Miracle-dz/UniFAR-FTUs huggingface dataset:
jaxsnn: Event-driven Gradient Estimation for Analog Neuromorphic Hardware

Traditional neuromorphic hardware architectures rely on event-driven computation, where the asynchronous transmission of events, such as spikes, triggers local computations within synapses and neurons. While machine learning frameworks are commonly used for gradient-based training, their emphasis on dense data structures poses challenges for processing asynchronous data such as spike trains. This problem is particularly pronounced for typical tensor data structures. In this context, we present a novel library (jaxsnn) built on top of JAX, that departs from conventional machine learning frameworks by providing flexibility in the data structures used and the handling of time, while maintaining Autograd functionality and composability. Our library facilitates the simulation of spiking neural networks and gradient estimation, with a focus on compatibility with time-continuous neuromorphic backends, such as the BrainScaleS-2 system, during the forward pass. This approach opens avenues for more efficient and flexible training of spiking neural networks, bridging the gap between traditional neuromorphic architectures and contemporary machine learning frameworks.
Is Artificial Intelligence Providing the Second Revolution for Weather Forecasting?

The rapid advancement of artificial intelligence technologies, particularly in recent years, has led to the emergence of several large parameter artificial intelligence weather forecast models. These models represent a significant breakthrough, overcoming the limitations of traditional numerical weather prediction models and indicating a potential second revolution for weather forecast. This study explores the evolution of these advanced artificial intelligence forecast models, and based on the identified commonalities, proposes the"Three Large Rules"for their development. We discuss the potential of artificial intelligence in revolutionizing numerical weather prediction, briefly outlining the underlying reasons for this potential. Additionally, we explore key areas for future development prospects for large artificial intelligence weather forecast models, integrating the entire numerical prediction process. Through an example that combines a large artificial intelligence model with ocean wave forecasting, we illustrate how forecasters can adapt and leverage the advanced artificial intelligence model. While acknowledging the high accuracy, computational efficiency, and ease of deployment of large artificial intelligence forecast models, we emphasize the irreplaceable values of traditional numerical forecasts. We believe that the optimal future of weather forecasting lies in achieving a seamless integration of artificial intelligence and traditional numerical models. Such a synthesis is anticipated to offer a more comprehensive and reliable approach for future weather forecasting.
A novel method to determine the phase-space distribution of a pulsed molecular beam

We demonstrate a novel method to determine the longitudinal phase-space distribution of a cryogenic buffer gas beam of barium-fluoride molecules based on a two-step laser excitation scheme. The spatial resolution is achieved by a transversely aligned laser beam that drives molecules from the ground state $X^2\Sigma^+$ to the $A^2\Pi_{1/2}$ state around 860 nm, while the velocity resolution is obtained by a laser beam that is aligned counter-propagating with respect to the molecular beam and that drives the Doppler shifted $A^2\Pi_{1/2}$ to $D^2\Sigma^+$ transition around 797 nm. Molecules in the $D$-state are detected virtually background-free by recording the fluorescence from the $D-X$ transition at 413 nm. As molecules in the ground state do not absorb light at 797 nm, problems due to due to optical pumping are avoided. Furthermore, as the first step uses a narrow transition, this method can also be applied to molecules with hyperfine structure. The measured phase-space distributions, reconstructed at the source exit, show that the average velocity and velocity spread vary significantly over the duration of the molecular beam pulse. Our method gives valuable insight into the dynamics in the source and helps to reduce the velocity and increase the intensity of cryogenic buffer gas beams. In addition, transition frequencies are reported for the $X-A$ and $X-D$ transitions in barium fluoride with an absolute accuracy below 0.3 MHz.
ReGAL: Refactoring Programs to Discover Generalizable Abstractions

While large language models (LLMs) are increasingly being used for program synthesis, they lack the global view needed to develop useful abstractions; they generally predict programs one at a time, often repeating the same functionality. Generating redundant code from scratch is both inefficient and error-prone. To address this, we propose Refactoring for Generalizable Abstraction Learning (ReGAL), a gradient-free method for learning a library of reusable functions via code refactorization, i.e. restructuring code without changing its execution output. ReGAL learns from a small set of existing programs, iteratively verifying and refining its abstractions via execution. We find that the shared function libraries discovered by ReGAL make programs easier to predict across diverse domains. On three datasets (LOGO graphics generation, Date reasoning, and TextCraft, a Minecraft-based text game), both open-source and proprietary LLMs improve in accuracy when predicting programs with ReGAL functions. For CodeLlama-13B, ReGAL results in absolute accuracy increases of 11.5% on graphics, 26.1% on date understanding, and 8.1% on TextCraft, outperforming GPT-3.5 in two of three domains. Our analysis reveals ReGAL's abstractions encapsulate frequently-used subroutines as well as environment dynamics.
ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models

This paper introduces ESPnet-SPK, a toolkit designed with several objectives for training speaker embedding extractors. First, we provide an open-source platform for researchers in the speaker recognition community to effortlessly build models. We provide several models, ranging from x-vector to recent SKA-TDNN. Through the modularized architecture design, variants can be developed easily. We also aspire to bridge developed models with other domains, facilitating the broad research community to effortlessly incorporate state-of-the-art embedding extractors. Pre-trained embedding extractors can be accessed in an off-the-shelf manner and we demonstrate the toolkit's versatility by showcasing its integration with two tasks. Another goal is to integrate with diverse self-supervised learning features. We release a reproducible recipe that achieves an equal error rate of 0.39% on the Vox1-O evaluation protocol using WavLM-Large with ECAPA-TDNN.
MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models

Large language models (LLMs) are increasingly relied upon for complex multi-turn conversations across diverse real-world applications. However, existing benchmarks predominantly focus on single-turn evaluations, overlooking the models' capabilities in multi-turn interactions. To address this gap, we introduce MT-Eval, a comprehensive benchmark designed to evaluate multi-turn conversational abilities. By analyzing human-LLM conversations, we categorize interaction patterns into four types: recollection, expansion, refinement, and follow-up. We construct multi-turn queries for each category either by augmenting existing datasets or by creating new examples with GPT-4 to avoid data leakage. To study the factors impacting multi-turn abilities, we create single-turn versions of the 1170 multi-turn queries and compare performance. Our evaluation of 11 well-known LLMs shows that while closed-source models generally surpass open-source ones, certain open-source models exceed GPT-3.5-Turbo in specific tasks. We observe significant performance degradation in multi-turn settings compared to single-turn settings in most models, which is not correlated with the models' fundamental capabilities. Moreover, we identify the distance to relevant content and susceptibility to error propagation as the key factors influencing multi-turn performance. MT-Eval is released publicly to encourage future research towards more robust conversational models.
Unexpected linear conductivity in Landau-Zener model: limitations and improvements of the relaxation time approximation in the quantum master equation

The nonequilibrium steady states of quantum materials have many challenges. Here, we highlight issues with the relaxation time approximation (RTA) for the DC conductivity in insulating systems. The RTA to the quantum master equation (QME) is frequently employed as a simple method, yet this phenomenological approach is exposed as a fatal approximation, displaying metallic DC conductivity in insulating systems within the linear response regime. We find that the unexpected metallic behavior is caused by the fact that the density matrix in the RTA incompletely incorporates the first order of the external field. To solve this problem, we have derived a new calculation scheme based on the QME that ensure correct behavior in low electric fields. Our method reproduces well the overall features of the exact electric currents in the whole field region. It is not time-consuming, and its application to lattice systems is straightforward. This method will encourage progress in this research area as a simple way to more accurately describe nonequilibrium steady states.
Towards Large-scale Network Emulation on Analog Neuromorphic Hardware

We present a novel software feature for the BrainScaleS-2 accelerated neuromorphic platform that facilitates the emulation of partitioned large-scale spiking neural networks. This approach is well suited for many deep spiking neural networks, where the constraint of the largest recurrent subnetwork fitting on the substrate or the limited fan-in of neurons is often not a limitation in practice. We demonstrate the training of two deep spiking neural network models, using the MNIST and EuroSAT datasets, that exceed the physical size constraints of a single-chip BrainScaleS-2 system. The ability to emulate and train networks larger than the substrate provides a pathway for accurate performance evaluation in planned or scaled systems, ultimately advancing the development and understanding of large-scale models and neuromorphic computing architectures.
Dynamic Electro-Optic Analog Memory for Neuromorphic Photonic Computing

Artificial intelligence (AI) has seen remarkable advancements across various domains, including natural language processing, computer vision, autonomous vehicles, and biology. However, the rapid expansion of AI technologies has escalated the demand for more powerful computing resources. As digital computing approaches fundamental limits, neuromorphic photonics emerges as a promising platform to complement existing digital systems. In neuromorphic photonic computing, photonic devices are controlled using analog signals. This necessitates the use of digital-to-analog converters (DAC) and analog-to-digital converters (ADC) for interfacing with these devices during inference and training. However, data movement between memory and these converters in conventional von Neumann computing architectures consumes energy. To address this, analog memory co-located with photonic computing devices is proposed. This approach aims to reduce the reliance on DACs and ADCs and minimize data movement to enhance compute efficiency. This paper demonstrates a monolithically integrated neuromorphic photonic circuit with co-located capacitive analog memory and compares various analog memory technologies for neuromorphic photonic computing using the MNIST dataset as a benchmark.
GoAmazon2014/15. Oxidation Flow Reactor Final Campaign Report

The primary goal of the Green Ocean Amazon (GoAmazon2014/5) field campaign was to measure and mechanistically understand the formation of particle number and mass in a region affected by large tropical rainforest biogenic emissions and sometimes anthropogenic influence from a large urban center. As part of the two intensive operational periods (IOPs) and in collaboration with Pacific Northwest National Laboratory (PNNL) and Harvard, the Jimenez Group proposed to deploy a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS), Thermal Denuder (TD), Scanning Mobility Particle Size (SMPS), two oxidation flow reactors (OFR; including supporting O3, CO/CO2/CH4, RH analyzers), and a high volume filter sampler (MCV) for the measurement of gas and aerosol chemical, physicochemical, and volatility properties. The two IOPs were conducted during the wet season (February to March, 2014) and dry season (August to October, 2014). This proposal was part of a collaborative proposal involving other university and government laboratories.
Layered and Staged Monte Carlo Tree Search for SMT Strategy Synthesis

Modern SMT solvers, such as Z3, offer user-controllable strategies, enabling users to tailor them for their unique set of instances, thus dramatically enhancing solver performance for their use case. However, this approach of strategy customization presents a significant challenge: handcrafting an optimized strategy for a class of SMT instances remains a complex and demanding task for both solver developers and users alike. In this paper, we address this problem of automatic SMT strategy synthesis via a novel Monte Carlo Tree Search (MCTS) based method. Our method treats strategy synthesis as a sequential decision-making process, whose search tree corresponds to the strategy space, and employs MCTS to navigate this vast search space. The key innovations that enable our method to identify effective strategies, while keeping costs low, are the ideas of layered and staged MCTS search. These novel approaches allow for a deeper and more efficient exploration of the strategy space, enabling us to synthesize more effective strategies than the default ones in state-of-the-art (SOTA) SMT solvers. We implement our method, dubbed Z3alpha, as part of the Z3 SMT solver. Through extensive evaluations across 6 important SMT logics, Z3alpha demonstrates superior performance compared to the SOTA synthesis tool FastSMT, the default Z3 solver, and the CVC5 solver on most benchmarks. Remarkably, on a challenging QF_BV benchmark set, Z3alpha solves 42.7% more instances than the default strategy in the Z3 SMT solver.
Traffic estimation in unobserved network locations using data-driven macroscopic models

This paper leverages macroscopic models and multi-source spatiotemporal data collected from automatic traffic counters and probe vehicles to accurately estimate traffic flow and travel time in links where these measurements are unavailable. This problem is critical in transportation planning applications where the sensor coverage is low and the planned interventions have network-wide impacts. The proposed model, named the Macroscopic Traffic Estimator (MaTE), can perform network-wide estimations of traffic flow and travel time only using the set of observed measurements of these quantities. Because MaTE is grounded in macroscopic flow theory, all parameters and variables are interpretable. The estimated traffic flow satisfies fundamental flow conservation constraints and exhibits an increasing monotonic relationship with the estimated travel time. Using logit-based stochastic traffic assignment as the principle for routing flow behavior makes the model fully differentiable with respect to the model parameters. This property facilitates the application of computational graphs to learn parameters from vast amounts of spatiotemporal data. We also integrate neural networks and polynomial kernel functions to capture link flow interactions and enrich the mapping of traffic flows into travel times. MaTE also adds a destination choice model and a trip generation model that uses historical data on the number of trips generated by location. Experiments on synthetic data show that the model can accurately estimate travel time and traffic flow in out-of-sample links. Results obtained using real-world multi-source data from a large-scale transportation network suggest that MaTE outperforms data-driven benchmarks, especially in travel time estimation. The estimated parameters of MaTE are also informative about the hourly change in travel demand and supply characteristics of the transportation network.
Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models

In the evolving landscape of online communication, moderating hate speech (HS) presents an intricate challenge, compounded by the multimodal nature of digital content. This comprehensive survey delves into the recent strides in HS moderation, spotlighting the burgeoning role of large language models (LLMs) and large multimodal models (LMMs). Our exploration begins with a thorough analysis of current literature, revealing the nuanced interplay between textual, visual, and auditory elements in propagating HS. We uncover a notable trend towards integrating these modalities, primarily due to the complexity and subtlety with which HS is disseminated. A significant emphasis is placed on the advances facilitated by LLMs and LMMs, which have begun to redefine the boundaries of detection and moderation capabilities. We identify existing gaps in research, particularly in the context of underrepresented languages and cultures, and the need for solutions to handle low-resource settings. The survey concludes with a forward-looking perspective, outlining potential avenues for future research, including the exploration of novel AI methodologies, the ethical governance of AI in moderation, and the development of more nuanced, context-aware systems. This comprehensive overview aims to catalyze further research and foster a collaborative effort towards more sophisticated, responsible, and human-centric approaches to HS moderation in the digital era. WARNING: This paper contains offensive examples.
Toxic language detection: A systematic review of Arabic datasets

The detection of toxic language in the Arabic language has emerged as an active area of research in recent years, and reviewing the existing datasets employed for training the developed solutions has become a pressing need. This paper offers a comprehensive survey of Arabic datasets focused on online toxic language. We systematically gathered a total of 54 available datasets and their corresponding papers and conducted a thorough analysis, considering 18 criteria across four primary dimensions: availability details, content, annotation process, and reusability. This analysis enabled us to identify existing gaps and make recommendations for future research works. For the convenience of the research community, the list of the analysed datasets is maintained in a GitHub repository.
Unraveling Variability and Estimating Mass Loss of Exoplanets in the Triplet Star System LTT 1445

The high-energy environment of the host stars could be deleterious for their planets. It is crucial to ascertain this contextual information to fully characterize the atmospheres of terrestrial exoplanets. We aim to fully characterize a unique triple system, LTT 1445, with three known rocky exoplanets around LTT 1445A. The X-ray irradiation and flaring of this system are studied through a new 50 ks Chandra observation, which is divided into 10 ks, 10 ks, and 30 ks segments conducted two days apart, and two months apart, respectively. This is complemented by an archival Chandra observation approximately one year earlier and repeated observations with eROSITA (extended ROentgen Survey with an Imaging Telescope Array), the soft X-ray instrument on the Spectrum-Roentgen-Gamma (SRG) mission, enabling the investigation of X-ray flux behavior across multiple time scales. The flux data acquired from these observations serve as a basis for estimating the photo-evaporation mass loss of the individual exoplanets with their host stars. To gain deeper insights into the environmental context influenced by XUV flux and to better understand the anticipated atmospheric conditions of the planets orbiting the A component, we integrate the use of the planet modeling package, VPLanet. Our findings indicate that LTT 1445C is the primary contributor to X-ray emissions, with additional input from LTT 1445B. Moreover, our study confirms that LTT1445A, recognized as a slowly-rotating star, exhibits no significant flare activity in the observed dataset. The observed results also suggest that the X-ray emissions from the LTT 1445BC components do not pose a greater threat to the planets orbiting LTT 1445A than the emissions from A itself. According to simulation results, LTT 1445Ad might have the capacity to retain its water surface.
Improving Reinforcement Learning from Human Feedback with Efficient Reward Model Ensemble

Reinforcement Learning from Human Feedback (RLHF) is a widely adopted approach for aligning large language models with human values. However, RLHF relies on a reward model that is trained with a limited amount of human preference data, which could lead to inaccurate predictions. As a result, RLHF may produce outputs that are misaligned with human values. To mitigate this issue, we contribute a reward ensemble method that allows the reward model to make more accurate predictions. As using an ensemble of large language model-based reward models can be computationally and resource-expensive, we explore efficient ensemble methods including linear-layer ensemble and LoRA-based ensemble. Empirically, we run Best-of-$n$ and Proximal Policy Optimization with our ensembled reward models, and verify that our ensemble methods help improve the alignment performance of RLHF outputs.
The effect of surface topography on benthic boundary layer flow: implications for marine larval transport and settlement

The effect of substrate topography on the settlement of coral larvae in wave-driven oscillatory flow is investigated using computational fluid dynamics coupled to a 2D agent-based simulation of individual larvae. Substrate topography modifies the boundary layer flow by generating vortices within roughness features that can be ejected into the bulk flow, directly influencing larval transport and settlement. In agreement with recent experimental findings, millimeter-scale ridged topographies were found to increase settlement compared to sub-mm feature heights. At this length scale, ridge spacing-to-height ratios of 10 to 20, spacings of more than 30 coral larval body lengths, resulted in the highest settlement rates. These optimal topographies produce a high averaged vertical velocity variance in the bulk flow, indicating that vertical larval movement to benthic surfaces is dominated by passive transport driven by recirculatory flow structures. Indeed, larval settlement was found to be positively correlated with mean vertical velocity variance, and settlement results with substrates comprising complex multiscale roughness were quantitatively similar to those with a simple rectangular model. Our findings reveal how substrates can be designed with surface features to promote larval settlement in natural flow conditions above shallow coral reefs independent of biological cues and substrate material composition.