Miracle-dz/UniFAR-FTUs huggingface dataset:
Know your Enemy: Investigating Monte-Carlo Tree Search with Opponent Models in Pommerman

In combination with Reinforcement Learning, Monte-Carlo Tree Search has shown to outperform human grandmasters in games such as Chess, Shogi and Go with little to no prior domain knowledge. However, most classical use cases only feature up to two players. Scaling the search to an arbitrary number of players presents a computational challenge, especially if decisions have to be planned over a longer time horizon. In this work, we investigate techniques that transform general-sum multiplayer games into single-player and two-player games that consider other agents to act according to given opponent models. For our evaluation, we focus on the challenging Pommerman environment which involves partial observability, a long time horizon and sparse rewards. In combination with our search methods, we investigate the phenomena of opponent modeling using heuristics and self-play. Overall, we demonstrate the effectiveness of our multiplayer search variants both in a supervised learning and reinforcement learning setting.
Constraints on the rotating self-dual black hole with quasi-periodic oscillations

An impressive feature of loop quantum gravity (LQG) is that it can elegantly resolve both the big bang and black hole singularities. By using the Newman-Janis algorithm, a regular and effective rotating self-dual black hole (SDBH) metric could be constructed, which alters the Kerr geometry with a polymeric function P from the quantum effects of LQG geometry. In this paper, we investigate its impact on the frequency characteristics of the X-ray quasi-periodic oscillations (QPOs) from 5 X-ray binaries and contrast it with the existing results of the orbital, periastron precession and nodal precession frequencies within the relativistic precession model. We apply a Monte Carlo Markov Chain (MCMC) simulation to examine the possible LQG effects on the X-ray QPOs. We found that the best constraint result for the rotating self-dual geometry from LQG came from the QPOs of X-ray binary GRO J1655-40, which establish an upper bound on the polymeric function P less than 6.15 × 10-3 at 95% confidence level. This bound leads to a restriction on the polymeric parameter δ of LQG to be 0.66.
ConQueR: Contextualized Query Reduction using Search Logs

Query reformulation is a key mechanism to alleviate the linguistic chasm of query in ad-hoc retrieval. Among various solutions, query reduction effectively removes extraneous terms and specifies concise user intent from long queries. However, it is challenging to capture hidden and diverse user intent. This paper proposes Contextualized Query Reduction (ConQueR) using a pre-trained language model (PLM). Specifically, it reduces verbose queries with two different views: core term extraction and sub-query selection. One extracts core terms from an original query at the term level, and the other determines whether a sub-query is a suitable reduction for the original query at the sequence level. Since they operate at different levels of granularity and complement each other, they are finally aggregated in an ensemble manner. We evaluate the reduction quality of ConQueR on real-world search logs collected from a commercial web search engine. It achieves up to 8.45% gains in exact match scores over the best competing model.
Multi-Task Models Adversarial Attacks

Multi-Task Learning (MTL) involves developing a singular model, known as a multi-task model, to concurrently perform multiple tasks. While the security of single-task models has been thoroughly studied, multi-task models pose several critical security questions, such as 1) their vulnerability to single-task adversarial attacks, 2) the possibility of designing attacks that target multiple tasks, and 3) the impact of task sharing and adversarial training on their resilience to such attacks. This paper addresses these queries through detailed analysis and rigorous experimentation. First, we explore the adaptation of single-task white-box attacks to multi-task models and identify their limitations. We then introduce a novel attack framework, the Gradient Balancing Multi-Task Attack (GB-MTA), which treats attacking a multi-task model as an optimization problem. This problem, based on averaged relative loss change across tasks, is approximated as an integer linear programming problem. Extensive evaluations on MTL benchmarks, NYUv2 and Tiny-Taxonomy, demonstrate GB-MTA's effectiveness against both standard and adversarially trained multi-task models. The results also highlight a trade-off between task accuracy improvement via parameter sharing and increased model vulnerability due to enhanced attack transferability.
Federated Learning of Medical Concepts Embedding using BEHRT

Electronic Health Records (EHR) data contains medical records such as diagnoses, medications, procedures, and treatments of patients. This data is often considered sensitive medical information. Therefore, the EHR data from the medical centers often cannot be shared, making it difficult to create prediction models using multi-center EHR data, which is essential for such models' robustness and generalizability. Federated Learning (FL) is an algorithmic approach that allows learning a shared model using data in multiple locations without the need to store all data in a central place. An example of a prediction model's task is to predict future diseases. More specifically, the model needs to predict patient's next visit diagnoses, based on current and previous clinical data. Such a prediction model can support care providers in making clinical decisions and even provide preventive treatment. We propose a federated learning approach for learning medical concepts embedding. This pre-trained model can be used for fine-tuning for specific downstream tasks. Our approach is based on an embedding model like BEHRT, a deep neural sequence transduction model for EHR. We train using federated learning, both the Masked Language Modeling (MLM) and the next visit downstream model. We demonstrate our approach on the MIMIC-IV dataset. We compare the performance of a model trained with FL against a model trained on centralized data. We find that our federated learning approach reaches very close to the performance of a centralized model, and it outperforms local models in terms of average precision. We also show that pre-trained MLM improves the model's average precision performance in the next visit prediction task, compared to an MLM model without pre-training. Our code is available at https://github.com/nadavlab/FederatedBEHRT.
Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization

This paper introduces a new method for minimizing matrix-smooth non-convex objectives through the use of novel Compressed Gradient Descent (CGD) algorithms enhanced with a matrix-valued stepsize. The proposed algorithms are theoretically analyzed first in the single-node and subsequently in the distributed settings. Our theoretical results reveal that the matrix stepsize in CGD can capture the objective's structure and lead to faster convergence compared to a scalar stepsize. As a byproduct of our general results, we emphasize the importance of selecting the compression mechanism and the matrix stepsize in a layer-wise manner, taking advantage of model structure. Moreover, we provide theoretical guarantees for free compression, by designing specific layer-wise compressors for the non-convex matrix smooth objectives. Our findings are supported with empirical evidence.
Evaluation of asphericity in different range of cornea in emmetropes

Objective To investigate the distribution of Q-value in cornea asphericity of emmetropes.Methods The examination of naked distant vision,computer optometry,slit lamp,direct ophthalmoscope and corneal topography in 192 cases(384 eyes) with emmetropic eyes,the results were analyzed statistically.Results The mean radius of corneal front curvature(R) was (7.80±0.23)mm,the mean value of Q10 was -0.083±0.098,Q15 was -0.119±0.097,Q20 was -0.149±0.104,Q25 was -0.200±0.107 and Q30 was- 0.262±0.104. There was no significant relationship between Q-value and gender (P0.05),there was statistically significant differences in value of Q25 and Q30 among different ages(P0.05). The value of Q25 and Q30 in cases with age more than 45 years were higher than that in cases with other ages,the difference was significant(P0.05). There was a negative relationship between Q30 and R(r=-0.115,P0.05),but there was no relationship between other Q-values (Q10,Q15,Q20,Q25)and R(P0.05). There was no significant relationship between Q-value and age.Conclusion The shape of corneal front surface is close to globular,but more adjacency to ambitus,more close to flat.
A three-dimensional MR-STAT protocol for high-resolution multi-parametric quantitative MRI

Magnetic Resonance Spin Tomography in Time-Domain (MR-STAT) is a multiparametric quantitative MR framework, which allows for simultaneously acquiring quantitative tissue parameters such as T1, T2 and proton density from one single short scan. A typical 2D MR-STAT acquisition uses a gradient-spoiled, gradient-echo sequence with a slowly varying RF flip-angle train and Cartesian readouts, and the quantitative tissue maps are reconstructed by an iterative, model-based optimization algorithm. In this work, we design a 3D MR-STAT framework based on previous 2D work, in order to achieve better image SNR, higher though-plan resolution and better tissue characterization. Specifically, we design a 7-minute, high-resolution 3D MR-STAT sequence, and the corresponding two-step reconstruction algorithm for the large-scale dataset. To reduce the long acquisition time, Cartesian undersampling strategies such as SENSE are adopted in our transient-state quantitative framework. To reduce the computational burden, a data splitting scheme is designed for decoupling the 3D reconstruction problem into independent 2D reconstructions. The proposed 3D framework is validated by numerical simulations, phantom experiments and in-vivo experiments. High-quality knee quantitative maps with 0.8 x 0.8 x 1.5mm3 resolution and bilateral lower leg maps with 1.6mm isotropic resolution can be acquired using the proposed 7-minute acquisition sequence and the 3-minute-per-slice decoupled reconstruction algorithm. The proposed 3D MR-STAT framework could have wide clinical applications in the future.
Hot Pixels: Frequency, Power, and Temperature Attacks on GPUs and ARM SoCs

The drive to create thinner, lighter, and more energy efficient devices has resulted in modern SoCs being forced to balance a delicate tradeoff between power consumption, heat dissipation, and execution speed (i.e., frequency). While beneficial, these DVFS mechanisms have also resulted in software-visible hybrid side-channels, which use software to probe analog properties of computing devices. Such hybrid attacks are an emerging threat that can bypass countermeasures for traditional microarchitectural side-channel attacks. Given the rise in popularity of both Arm SoCs and GPUs, in this paper we investigate the susceptibility of these devices to information leakage via power, temperature and frequency, as measured via internal sensors. We demonstrate that the sensor data observed correlates with both instructions executed and data processed, allowing us to mount software-visible hybrid side-channel attacks on these devices. To demonstrate the real-world impact of this issue, we present JavaScript-based pixel stealing and history sniffing attacks on Chrome and Safari, with all side channel countermeasures enabled. Finally, we also show website fingerprinting attacks, without any elevated privileges.
A Heuristic for Completing Covariance And Correlation Matrices

In practical applications, and especially in high dimensional models, it is common that not all covariances are known due to, for example, data limitations. In such situations, the covariance matrix must be completed in order to use the statisti-cal or stochastic model at hand. The already speciﬁed covariances generically imply a dependence between variables between which the covariance is unspeciﬁed. A criterion that can then be used to complete the covariance matrix is to introduce as little extra dependence between variables as possible which is equivalent with entropy maximization. Such completion problems can be solved with global optimization al-gorithms which unfortunately are slow for large matrices. And thus, this makes global optimization less suitable for applications in which the computation time is important. Another difﬁculty is that the initially speciﬁed covariances can be inconsistent in the sense that no valid completion exists. In this paper, we present a heuristic for complet-ing partially speciﬁed covariance (and correlation) matrices that is: fast (also for large matrices), tries to introduce as little extra dependence between variables as possible and also corrects for inconsistencies in the initially speciﬁed covariances. Finally, we present error measures for the performance of the heuristic.
Aplicação de Algoritmos de Árvore de Decisão Sobre Uma Base de Dados de Câncer de Mama

Este artigo descreve um experimento de minera ca o de dados utilizando a rvores de decis a o para a extra ca o de informa co es de um dataset com amostras de tumores de mama e suas caracter i sticas, tendo como finalidade a classifica ca o do tumor em benigno ou maligno. Detalhou­se a base de dados e as estrat e gias utilizadas para realiza ca o dos experimentos e aplica ca o dos algoritmos de decis a o. Os algoritmos de a rvore de decis a o escolhidos para a classifica ca o das amostras em quest a o foram o J48 e o REPtree. Ap o s analise das a rvores geradas, conclui­se que o atributo quantidade de citoplasma no n u cleo sempre foi analisado para a determina ca o tanto de tumores malignos quanto de tumores benignos. Al e m disso, houve confirma co es de padr o es para tumores de mama.
Practical Guidelines for Overcoming Obesity in Children

This book explores the problem of childhood obesity, a growing concern worldwide. Chapter I provides an overview of the issue, defining obesity and outlining the various factors contributing to its prevalence in children, including diet, lifestyle, genetics, wealth, technology, and cultural norms. The psychological aspects of childhood obesity are also discussed. Chapter II examines the health problems associated with childhood obesity, such as heart disease, type 2 diabetes mellitus, high blood pressure, and social isolation. The long-term consequences of childhood obesity are also explored, highlighting the risks of continued obesity into adulthood. Chapter III describes the various methods for measuring obesity in children, including body mass index (BMI), Z-score value, and percentile value. The limitations of using BMI are discussed, and alternative methods are proposed. Chapter IV focuses on the management of childhood obesity, with an emphasis on healthy eating and physical activity. The chapter outlines strategies for parents and healthcare providers to help children balance calories and develop healthy eating habits, such as reducing high-calorie foods and avoiding fast food. The importance of reducing sedentary time and increasing physical activity is also discussed, with guidelines for aerobic, muscle, and bone-strengthening exercises. Other strategies for managing childhood obesity include reducing stress and improving healthcare team outcomes. Overall, this book provides a comprehensive overview of childhood obesity, its causes, and its consequences, as well as practical advice for parents and healthcare providers on managing this growing problem.
Trainingsinduzierte Hypoalgesie im Ausdauersport

Menschen nehmen Schmerz sehr unterschiedlich wahr. Interessanterweise scheinen diejenigen, die Ausdauersport betreiben, über ein effizienteres Schmerzmodulationssystem zu verfügen als Nichtsportler*innen. Könnte Ausdauersport demnach einer Chronifizierung von Schmerzen entgegenwirken?
Basic Characterization of Natural Transformation in Avibacterium paragallinarum

Natural transformation is an important mechanism for bacteria to acquire exogenous DNA molecules during the process of evolution. Additionally, it can also be used as a method to introduce foreign genes into bacteria under laboratory conditions. ABSTRACT Avibacterium paragallinarum is the pathogen involved in infectious coryza (IC), an acute infectious upper respiratory disease in chickens. The prevalence of IC has increased in China in recent years. There is a lack of reliable and effective procedures for gene manipulation, which has limited the research on the bacterial genetics and pathogenesis of A. paragallinarum. Natural transformation has been developed as a method of gene manipulation in Pasteurellaceae by the introduction of foreign genes or DNA fragments into bacterial cells, but there has been no report on natural transformation in A. paragallinarum. In this study, we analyzed the existence of homologous genetic factors and competence proteins underlying natural transformation in A. paragallinarum and established a method for transformation in it. Through bioinformatics analysis, we identified 16 homologs of Haemophilus influenzae competence proteins in A. paragallinarum. We found that the uptake signal sequence (USS) was overrepresented in the genome of A. paragallinarum (1,537 to 1,641 copies of the core sequence ACCGCACTT). We then constructed a plasmid, pEA-KU, that carries the USS and a plasmid, pEA-K, without the USS. These plasmids can be transferred via natural transformation into naturally competent strains of A. paragallinarum. Significantly, the plasmid that carries USS showed a higher transformation efficiency. In summary, our results demonstrate that A. paragallinarum has the ability to undergo natural transformation. These findings should prove to be a valuable tool for gene manipulation in A. paragallinarum. IMPORTANCE Natural transformation is an important mechanism for bacteria to acquire exogenous DNA molecules during the process of evolution. Additionally, it can also be used as a method to introduce foreign genes into bacteria under laboratory conditions. Natural transformation does not require equipment such as an electroporation apparatus. It is easy to perform and is similar to gene transfer under natural conditions. However, there have been no reports on natural transformation in Avibacterium paragallinarum. In this study, we analyzed the presence of homologous genetic factors and competence proteins underlying natural transformation in A. paragallinarum. Our results indicate that natural competence could be induced in A. paragallinarum serovars A, B, and C. Furthermore, the method that we established to transform plasmids into naturally competent A. paragallinarum strains was stable and efficient.
Building the Ohio Hopewell Chronology: An Incremental Approach to Historical Reckoning

Abstract Ohio Hopewell is an archaeological concept that is known worldwide but that suffers from “a disarray of radiocarbon results” (Lynott 2015:60). Here, we establish a comprehensive dataset of 425 14C dates from Ohio Hopewell sites and apply formal chronometric hygiene criteria to all dates. We then iteratively assess the temporal placement and span of the six most important Ohio Hopewell sites—the Hopewell Mound Group, Liberty, Mound City, Seip, Tremper, and Turner. A staged relaxation of hygiene criteria for our best three categories (Classes 1–3) permits alternate but generally consistent conclusions. As the first large-scale analyses of Ohio Hopewell temporality since the publication of IntCal20 (Reimer et al. 2020), the available data show a ritual complex that begins 90 or more years later than generally has been recognized circa 2010 14C BP, or as Bayesian modeled, AD 90–120. Our analysis reveals site histories of differing spans, more late dates than early dates, and with most Hopewell activity ending across these sites circa 1640 14C BP, or as Bayesian modeled, AD 395–430. An increased consideration of contingency in contemporary temporal reckoning increases the utility of the historical narratives that we as archaeologists can construct.
Stochastic stabilization of sampled-data control systems based on impulsive model with multiple input delays

For a sampled-data control system with nonuniform sampling,the sampling interval sequence,which is distributed in an interval,is described as a multiple independent identically distributed process.The closed-loop system is transformed into an impulsive model with multiple input delays.By creating an appropriate discontinuous Lyapunov functional and rationally exploiting the condition that the derivative of input delay equals 1 in all sampling intervals,we derive a LMIs-based global mean-square asymptotical stability criterion based on free weight matrix approach.By using regulatory factor technique and cone-complementary linearization method,we transform the controller design method to a nonlinear optimization problem with LMI constraints,and an LMI-based iterative solution algorithm is given.Numerical examples and experimental results demonstrate the advantages and effectiveness of our theoretic results.
Patient-Guided Tours: A Patient-Centered Methodology to Understand Patient Experiences of Health Care

PURPOSE The experience-based design approach using patient-guided tours (PGT) has been suggested as an effective way to understand the patient experience and may better allow the patient to recall thoughts and feelings. The objective of this study was to assess how patients with a disability perceive the effectiveness of PGTs for understanding their experiences of receiving primary health care. METHODS A qualitative study design was used. Participants were chosen by convenience sampling. The patient was asked to walk through the clinic as they would on a “typical visit” while describing their experiences. They were questioned about their experience and perception of PGTs. The tour was audiotaped and transcribed. The investigators took field notes and completed thematic content analysis. RESULTS Eighteen patients participated. The main findings were: (1) Touchpoints and physical cues were effective in eliciting experiences that participants stated they would not have recalled using other research methods, (2) The ability for participants to show the investigator aspects of the space that impacted their experience enabled the investigator to “see through their eyes” resulting in ease of communication and a sense of empowerment, (3) PGTs encouraged individuals to be active participants which fostered comfort and collaboration, and (4) PGTs may exclude those that are severely disabled. CONCLUSION This method was perceived as effective at eliciting experiences of patients with a disability. It has benefits over more traditional research methods by allowing the participant to refresh their memory at “touchpoints” and enabling them to be active participants.