated responses when families show signs of acute crisis. The identification of a two-week crisis window before many mass and multi-victim killings suggests a potential point of intervention, though the study stops short of prescribing specific policies.

Patrick S. Sullivan, DVM, PhD, of Emory University, Editor-in-Chief of Annals of Epidemiology, underscored the value of the underlying data infrastructure in a statement accompanying the publication. “This analysis is a reminder of what public data systems make possible,” he said. “By linking death certificates, medical examiner findings, and law enforcement reports, the National Violent Death Reporting System lets us see that homicide is not one problem but several — each with its own affected populations and precipitating circumstances. The findings of this study offer important guideposts for the policies and interventions most likely to reduce these tragic and preventable outcomes.” As US homicide rates continue to fluctuate, the study provides a granular evidence base suggesting that treating homicide as a single public health problem obscures the very distinctions that effective prevention depends on.

Subject of Research: Patterns and precipitating circumstances of single-victim, multi-victim, and mass homicides in the United States

Article Title: Study reveals distinct patterns across single-, multi-, and mass homicides

Article References: Study reveals distinct patterns across single-, multi-, and mass homicides. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: homicide, mass homicide, intimate partner violence, public health, National Violent Death Reporting System, firearms, epidemiology, violence prevention, CDC, Annals of Epidemiology, victim demographics, precipitating circumstances

The mathematical foundation of heat flow is the heat equation, a partial differential equation that describes how temperature evolves in space and time. For a single rod with a known heating condition, the solution can be written in terms of a well-known special function called the complementary error function. This function, which takes a real number as its input and returns a value between 0 and 2, encodes how heat from a concentrated source diffuses outward over time. It is one of the workhorses of applied mathematics, appearing not only in heat conduction but also in probability theory, diffusion processes, and statistical analysis. When a computer evaluates the complementary error function for the right inputs, it can effectively reproduce the temperature profile of a heated rod at any moment in time.

The difficulty arises when multiple rods are heated simultaneously. Because the rods interact thermally, the simple one-dimensional solution no longer applies on its own. What the researchers realized is that the coupled system can be captured if the ordinary, single-number inputs to the complementary error function are replaced by an entire matrix of numbers. A matrix is an array of real numbers arranged in rows and columns, and it provides a natural bookkeeping device for systems with many interacting components. In this setting, the entries of the matrix encode the geometry and thermal coupling of the rods, so that a single matrix-valued calculation carries information about all of the rods at once. The resulting object is called the complementary error matrix function, a matrix-valued extension of the classical complementary error function.

Professor Miyajima describes the central challenge in simple terms: a matrix is a way of organizing numbers so that we can better understand how certain systems work, and the hard part is figuring out how to arrange the numbers. That arrangement is not arbitrary. For the matrix-based simulation to be valid, the input matrix must satisfy a specific mathematical assumption. If that assumption fails, the elegant correspondence between the matrix function and the physical heat-flow problem breaks down. Even when the assumption does hold, the simulation is not guaranteed to succeed, because computing the value of a matrix function is itself a formidable numerical task. Before this work, no method for computing the complementary error matrix function had ever been reported in the scientific literature, which meant the team had to build the entire computational framework from the ground up.

The first hurdle was theoretical. The researchers had to clarify the mathematical properties of the complementary error matrix function: what it means, how it behaves, and under what conditions it is well defined. Establishing these properties was essential, because numerical computation without a solid theoretical footing can produce answers that look plausible but are quietly wrong. By pinning down the function’s properties, the team ensured that any value computed on a computer genuinely corresponds to the physical heat-propagation scenario the matrix is meant to represent. This careful groundwork is what separates a reliable simulation tool from a mathematical curiosity, and it is the kind of unglamorous but critical work that underpins much of computational science.

The second hurdle was speed. Evaluating a matrix version of the complementary error function directly would require an enormous amount of computational time, because the standard definitions of matrix functions involve operations whose cost grows rapidly with the size of the matrix. For a simulation of multiple interacting rods, where the matrix can be large and the function must be evaluated many times to trace the heat over time, a naive approach would be impractical. Professor Sadeghi explains that to avoid this bottleneck, the team derived a new representation of the function, describing it as being like shorthand. This reformulation preserves the mathematical meaning of the function while restructuring the calculation so that it can be carried out far more efficiently, allowing the simulation to be completed much, much faster than a direct evaluation would allow.

The payoff of this shorthand representation is substantial. With an efficient way to compute the complementary error matrix function, the researchers can rapidly simulate how heat propagates through each rod when multiple rods are heated at the same time, including the mutual influence that makes the problem so hard. What might previously have demanded prohibitive amounts of computational time can now be obtained in a fraction of the effort. Speed matters in this field for reasons that go beyond convenience. Fast simulations allow engineers to explore many design variations, to run what-if analyses in real time, and to build the kind of predictive models that can be consulted before a physical system is ever built or a hazardous situation arises.

The potential practical implications reach into everyday life. Being able to predict accurately how heat spreads through coupled conductive components means being able to anticipate where hot spots will form, how long surfaces will remain dangerous to touch, and how thermal energy migrates through assemblies of materials. As the researchers note, this capability may allow us to better predict how heat might spread in real life, and how to avoid getting burned. That framing is not merely rhetorical: thermal safety is a genuine design constraint in everything from consumer electronics to industrial machinery, and tools that make coupled heat-flow predictions faster and more accessible give designers a clearer window into risks that were previously expensive to quantify.

The research also represents a notable milestone in pure and applied mathematics. The complementary error matrix function had, until now, been an unexplored object: its properties had not been clarified and no computational method for it existed. By defining the function rigorously, establishing its mathematical behavior, and deriving an efficient numerical scheme for evaluating it, Sadeghi and Miyajima have added a new tool to the mathematical toolbox, one that connects the classical theory of special functions to the modern needs of coupled-system simulation. Their work illustrates a recurring pattern in applied mathematics, in which extending a familiar scalar function to matrices unlocks entirely new classes of physical problems that can be solved with the same conceptual machinery.

The findings were published in the journal Linear Algebra and Its Applications on August 18, 2026, in a paper titled Complementary error matrix function and its numerical computation. The choice of venue is fitting, since the entire contribution rests on the disciplined use of matrices, the arrays of rows and columns that sit at the heart of linear algebra. For a problem as seemingly mundane as heat traveling through rods, the solution draws on deep mathematical structure, and the result is a simulation technique that is both theoretically sound and computationally swift. As researchers and engineers begin to work with this new function, the ability to model interacting heated components quickly and accurately could find uses well beyond the rods that inspired it, wherever coupled diffusion processes demand fast, reliable answers.

Subject of Research: Numerical computation of the complementary error matrix function for simulating heat propagation through multiple coupled rods

Article Title: Rapid simulation of heat propagation through multiple rods

Article References: Rapid simulation of heat propagation through multiple rods. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: heat propagation, matrix functions, complementary error function, numerical computation, linear algebra, heat equation, simulation, Tohoku University, Islamic Azad University, coupled systems, thermal analysis, applied mathematics