Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning

Learning Nash equilibrium (NE) in complex zero-sum games with multi-agent reinforcement learning (MARL) can be extremely computationally expensive. Curriculum learning is an effective way to accelerate learning, but an under-explored dimension for generating a curriculum is the difficulty-to-learn of the subgames –games induced by starting from a specific state. In this work, we present a novel subgame curriculum learning framework for zero-sum games. It adopts an adaptive initial state distribution by resetting agents to some previously visited states where they can quickly learn to improve performance. Building upon this framework, we derive a subgame selection metric that approximates the squared distance to NE values and further adopt a particle-based state sampler for subgame generation. Integrating these techniques leads to our new algorithm, Subgame Automatic Curriculum Learning (SACL), which is a realization of the subgame curriculum learning framework. SACL can be combined with any MARL algorithm such as MAPPO. Experiments in the particle-world environment and Google Research Football environment show SACL produces much stronger policies than baselines. In the challenging hide-and-seek quadrant environment, SACL produces all four emergent stages and uses only half the samples of MAPPO with self-play. The project website is at https://sites.google. com/view/sacl-neurips.
Experimental neutrino physics in a nuclear landscape

There are profound connections between neutrino physics and nuclear experiments. Exceptionally precise measurements of single and double beta-decay spectra illuminate the scale and nature of neutrino mass and may finally answer the question of whether neutrinos are their own antimatter counterparts. Neutrino-nucleus scattering underpins oscillation experiments and probes nuclear structure, neutrinos offer a rare vantage point into collapsing stars and nuclear fission reactors, and techniques pioneered in neutrino nuclear-physics experiments are advancing quantum-sensing technologies. In this article, we review current and planned efforts at the intersection of neutrino and nuclear experiments.
Big Data Privacy in Emerging Market Fintech and Financial Services: A Research Agenda

The data revolution in low- and middle-income countries is quickly transforming how companies approach emerging markets. As mobile phones and mobile money proliferate, they generate new streams of data that enable innovation in consumer finance, credit, and insurance. Already, this new generation of products are being used by hundreds of millions of consumers, often to use financial services for the first time. However, the collection, analysis, and use of these data, particularly from economically disadvantaged populations, raises serious privacy concerns. This white paper describes a research agenda to advance our understanding of the problem and solution space of data privacy in emerging market fintech and financial services. We highlight five priority areas for research: conducting comprehensive landscape analyses; understanding local definitions of ``data privacy''; documenting key sources of risk, and potential technical solutions (such as differential privacy and homomorphic encryption); improving non-technical approaches to data privacy (such as policies and practices); and understanding the tradeoffs involved in deploying privacy-enhancing solutions. Taken together, we hope this research agenda will focus attention on the multi-faceted nature of privacy in emerging markets, and catalyze efforts to develop responsible and consumer-oriented approaches to data-intensive applications.
Top of the Heap: Efficient Memory Error Protection for Many Heap Objects

Exploits against heap memory errors continue to be a major concern. Although many defenses have been proposed, heap data are not protected from attacks that exploit memory errors systematically. Research defenses focus on complete coverage of heap objects, often giving up on comprehensive memory safety protection and/or incurring high costs in performance overhead and memory usage. In this paper, we propose a solution for heap memory safety enforcement that aims to provide comprehensive protection from memory errors efficiently by protecting those heap objects whose accesses are provably safe from memory errors. Specifically, we present the Uriah system that statically validates spatial and type memory safety for heap objects, isolating compliant objects on a safe heap that enforces temporal type safety to prevent attacks on memory reuse. Using Uriah, 71.9% of heap allocation sites can be shown to produce objects (73% of allocations are found safe) that satisfy spatial and type safety, which are then isolated using Uriah's heap allocator from memory accesses via unsafe heap objects. Uriah only incurs 2.9% overhead and only uses 9.3% more memory on SPEC CPU2006 (C/C++) benchmarks, showing that many heap objects can be protected from all classes of memory errors efficiently.
Pleistocene Formations in Indiana

The system of stratigraphic classification used in North America for all sedimentary rock units except nonmarine deposits of the Pleistocene Series established groups, formations, and smaller units based on objectively determined characteristics of the rocks and not on geomorphology or subjective features, such as geologic time. Not all students of the nonmarine Pleistocene have kept these two kinds of units distinct from each other. The Pleistocene sediments of Indiana and the surrounding States are continental in origin rather than marine, and thus they show far less lithologic homogeneity than sediments and rocks of marine origin. Nevertheless, these Pleistocene sediments can and should be grouped into logical and usable formational units by means of lithologic characteristics. A classification based on lithologic characteristics omits units that are wholly geomorphic, such as terraces, but it does not change many time-lithic units that are now shown on most surficial geologic maps. Instead, these units are given names as formations or members or are regarded as facies. Three of the six formations proposed in this study of Pleistocene sediments in Indiana, the Jessup, Trafalgar, and Lagro Formations, are composed dominantly of glacially deposited mudstone or till. These formations have upper and lower contacts that coincide with unconformities within the Pleistocene. Other unconformities permit the separation of the Jessup and Trafalgar Formations into members, named here the Cloverdale, Butlerville, Center Grove, and Cartersburg Till Members. These unconformities generally are marked by key beds, such as paleosols and thin fossiliferous beds. The remaining three units, the Prospect, Atherton, and Martinsville Formations, consist of several related lithofacies, through most of which significant unconformities are difficult or impossible to trace. This method of classification permits local names to be designated for lithostratigraphic units in the Pleistocene Series that are useful in field studies in Indiana. These units are particularly valuable for use in problems that frequently arise in correlating rocks within a finely divided time sequence. By means of a classification system for Pleistocene sediments, existing terminology of glaciations can be referred to readily and can be correlated as a kind of geologic-time stratigraphy. Fossil mollusks are found in two of the facies of the Atherton Formation and in thin beds of silt between barren mudstones in the Jessup and Trafalgar Formations. Largely because of environmental changes, the assemblages differ from unit to unit in vertical succession, but differences are slight. Three range zones based on forms of Succinea are named, in descending order, the S. vermeta,S. gelida, and S. gelida var. Range Zones. In addition, the fossiliferous parts of the key beds that separate formations and members are termed the lower, middle, and upper Hendersonia occulta beds and the Vertigo alpestris oughtoni bed.
Fictitious Play via Finite Differences for Mean Field Games with Optimal Stopping

This paper considers mean field games with optimal stopping time (OSMFGs) where agents make optimal exit decisions, the coupled obstacle and Fokker-Planck equations in such models pose challenges versus classic MFGs. This paper proposes a generalized fictitious play algorithm that computes OSMFG mixed equilibria by iteratively solving pure strategy systems, i.e. approximating mixed strategies through averaging pure strategies according to a certain updating rule. The generalized fictitious play allows for a broad family of learning rates and the convergence to the mixed strategy equilibrium can be rigorously justified. The algorithm also incorporates efficient finite difference schemes of the pure strategy system, and numerical experiments demonstrate the effectiveness of the proposed method in robustly and efficiently computing mixed equilibria for OSMFGs.
Lifelong Learning for Fog Load Balancing: A Transfer Learning Approach

Fog computing emerged as a promising paradigm to address the challenges of processing and managing data generated by the Internet of Things (IoT). Load balancing (LB) plays a crucial role in Fog computing environments to optimize the overall system performance. It requires efficient resource allocation to improve resource utilization, minimize latency, and enhance the quality of service for end-users. In this work, we improve the performance of privacy-aware Reinforcement Learning (RL) agents that optimize the execution delay of IoT applications by minimizing the waiting delay. To maintain privacy, these agents optimize the waiting delay by minimizing the change in the number of queued requests in the whole system, i.e., without explicitly observing the actual number of requests that are queued in each Fog node nor observing the compute resource capabilities of those nodes. Besides improving the performance of these agents, we propose in this paper a lifelong learning framework for these agents, where lightweight inference models are used during deployment to minimize action delay and only retrained in case of significant environmental changes. To improve the performance, minimize the training cost, and adapt the agents to those changes, we explore the application of Transfer Learning (TL). TL transfers the knowledge acquired from a source domain and applies it to a target domain, enabling the reuse of learned policies and experiences. TL can be also used to pre-train the agent in simulation before fine-tuning it in the real environment; this significantly reduces failure probability compared to learning from scratch in the real environment. To our knowledge, there are no existing efforts in the literature that use TL to address lifelong learning for RL-based Fog LB; this is one of the main obstacles in deploying RL LB solutions in Fog systems.
An Optimal Unequal Error Protection LDPC Coded Recording System

For efficient modulation and error control coding, the deliberate flipping approach imposes the run-length-limited(RLL) constraint by bit error before recording. From the read side, a high coding rate limits the correcting capability of RLL bit error. In this paper, we study the low-density parity-check (LDPC) coding for RLL constrained recording system based on the Unequal Error Protection (UEP) coding scheme design. The UEP capability of irregular LDPC codes is used for recovering flipped bits. We provide an allocation technique to limit the occurrence of flipped bits on the bit with robust correction capability. In addition, we consider the signal labeling design to decrease the number of nearest neighbors to enhance the robust bit. We also apply the density evolution technique to the proposed system for evaluating the code performances. In addition, we utilize the EXIT characteristic to reveal the decoding behavior of the recommended code distribution. Finally, the optimization approach for the best distribution is proven by differential evolution for the proposed system.
Local to Global: A Distributed Quantum Approximate Optimization Algorithm for Pseudo-Boolean Optimization Problems

With the rapid advancement of quantum computing, Quantum Approximate Optimization Algorithm (QAOA) is considered as a promising candidate to demonstrate quantum supremacy, which exponentially solves a class of Quadratic Unconstrained Binary Optimization (QUBO) problems. However, limited qubit availability and restricted coherence time challenge QAOA to solve large-scale pseudo-Boolean problems on currently available Near-term Intermediate Scale Quantum (NISQ) devices. In this paper, we propose a distributed QAOA which can solve a general pseudo-Boolean problem by converting it to a simplified Ising model. Different from existing distributed QAOAs' assuming that local solutions are part of a global one, which is not often the case, we introduce community detection using Louvian algorithm to partition the graph where subgraphs are further compressed by community representation and merged into a higher level subgraph. Recursively and backwards, local solutions of lower level subgraphs are updated by heuristics from solutions of higher level subgraphs. Compared with existing methods, our algorithm incorporates global heuristics into local solutions such that our algorithm is proven to achieve a higher approximation ratio and outperforms across different graph configurations. Also, ablation studies validate the effectiveness of each component in our method.
On the Optical Transients from Double White-dwarf Mergers

Double white dwarf (DWD) mergers are relevant astrophysical sources expected to produce massive, highly magnetized white dwarfs (WDs), supernovae (SNe) Ia, and neutron stars (NSs). Although they are expected to be numerous sources in the sky, their detection has evaded the most advanced transient surveys. This article characterizes the optical transient expected from DWD mergers in which the central remnant is a stable (sub-Chandrasekhar) WD. We show that the expansion and cooling of the merger’s dynamical ejecta lead to an optical emission peaking at 1–10 days postmerger, with luminosities of 1040–1041 erg s−1. We present simulations of the light curves, spectra, and the color evolution of the transient. We show that these properties, together with the estimated rate of mergers, are consistent with the absence of detection, e.g., by the Zwicky Transient Facility. More importantly, we show that the Legacy Survey of Space and Time of the Vera C. Rubin Observatory will likely detect a few/several hundred per year, opening a new window to the physics of WDs, NSs, and SNe Ia.
Extraction of Medication and Temporal Relation from Clinical Text using Neural Language Models

Clinical texts, represented in electronic medical records (EMRs), contain rich medical information and are essential for disease prediction, personalised information recommendation, clinical decision support, and medication pattern mining and measurement. Relation extractions between medication mentions and temporal information can further help clinicians better understand the patients’ treatment history. To evaluate the performances of deep learning (DL) and large language models (LLMs) in medication extraction and temporal relations classification, we carry out an empirical investigation of MEDTEM project using several advanced learning structures including BiLSTM-CRF and CNN-BiLSTM for a clinical domain named entity recognition (NER), and BERT-CNN for temporal relation extraction (RE), in addition to the exploration of different word embedding techniques. Furthermore, we also designed a set of post-processing roles to generate structured output on medications and the temporal relation. Our experiments show that CNN-BiLSTM slightly wins the BiLSTM-CRF model on the i2b2-2009 clinical NER task yielding 75.67, 77.83, and 78.17 for precision, recall, and F1 scores using Macro Average. BERT-CNN model also produced reasonable evaluation scores 64.48, 67.17, and 65.03 for P/R/F1 using Macro Avg on the temporal relation extraction test set from i2b2-2012 challenges. Code and Tools from MEDTEM will be hosted at https://github.com/HECTA-UoM/MedTem
Medical Image Analysis

model trained on masks generated by NuClick achieved the ﬁrst rank in LYON19 challenge. As exemplar outputs of our framework, we are releasing two datasets: 1) a dataset of lymphocyte annotations within IHC images, and 2) a dataset of segmented WBCs in blood smear images.
Query-dominant User Interest Network for Large-Scale Search Ranking

Historical behaviors have shown great effect and potential in various prediction tasks, including recommendation and information retrieval. The overall historical behaviors are various but noisy while search behaviors are always sparse. Most existing approaches in personalized search ranking adopt the sparse search behaviors to learn representation with bottleneck, which do not sufficiently exploit the crucial long-term interest. In fact, there is no doubt that user long-term interest is various but noisy for instant search, and how to exploit it well still remains an open problem. To tackle this problem, in this work, we propose a novel model named Query-dominant user Interest Network (QIN), including two cascade units to filter the raw user behaviors and reweigh the behavior subsequences. Specifically, we propose a relevance search unit (RSU), which aims to search a subsequence relevant to the query first and then search the sub-subsequences relevant to the target item. These items are then fed into an attention unit called Fused Attention Unit (FAU). It should be able to calculate attention scores from the ID field and attribute field separately, and then adaptively fuse the item embedding and content embedding based on the user engagement of past period. Extensive experiments and ablation studies on real-world datasets demonstrate the superiority of our model over state-of-the-art methods. The QIN now has been successfully deployed on Kuaishou search, an online video search platform, and obtained 7.6% improvement on CTR.
RECAP-KG: Mining Knowledge Graphs from Raw Primary Care Physician Notes for Remote COVID-19 Assessment in Primary Care.

Building Clinical Decision Support Systems, whether from regression models or machine learning requires clinical data either in standard terminology or as text for Natural Language Processing (NLP). Unfortunately, many clinical notes are written quickly during the consultation and contain many abbreviations, typographical errors, and a lack of grammar and punctuation Processing these highly unstructured clinical notes is an open challenge for NLP that we address in this paper. We present RECAP-KG - a knowledge graph construction frame workfrom primary care clinical notes. Our framework extracts structured knowledge graphs from the clinical record by utilising the SNOMED-CT ontology both the entire finding hierarchy and a COVID-relevant curated subset. We apply our framework to consultation notes in the UK COVID-19 Clinical Assessment Service (CCAS) dataset and provide a quantitative evaluation of our framework demonstrating that our approach has better accuracy than traditional NLP methods when answering questions about patients.
Lead pollution in the ancient harbours of Marseilles

La concentration et la composition isotopique du plomb ont ete mesurees dans deux coupes stratigraphiques provenant du port antique de Marseille. Les sediments dates de la periode romaine montrent une augmentation de la concentration en Pb et un changement dans les rapports isotopiques. Ces variations traduisent indiscutablement une pollution au Pb dans le port romain de Marseille. La composition isotopique du plomb permet de retracer l´origine du metal utilise a cette periode, probablement un melange provenant de mines francaises et espagnoles.
Phylogenetic characteristics and recombination analysis of echovirus 5 associated with severe acute respiratory infection in China

ABSTRACT Echovirus 5 (E5) belongs to the enterovirus B (EV-B) species, which is one of the pathogens causing respiratory diseases. Only two whole genome sequences (WGSs) of E5 are available in GenBank, and few studies on E5 have been reported worldwide. In this study, human rhabdomyosarcoma (RD) cells were used to isolate E5 from respiratory samples associated with severe acute respiratory infection (SARI). The nucleotide sequence of the VP1 region and the WGSs of two E5 strains were sequenced using Sanger sequencing and high-throughput sequencing technology, respectively. Together with 31 E5 VP1 sequences and 2 WGSs downloaded from GenBank, a total of 33 complete VP1 sequences and 4 WGSs were used for genetic characterization and evolutionary analysis. Phylogenetic analysis revealed five genotypes (A–E) based on complete VP1 sequences. Evolutionary analysis showed that the time to the most recent common ancestor of E5 was approximately 1952 (95% highest posterior density [HPD] range, 1948–1953]), with a mean substitution rate of 7.74 × 10−3 subs/site/year (95% HPD range, 6.44–9.11 × 10−3). In addition, the WGS analyses showed that the nucleotide and amino acid similarities of the coding regions of the two strains obtained in this study with the prototype of the E5 strain Noyce were 80.2% and 97.2%, respectively. Recombination analysis showed that the two E5 strains may have intraspecific recombination with E6, EV-B85, and CVA9 serotypes in the nonstructural region. This study reports the isolation of E5 from SARI cases for the first time, and genetic characterization and evolutionary analysis provide valuable information on global E5 molecular epidemiology. IMPORTANCE This study is the first report of echovirus 5 (E5) associated with severe acute respiratory infection and obtained the first E5 whole-genome sequence in China. Combined with the sequences available in the GenBank database, the first genotyping, phylogenetic characteristics, recombination, and genetic evolutionary analysis of E5 was performed in this study. Our findings providing valuable information on global E5 molecular epidemiology. This study is the first report of echovirus 5 (E5) associated with severe acute respiratory infection and obtained the first E5 whole-genome sequence in China. Combined with the sequences available in the GenBank database, the first genotyping, phylogenetic characteristics, recombination, and genetic evolutionary analysis of E5 was performed in this study. Our findings providing valuable information on global E5 molecular epidemiology.
Ecological consequences of climate change altered forest insect disturbanceregimes

ECOLOGICAL CONSEQUENCES OF CLIMATE CHANGE ALTERED FOREST INSECT DISTURBANCE REGIMES JESSE A. LOGAN AND JAMES A. POWELL USDA Forest Service, Rocky Mountain Research Station, 860 N 1200 East, Logan UT. 84321. Department of Mathematics and Statistics, Utah State University, Logan, UT 84322 Abstract: Unprecedented outbreaks of native bark beetles are occurring in forests throughout the mountains of western North America. Any one of these events would be unusual; their simultaneous occurrence is nothing short of remarkable. Significant biogeographical events are occurring at a continental scale, and a warming climate is the one commonality across all of these spectacular outbreak events. Mountain pine beetle (Dendroctonus ponderosae Hopkins) populations are responsible for three of the more impressive of these events, and we describe recent case histories that illustrate the unique attributes of the current situation. These case histories involve outbreaks within the current range of mountain pine beetle in areas with micro-climate that were previously too cold; unusually large and intense outbreaks in high elevation pines; and range expansion north and east beyond the historical distribution in Canada. In this article we describe each of these three situations and briefly discuss their importance with respect to global climate warming. INTRODUCTION Native insects are the greatest forces of natural change in forested ecosystems of North America. In aggregate, insect and pathogen disturbances affect an area that is