Miracle-dz/UniFAR-FTUs huggingface dataset:
Residual Q-Networks for Value Function Factorizing in Multiagent Reinforcement Learning

Multiagent reinforcement learning (MARL) is useful in many problems that require the cooperation and coordination of multiple agents. Learning optimal policies using reinforcement learning in a multiagent setting can be very difficult as the number of agents increases. Recent solutions such as value decomposition networks (VDNs), QMIX, QTRAN, and QPLEX adhere to the centralized training and decentralized execution (CTDE) scheme and perform factorization of the joint action-value functions. However, these methods still suffer from increased environmental complexity, and at times fail to converge in a stable manner. We propose a novel concept of residual Q-networks (RQNs) for MARL, which learns to transform the individual $Q$ -value trajectories in a way that preserves the individual-global-max (IGM) criteria, but is more robust in factorizing action-value functions. The RQN acts as an auxiliary network that accelerates convergence and will become obsolete as the agents reach the training objectives. The performance of the proposed method is compared against several state-of-the-art techniques such as QPLEX, QMIX, QTRAN, and VDN, in a range of multiagent cooperative tasks. The results illustrate that the proposed method, in general, converges faster, with increased stability, and shows robust performance in a wider family of environments. The improvements in results are more prominent in environments with severe punishments for noncooperative behaviors and especially in the absence of complete state information during training time.
Detection of Flare-induced Plasma Flows in the Corona of EV Lac with X-Ray Spectroscopy

Stellar flares are characterized by sudden enhancement of electromagnetic radiation from the atmospheres of stars. Compared to their solar counterparts, our knowledge on the coronal plasma dynamics of stellar flares and their connection to coronal mass ejections remains very limited. With time-resolved high-resolution spectroscopic observations from the Chandra X-ray Observatory, we detected noticeable coronal plasma flows during several stellar flares on a nearby dMe star EV Lac. In the observed spectra of O viii (3 MK), Fe xvii (6 MK), Mg xii (10 MK), and Si xiv (16 MK) lines, these flare-induced upflows/downflows appear as significant Doppler shifts of several tens to 130 km s−1 , and the upflow velocity generally increases with temperature. Variable line ratios of the Si xiii triplet reveal that this plasma flows in most flares are accompanied by an increase in the coronal plasma density and temperature. We interpret these results as X-ray evidence of chromospheric evaporation on EV Lac. In two successive flares, the plasma flow pattern and a sharp increase of the measured coronal density are highly suggestive of explosive evaporation. The transition from redshifts to blueshifts in such an explosive evaporation occurs at a temperature of at least 10 MK, much higher than that observed in solar flares (∼1 MK). However, in one flare the cool and warm upflows appear to be accompanied by a decreasing plasma density, which might be explained by a stellar filament/prominence eruption coupled to this flare. These results provide important clues to understanding the coronal plasma dynamics during flares on M dwarfs.
Threat Assessment and Management in Higher Education in the United States: A Review of the 10 Years Since the Mass Casualty Incident at Virginia Tech

The April 16, 2007 mass casualty incident at Virginia Tech had an immediate, significant, and enduring impact on approaches to campus safety in the United States. In the aftermath of the incident, there were numerous campus safety reviews, not only at Virginia Tech, but across the Commonwealth of Virginia, across the nation, and around the world. Those reviews by campus administrations, state and federal government agencies, professional associations, victim advocacy groups, and law enforcement bodies, resulted in hundreds of campus safety recommendations. Many of those recommendations were intended to improve reactive aspects of physical security, emergency preparedness, law enforcement response, and emergency notification. However, several recommendations addressed preventative approaches to enhance campus safety and bolster institutions’ capabilities to identify, investigate, assess and manage actual and potential threats of violence to the campus community. This article will summarize the changes in approaches to behavioral threat assessment and management among institutions of higher education in the United States, drawing on the lessons confirmed and learned from the Virginia Tech incident and those that followed through various research efforts.
Fine-Grained Buy-Many Mechanisms Are Not Much Better Than Bundling

Multi-item revenue-optimal mechanisms are known to be extremely complex, often offering buyers randomized lotteries of goods. In the standard buy-one model, it is known that optimal mechanisms can yield revenue infinitely higher than that of any "simple" mechanism---the ones with size polynomial in the number of items---even with just two items and a single buyer [Briest et al. 2015; Hart and Nisan 2017]. We introduce a new parameterized class of mechanisms, buy-k mechanisms, which smoothly interpolate between the classical buy-one mechanisms and the recently studied buy-many mechanisms [Chawla et al. 2022, 2019, 2020a,b]. Buy-k mechanisms allow the buyer to buy up to k many menu options. We show that restricting the seller to the class of buy-n incentive-compatible mechanisms suffices to overcome the bizarre, infinite revenue properties of the buy-one model. Our main result is that the revenue gap with respect to bundling, an extremely simple mechanism, is bounded by O(n2) for any arbitrarily correlated distribution D over n items for the case of an additive buyer. Our techniques also allow us to prove similar upper bounds for arbitrary monotone valuations, albeit with an exponential factor in the approximation. On the negative side, we show that allowing the buyer to purchase a small number of menu options does not suffice to guarantee sub-exponential approximations, even when we weaken the benchmark to the optimal buy-k deterministic mechanism. If an additive buyer is only allowed to buy k = Θ(n1/2--ε) many menu options, the gap between the revenue-optimal deterministic buy-k mechanism and bundling may be exponential in n. In particular, this implies that no "simple" mechanism can obtain a sub-exponential approximation in this regime. As a complementary result, we show that when [EQUATION], bundling recovers a poly(n) fraction of the optimal deterministic buy-k mechanism's revenue.
An expected event, but unprecedented damage: Structure and gaps of large-scale response coordination of the 2011 Thailand floods

Purpose The purpose of this paper is to focus on the large-scale flood response coordination across sectors and jurisdictions, investigating the characteristics and gaps of the 2011 Thailand flood response operations. Design/methodology/approach The large-scale flood response coordination was measured as an inter-organizational network. An extensive content analysis of news reports was conducted to identify the participating organizations and relationships among them that emerged during the initial flood response operations. Social network analysis was used to examine the patterns and gaps of coordination among the organizations. Findings The research identified three major gaps that might weaken the response coordination. First, the coordination structure was highly fragmented with many isolated actors. Second, the benefit of inter-sector relationships was not well leveraged in the system due to weak reciprocal relationships across sectors. Third, provincial level organizations did not serve as a strong liaison between local actors (cities) and national actors. Practical implications Based on the findings, the research offers suggestions to improve the performance of response coordination in recurring flood disasters. Originality/value This study is distinctive in its examination of structural characteristics of large-scale, inter-sector and multi-jurisdictional flood response coordination in Thailand. Previous studies have explored how citizens were organized and responded to flood disasters at the local level, and measured indicators or causes of response resilience at the provincial level system. Yet, studies examining the patterns of coordination structure among response organizations across all affected-jurisdictional authorities and sectors have been lacking.
Characterizing quantum pseudorandomness by machine learning

Random dynamics in isolated quantum systems is of practical use in quantum information and is of theoretical interest in fundamental physics. Despite a large number of theoretical studies, it has not been addressed how random dynamics can be verified from experimental data. In this paper, based on an information-theoretic formulation of random dynamics, i.e., unitary $t$-designs, we propose a method for verifying random dynamics from the data that is experimentally easy-to-access. More specifically, we use measurement probabilities estimated by a finite number of measurements of quantum states generated by a given random dynamics. Based on a supervised learning method, we construct classifiers of random dynamics and show that the classifiers succeed to characterize random dynamics. We then apply the classifiers to the data set generated by local random circuits (LRCs), which are canonical quantum circuits with growing circuit complexity, and show that the classifiers successfully characterize the growing features. We further apply the classifiers to noisy LRCs, showing the possibility of using them for verifying noisy quantum devices, and to monitored LRCs, indicating that the measurement-induced phase transition may possibly not be directly related to randomness.
Learning Open Domain Multi-hop Search Using Reinforcement Learning

We propose a method to teach an automated agent to learn how to search for multi-hop paths of relations between entities in an open domain. The method learns a policy for directing existing information retrieval and machine reading resources to focus on relevant regions of a corpus. The approach formulates the learning problem as a Markov decision process with a state representation that encodes the dynamics of the search process and a reward structure that minimizes the number of documents that must be processed while still finding multi-hop paths. We implement the method in an actor-critic reinforcement learning algorithm and evaluate it on a dataset of search problems derived from a subset of English Wikipedia. The algorithm finds a family of policies that succeeds in extracting the desired information while processing fewer documents compared to several baseline heuristic algorithms.
Bayesian Low-Rank Interpolative Decomposition for Complex Datasets

In this paper, we introduce a probabilistic model for learning interpolative decomposition (ID), which is commonly used for feature selection, low-rank approximation, and identifying hidden patterns in data, where the matrix factors are latent variables associated with each data dimension. Prior densities with support on the specified subspace are used to address the constraint for the magnitude of the factored component of the observed matrix. Bayesian inference procedure based on Gibbs sampling is employed. We evaluate the model on a variety of real-world datasets including CCLE EC50, CCLE IC50, CTRP EC50, and MovieLens 100K datasets with different sizes, and dimensions, and show that the proposed Bayesian ID GBT and GBTN models lead to smaller reconstructive errors compared to existing randomized approaches.
Multi-agent Databases via Independent Learning

Machine learning is rapidly being used in database research to improve the effectiveness of numerous tasks included but not limited to query optimization, workload scheduling, physical design, etc. Currently, the research focus has been on replacing a single database component responsible for one task by its learning-based counterpart. However, query performance is not simply determined by the performance of a single component, but by the cooperation of multiple ones. As such, learning based database components need to collaborate during both training and execution in order to develop policies that meet end performance goals. Thus, the paper attempts to address the question"Is it possible to design a database consisting of various learned components that cooperatively work to improve end-to-end query latency? ". To answer this question, we introduce MADB (Multi-Agent DB), a proof-of-concept system that incorporates a learned query scheduler and a learned query optimizer. MADB leverages a cooperative multi-agent reinforcement learning approach that allows the two components to exchange the context of their decisions with each other and collaboratively work towards reducing the query latency. Preliminary results demonstrate that MADB can outperform the non-cooperative integration of learned components.
To test, or not to test: A proactive approach for deciding complete performance test initiation

Software performance testing requires a set of inputs that exercise different sections of the code to identify performance issues. However, running tests on a large set of inputs can be a very time consuming process. It is even more problematic when test inputs are constantly growing, which is the case with a large-scale scientific organization such as CERN where the process of performing scientific experiment generates plethora of data that is analyzed by physicists leading to new scientific discoveries. Therefore, in this article, we present a test input minimization approach based on a clustering technique to handle the issue of testing on growing data. Furthermore, we use clustering information to propose an automatic approach that recommends the tester to decide when to run the complete test suite for performance testing. To demonstrate the efficacy of our approach, we applied it to two different code updates of a web service which is used at CERN and we found that the recommendation for performance test initiation made by our approach for an update with bottleneck is valid.
Deep Very Long Baseline Interferometry Observations Challenge Previous Evidence of a Binary Supermassive Black Hole Residing in Seyfert Galaxy NGC 7674

Previous Ku-band (15 GHz) imaging with data obtained from the Very Long Baseline Array (VLBA) had shown two compact, subparsec components at the location of a presumed kiloparsec-scale radio core in Seyfert galaxy NGC 7674. It was then presumed that these two unresolved and compact components were dual radio cores corresponding to two supermassive black holes (SMBHs) accreting surrounding gas and launching radio-bright relativistic jets. However, utilizing the original VLBA data set used to claim the detection of a binary SMBH, in addition to later multiepoch/multifrequency data sets obtained from both the VLBA and the European very long baseline interferometry (VLBI) network, we find no evidence to support the presence of a binary SMBH. We place stringent upper limits to the flux densities of any subparsec-scale radio cores that are at least an order of magnitude lower than the original VLBI radio-core detections, directly challenging the original binary SMBH detection claim. With this in mind, we discuss the possible reasons for the nondetection of any VLBI radio cores in our imaging, the possibility of a binary SMBH still residing in NGC 7674, and the prospect of future observations shedding further light on the true nature of this active galactic nucleus.
Biased random walk on random networks in presence of stochastic resetting: exact results

We consider biased random walks on random networks constituted by a random comb comprising a backbone with quenched-disordered random-length branches. The backbone and the branches run in the direction of the bias. For the bare model as also when the model is subject to stochastic resetting, whereby the walkers on the branches reset with a constant rate to the respective backbone sites, we obtain exact stationary-state static and dynamic properties for a given disorder realization of branch lengths sampled following an arbitrary distribution. We derive a criterion to observe in the stationary state a non-zero drift velocity along the backbone. For the bare model, we discuss the occurrence of a drift velocity that is non-monotonic as a function of the bias, becoming zero beyond a threshold bias because of walkers trapped at very long branches. Further, we show that resetting allows the system to escape trapping, resulting in a drift velocity that is finite at any bias.
Why Adversarial Training of ReLU Networks Is Difficult?

This paper mathematically derives an analytic solution of the adversarial perturbation on a ReLU network, and theoretically explains the difﬁculty of adversarial training. Speciﬁcally, we formulate the dynamics of the adversarial perturbation generated by the multi-step attack, which shows that the adversarial perturbation tends to strengthen eigenvectors corresponding to a few top-ranked eigenvalues of the Hessian matrix of the loss w.r. t. the input. We also prove that adversarial training tends to strengthen the inﬂuence of unconﬁdent input samples with large gradient norms in an exponential manner. Besides, we ﬁnd that adversarial training strengthens the inﬂuence of the Hessian matrix of the loss w.r. t. network parameters, which makes the adversarial training more likely to oscillate along directions of a few samples, and boosts the difﬁculty of adversarial training. Crucially, our proofs provide a uniﬁed explanation for previous ﬁndings in understanding adversarial training [13, 8, 28, 30, 1, 26, 7, 14, 2, 20].
Flowification: Everything is a Normalizing Flow

The two key characteristics of a normalizing flow is that it is invertible (in particular, dimension preserving) and that it monitors the amount by which it changes the likelihood of data points as samples are propagated along the network. Recently, multiple generalizations of normalizing flows have been introduced that relax these two conditions. On the other hand, neural networks only perform a forward pass on the input, there is neither a notion of an inverse of a neural network nor is there one of its likelihood contribution. In this paper we argue that certain neural network architectures can be enriched with a stochastic inverse pass and that their likelihood contribution can be monitored in a way that they fall under the generalized notion of a normalizing flow mentioned above. We term this enrichment flowification. We prove that neural networks only containing linear layers, convolutional layers and invertible activations such as LeakyReLU can be flowified and evaluate them in the generative setting on image datasets.
Magnetic collapse in Fe 3 Se 4 under high pressure

: Electronic structure and magnetic properties of Fe 3 Se 4 are calculated within the density functional approach. Due to the metallic properties, magnetic moments of the iron atoms in two nonequivalent positions in the unit cell are different from ionic values for Fe 3 + and Fe 2 + and are equal to M 1 = 2.071 µ B and M 2 = − 2.042 µ B making the system ferrimagnetic. The total magnetic moment for the unit cell is 2.135 µ B . Under the isotropic compression, the total magnetic moment decreases non-monotonically and correlates with the non-monotonic dependence of the density of states at the Fermi level N ( E F ) . For 7% compression, the magnetic order changes from the ferrimagnetic to the ferromagnetic. At 14% compression, the magnetic order disappears and the magnetic moment becomes zero. This compression corresponds to the pressure of 114 GPa. The system remains in the metallic state for all values of compression.
Image Super-resolution with An Enhanced Group Convolutional Neural Network

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://github.com/hellloxiaotian/ESRGCNN.
An efficient polynomial-time approximation scheme for parallel multi-stage open shops

Various new scheduling problems have been arising from practical production processes and spawning new research areas in the scheduling field. We study the parallel multi-stage open shops problem, which generalizes the classic open shop scheduling and parallel machine scheduling problems. Given m identical k-stage open shops and a set of n jobs, we aim to process all jobs on these open shops with the minimum makespan, i.e., the completion time of the last job, under the constraint that job preemption is not allowed. We present an efficient polynomial-time approximation scheme (EPTAS) for the case when both m and k are constant. The main idea for our EPTAS is the combination of several categorization, scaling, and linear programming rounding techniques. Jobs and/or operations are first scaled and then categorized carefully into multiple types so that different types of jobs and/or operations are scheduled appropriately without increasing the makespan too much.
EVIboost for the Estimation of Extreme Value Index Under Heterogeneous Extremes

Modeling heterogeneity on heavy-tailed distributions under a regression framework is challenging, yet classical statistical methodologies usually place conditions on the distribution models to facilitate the learning procedure. However, these conditions will likely overlook the complex dependence structure between the heaviness of tails and the covariates. Moreover, data sparsity on tail regions makes the inference method less stable, leading to biased estimates for extreme-related quantities. This paper proposes a gradient boosting algorithm to estimate a functional extreme value index with heterogeneous extremes. Our proposed algorithm is a data-driven procedure capturing complex and dynamic structures in tail distributions. We also conduct extensive simulation studies to show the prediction accuracy of the proposed algorithm. In addition, we apply our method to a real-world data set to illustrate the state-dependent and time-varying properties of heavy-tail phenomena in the financial industry.