Miracle-dz/UniFAR-FTUs huggingface dataset:
pyRDDLGym: From RDDL to Gym Environments

We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym environments from RDDL declerative description. The discrete time step evolution of variables in RDDL is described by conditional probability functions, which fits naturally into the Gym step scheme. Furthermore, since RDDL is a lifted description, the modification and scaling up of environments to support multiple entities and different configurations becomes trivial rather than a tedious process prone to errors. We hope that pyRDDLGym will serve as a new wind in the reinforcement learning community by enabling easy and rapid development of benchmarks due to the unique expressive power of RDDL. By providing explicit access to the model in the RDDL description, pyRDDLGym can also facilitate research on hybrid approaches for learning from interaction while leveraging model knowledge. We present the design and built-in examples of pyRDDLGym, and the additions made to the RDDL language that were incorporated into the framework.
Inferring probabilistic Boolean networks from steady-state gene data samples

Probabilistic Boolean Networks have been proposed for estimating the behaviour of dynamical systems as they combine rule-based modelling with uncertainty principles. Inferring PBNs directly from gene data is challenging however, especially when data is costly to collect and/or noisy, e.g., in the case of gene expression profile data. In this paper, we present a reproducible method for inferring PBNs directly from real gene expression data measurements taken when the system was at a steady state. The steady-state dynamics of PBNs is of special interest in the analysis of biological machinery. The proposed approach does not rely on reconstructing the state evolution of the network, which is computationally intractable for larger networks. We demonstrate the method on samples of real gene expression profiling data from a well-known study on metastatic melanoma. The pipeline is implemented using Python and we make it publicly available.
Convergence of the integral fluctuation theorem estimator for nonequilibrium Markov systems

The integral fluctuation theorem (IFT) for entropy production is among the few equalities that are known to be valid for physical systems arbitrarily driven far from equilibrium. Microscopically, it can be understood as an inherent symmetry for the fluctuating entropy production rate implying the second law of thermodynamics. Here, we examine an IFT statistical estimator based on regular sampling and discuss its limitations for nonequilibrium systems, when sampling rare events becomes pivotal. Furthermore, via a large deviation study, we discuss a method to carefully setup an experiment in the parameter region where the IFT estimator safely converges and also show how to improve the convergence region for Markov chains with finite correlation time. We corroborate our arguments with two illustrative examples.
MF2-MVQA: A Multi-Stage Feature Fusion Method for Medical Visual Question Answering

There is a key problem in the medical visual question answering task that how to effectively realize the feature fusion of language and medical images with limited datasets. In order to better utilize multi-scale information of medical images, previous methods directly embed the multi-stage visual feature maps as tokens of same size respectively and fuse them with text representation. However, this will cause the confusion of visual features at different stages. To this end, we propose a simple but powerful multi-stage feature fusion method, MF2-MVQA, which stage-wise fuses multi-level visual features with textual semantics. MF2-MVQA achieves the State-Of-The-Art performance on VQA-Med 2019 and VQA-RAD dataset. The results of visualization also verify that our model outperforms previous work.
Intrinsically-Motivated Goal-Conditioned Reinforcement Learning in Multi-Agent Environments

How can a population of reinforcement learning agents autonomously learn a diversity of cooperative tasks in a shared environment? In the single-agent paradigm, goal-conditioned policies have been combined with intrinsic motivation mechanisms to endow agents with the ability to master a wide diversity of autonomously discovered goals. Transferring this idea to cooperative multi-agent systems (MAS) entails a challenge: intrinsically motivated agents that sample goals independently focus on a shared cooperative goal with low probability, impairing their learning performance. In this work, we propose a new learning paradigm for modeling such settings, the Decentralized Intrinsically Motivated Skill Acquisition Problem (Dec-IMSAP), and employ it to solve cooperative navigation tasks. Agents in a Dec-IMSAP are trained in a fully de-centralized way, which comes in contrast to previous contributions in multi-goal MAS that consider a centralized goal-selection mechanism. Our empirical analysis indicates that a sufficient condition for efficiently learning a diversity of cooperative tasks is to ensure that a group aligns its goals, i.e., the agents pursue the same cooperative goal and learn to coordinate their actions through specialization. We introduce the Goal-coordination game, a fully-decentralized emergent communication algorithm, where goal alignment emerges from the maximization of individual rewards in multi-goal cooperative environments and show that it is able to reach equal performance to a centralized training baseline that guarantees aligned goals. To our knowledge, this is the first contribution addressing the problem of intrinsically motivated multi-agent goal exploration in a decentralized training
Lamda: The Last Mile of the Datacenter Network Does matter

In this paper, we conduct systematic measurement studies to show that the high memory bandwidth consumption of modern distributed applications can lead to a signiﬁcant drop of network throughput and a large increase of tail latency in high-speed RDMA networks. We identify its root cause as the high contention of memory bandwidth between application processes and network processes. This contention leads to frequent packet drops at the NIC of receiving hosts, which triggers the congestion control mechanism of the network and eventually results in network performance degradation. To tackle this problem, we make a key observation that given the distributed storage service, the vast majority of data it receives from the network will be eventually written to high-speed storage media (e.g., SSD) by CPU. As such, we propose to bypass host memory when processing received data to completely circumvent this performance bottleneck. In particular, we design Lamda, a novel receiver cache processing system that consumes a small amount of CPU cache to process received data from the network at line rate. We implement a prototype of Lamda and evaluate its performance extensively in a Clos-based testbed. Results show that for distributed storage applications, Lamda improves network throughput by 4.7% with zero memory bandwidth consumption on storage nodes, and improves network throughput by up 17% and 45% for large block size and small size under the memory bandwidth
Prior-mean-assisted Bayesian optimization application on FRIB Front-End tunning

Bayesian optimization (BO) is often used for accelerator tuning due to its high sample efﬁciency. However, the computational scalability of training over large data-set can be problematic and the adoption of historical data in a computationally efﬁcient way is not trivial. Here, we exploit a neural network model trained over historical data as a prior mean of BO for FRIB Front-End tuning.
Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network

Lagrangian and Hamiltonian neural networks (LNNs and HNNs, respectively) encode strong inductive biases that allow them to outperform other models of physical systems significantly. However, these models have, thus far, mostly been limited to simple systems such as pendulums and springs or a single rigid body such as a gyroscope or a rigid rotor. Here, we present a Lagrangian graph neural network (LGNN) that can learn the dynamics of articulated rigid bodies by exploiting their topology. We demonstrate the performance of LGNN by learning the dynamics of ropes, chains, and trusses with the bars modeled as rigid bodies. LGNN also exhibits generalizability -- LGNN trained on chains with a few segments exhibits generalizability to simulate a chain with large number of links and arbitrary link length. We also show that the LGNN can simulate unseen hybrid systems including bars and chains, on which they have not been trained on. Specifically, we show that the LGNN can be used to model the dynamics of complex real-world structures such as the stability of tensegrity structures. Finally, we discuss the non-diagonal nature of the mass matrix and its ability to generalize in complex systems.
SimProp: a simulation code for ultra high energy cosmic ray propagation

A new Monte Carlo simulation code for the propagation of Ultra High Energy Cosmic Rays is presented. The results of this simulation scheme are tested by comparison with results of another Monte Carlo computation as well as with the results obtained by directly solving the kinetic equation for the propagation of Ultra High Energy Cosmic Rays. A short comparison with the latest flux published by the Pierre Auger collaboration is also presented.
RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization

Feature reuse has been a key technique in light-weight convolutional neural networks (CNNs) design. Current methods usually utilize a concatenation operator to keep large channel numbers cheaply (thus large network capacity) by reusing feature maps from other layers. Although concatenation is parameters- and FLOPs-free, its computational cost on hardware devices is non-negligible. To address this, this paper provides a new perspective to realize feature reuse via structural re-parameterization technique. A novel hardware-efficient RepGhost module is proposed for implicit feature reuse via re-parameterization, instead of using concatenation operator. Based on the RepGhost module, we develop our efficient RepGhost bottleneck and RepGhostNet. Experiments on ImageNet and COCO benchmarks demonstrate that the proposed RepGhostNet is much more effective and efficient than GhostNet and MobileNetV3 on mobile devices. Specially, our RepGhostNet surpasses GhostNet 0.5x by 2.5% Top-1 accuracy on ImageNet dataset with less parameters and comparable latency on an ARM-based mobile phone.
SceneFake: An Initial Dataset and Benchmarks for Scene Fake Audio Detection

Previous databases have been designed to further the development of fake audio detection. However, fake utterances are mostly generated by altering timbre, prosody, linguistic content or channel noise of original audios. They ignore a fake situation, in which the attacker manipulates an acoustic scene of the original audio with another forgery one. It will pose a major threat to our society if some people misuse the manipulated audio with malicious purpose. Therefore, this motivates us to fill in the gap. This paper designs such a dataset for scene fake audio detection (SceneFake). A manipulated audio in the SceneFake dataset involves only tampering the acoustic scene of an utterance by using speech enhancement technologies. We can not only detect fake utterances on a seen test set but also evaluate the generalization of fake detection models to unseen manipulation attacks. Some benchmark results are described on the SceneFake dataset. Besides, an analysis of fake attacks with different speech enhancement technologies and signal-to-noise ratios are presented on the dataset. The results show that scene manipulated utterances can not be detected reliably by the existing baseline models of ASVspoof 2019. Furthermore, the detection of unseen scene manipulation audio is still challenging.
A Multimodal Embedding-Based Approach to Industry Classification in Financial Markets

Industry classification schemes provide a taxonomy for segmenting companies based on their business activities. They are relied upon in industry and academia as an integral component of many types of financial and economic analysis. However, even modern classification schemes have failed to embrace the era of big data and remain a largely subjective undertaking prone to inconsistency and misclassification. To address this, we propose a multimodal neural model for training company embeddings, which harnesses the dynamics of both historical pricing data and financial news to learn objective company representations that capture nuanced relationships. We explain our approach in detail and highlight the utility of the embeddings through several case studies and application to the downstream task of industry classification.
Synchronization of an array of spin torque nano oscillators in periodic applied external magnetic field

Considering an array of spin torque transfer nano oscillators (STNOs), we have investigated the synchronization property of the system under the action of a common periodically driven applied external magnetic field by numerically analyzing the underlying system of Landau-Lifshitz-Gilbert-Slonczewski (LLGS) equations for the macro-spin variables. We find the novel result that the applied external magnetic field can act as a medium to induce synchronization of periodic oscillations, both in-phase and anti-phase, even without coupling through spin current, thereby leading to the exciting possibility of enhancement of microwave power in a straightforward way.
The Observable Properties of Galaxy Accretion Events in Milky Way–like Galaxies in the FIRE-2 Cosmological Simulations

In the Λ-Cold Dark Matter model of the universe, galaxies form in part through accreting satellite systems. Previous works have built an understanding of the signatures of these processes contained within galactic stellar halos. This work revisits that picture using seven Milky Way–like galaxies in the Latte suite of FIRE-2 cosmological simulations. The resolution of these simulations allows a comparison of contributions from satellites aboveM * ≳ 10 × 7 M ⊙, enabling the analysis of observable properties for disrupted satellites in a fully self-consistent and cosmological context. Our results show that the time of accretion and the stellar mass of an accreted satellite are fundamental parameters that in partnership dictate the resulting spatial distribution, orbital energy, and [α/Fe]-[Fe/H] compositions of the stellar debris of such mergers at present day. These parameters also govern the resulting dynamical state of an accreted galaxy at z = 0, leading to the expectation that the inner regions of the stellar halo (R GC ≲ 30 kpc) should contain fully phase-mixed debris from both lower- and higher-mass satellites. In addition, we find that a significant fraction of the lower-mass satellites accreted at early times deposit debris in the outer halo (R GC > 50 kpc) that are not fully phased-mixed, indicating that they could be identified in kinematic surveys. Our results suggest that, as future surveys become increasingly able to map the outer halo of our Galaxy, they may reveal the remnants of long-dead dwarf galaxies whose counterparts are too faint to be seen in situ in higher-redshift surveys.
Gravitational Waves from Feebly Interacting Particles in a First Order Phase Transition

First order phase transitions are well-motivated and extensively studied sources of gravitational waves (GWs) from the early Universe. The vacuum energy released during such transitions is assumed to be transferred primarily either to the expanding walls of bubbles of true vacuum, whose collisions source GWs, or to the surrounding plasma, producing sound waves and turbulence, which act as GW sources. In this Letter, we study an alternative possibility that has so far not been considered: the released energy gets transferred primarily to feebly interacting particles that do not admit a ﬂuid description but simply free-stream individually. We develop the formalism to study the production of GWs from such conﬁgurations, and demonstrate that such GW signals have qualitatively distinct characteristics compared to conventional sources and are potentially observable with near-future GW detectors.
Discrimination of nuclear and electronic recoil events using plasma effect in germanium detectors

We report a new method of using the plasma time difference, which results from the plasma effect, between the nuclear and electronic recoil events in high-purity germanium detectors to distinguish these two types of events in the search for rare physics processes. The physics mechanism of the plasma effect is discussed in detail. A numerical model is developed to calculate the plasma time for nuclear and electronic recoils at various energies in germanium detectors. It can be shown that under certain conditions the plasma time difference is large enough to be observable. The experimental aspects in realizing such a discrimination in germanium detectors is discussed.
The BlackParrot BedRock Cache Coherence System

This paper presents BP-BedRock, the open-source cache coherence protocol and system implemented within the BlackParrot 64-bit RISC-V multicore processor. BP-BedRock implements the BedRock directory-based MOESIF cache coherence protocol and includes two different open-source coherence protocol engines, one FSM-based and the other microcode programmable. Both coherence engines support coherent uncacheable access to cacheable memory and L1-based atomic read-modify-write operations. Fitted within the BlackParrot multicore, BP-BedRock has been silicon validated in a GlobalFoundries 12nm FinFET process and FPGA validated with both coherence engines in 8-core configurations, booting Linux and running off the shelf benchmarks. After describing BP-BedRock and the design of the two coherence engines, we study their performance by analyzing processing occupancy and running the Splash-3 benchmarks on the 8-core FPGA implementations. Careful design and coherence-specific ISA extensions enable the programmable controller to achieve performance within 1% of the fixed-function FSM controller on average (2.3% worst-case) as demonstrated on our FPGA test system. Analysis shows that the programmable coherence engine increases die area by only 4% in an ASIC process and increases logic utilization by only 6.3% on FPGA with one additional block RAM added per core.
SIMPLICIAL APPROACH TO PATH HOMOLOGY OF QUIVERS, SUBSETS OF GROUPS AND SUBMODULES OF ALGEBRAS

We develop a generalisation of the path homology theory introduced by Grigor’yan, Lin, Muranov and Yau (GLMY-theory) in a general simplicial setting. The new theory includes as particular cases the GLMYtheory for path complexes and new homology theories: homology of subsets of groups and Hochschild homology of submodules of algebras. Using our general machinery, we also introduce a new homology theory for quivers that we call square-commutative homology of quivers and compare it with the theory developed by Grigor’yan, Muranov, Vershinin and Yau.
Re-visiting Reservoir Computing architectures optimized by Evolutionary Algorithms

For many years, Evolutionary Algorithms (EAs) have been applied to improve Neural Networks (NNs) architectures. They have been used for solving different problems, such as training the networks (adjusting the weights), designing network topology, optimizing global parameters, and selecting features. Here, we provide a systematic brief survey about applications of the EAs on the specific domain of the recurrent NNs named Reservoir Computing (RC). At the beginning of the 2000s, the RC paradigm appeared as a good option for employing recurrent NNs without dealing with the inconveniences of the training algorithms. RC models use a nonlinear dynamic system, with fixed recurrent neural network named the \textit{reservoir}, and learning process is restricted to adjusting a linear parametric function. %so the performance of learning is fast and precise. However, an RC model has several hyper-parameters, therefore EAs are helpful tools to figure out optimal RC architectures. We provide an overview of the results on the area, discuss novel advances, and we present our vision regarding the new trends and still open questions.
Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness

Developing robust and fair AI systems require datasets with comprehensive set of labels that can help ensure the validity and legitimacy of relevant measurements. Recent efforts, therefore, focus on collecting person-related datasets that have carefully selected labels, including sensitive characteristics, and consent forms in place to use those attributes for model testing and development. Responsible data collection involves several stages, including but not limited to determining use-case scenarios, selecting categories (annotations) such that the data are fit for the purpose of measuring algorithmic bias for subgroups and most importantly ensure that the selected categories/subcategories are robust to regional diversities and inclusive of as many subgroups as possible. Meta, in a continuation of our efforts to measure AI algorithmic bias and robustness (https://ai.facebook.com/blog/shedding-light-on-fairness-in-ai-with-a-new-data-set), is working on collecting a large consent-driven dataset with a comprehensive list of categories. This paper describes our proposed design of such categories and subcategories for Casual Conversations v2.
Antimatter in the Direct-Action Theory of Fields

One of Feynman's greatest contributions to physics was the interpretation of negative energies as antimatter in quantum field theory. A key component of this interpretation is the Feynman propagator, which seeks to describe the behavior of antimatter at the virtual particle level. Ironically, it turns out that one can dispense with the Feynman propagator in a direct-action theory of fields, while still retaining the interpretation of negative energy solutions as antiparticles.
QED corrections of order alpha(Z alpha)E-2(F) to the hyperfine splitting of P-1/2 and P-3/2 states in hydrogenlike ions

The hyperfine structure (HFS) of a bound electron is modified by the self-interaction of the electron with its own radiation field. This effect is known as the self-energy correction. In this work, we discuss the evaluation of higher-order self-energy corrections to the HFS of bound P states. These are expressed in a semi-analytic expansion involving powers of Zalpha and ln(Zalpha), where Z is the nuclear charge number and alpha is the fine-structure constant. We find that the correction of relative order alpha (Zalpha)^2 involves only a single logarithm ln(Zalpha) for P_1/2 states [but no term of order alpha (Zalpha)^2 ln^2(Zalpha), whereas for P_3/2 states, even the single logarithm vanishes. By a Foldy-Wouthuysen transformation, we identify a nuclear-spin dependent correction to the electron's transition current, which contributes to the HFS of P states. A comparison of the obtained analytic results to a numerical approach is made.
Exploring Mental Health Communications among Instagram Coaches

There has been a significant expansion in the use of online social networks (OSNs) to support people experiencing mental health issues. This paper studies the role of Instagram influencers who specialize in coaching people with mental health issues. Using a dataset of 97k posts, we characterize such users' linguistic and behavioural features. We explore how these observations impact audience engagement (as measured by likes). We show that the support provided by these accounts varies based on their self-declared professional identities. For instance, Instagram accounts that declare themselves as Authors offer less support than accounts that label themselves as a Coach. We show that increasing information support in general communication positively affects user engagement. However, the effect of vocabulary on engagement is not consistent across the Instagram account types. Our findings shed light on this understudied topic and guide how mental health practitioners can improve outreach.