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Statutory reasoning is the task of reasoning with facts and statutes, which are rules written in natural language by a legislature. It is a basic legal skill. In this paper we explore the capabilities of the most capable GPT-3 model, text-davinci-003, on an established statutory-reasoning dataset called SARA. We consider a variety of approaches, including dynamic few-shot prompting, chain-of-thought prompting, and zero-shot prompting. While we achieve results with GPT-3 that are better than the previous best published results, we also identify several types of clear errors it makes. We investigate why these errors happen. We discover that GPT-3 has imperfect prior knowledge of the actual U.S. statutes on which SARA is based. More importantly, we create simple synthetic statutes, which GPT-3 is guaranteed not to have seen during training. We find GPT-3 performs poorly at answering straightforward questions about these simple synthetic statutes. Comment: 10 pages

Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalman Filter, achieve excellent performance in reconstructing the prompt tracks from the collision points. However, they require dedicated configuration and additional computing time to efficiently reconstruct the large radius tracks created away from the collision points. We developed an end-to-end machine learning-based track finding algorithm for the HL-LHC, the Exa.TrkX pipeline. The pipeline is designed so as to be agnostic about global track positions. In this work, we study the performance of the Exa.TrkX pipeline for finding large radius tracks. Trained with all tracks in the event, the pipeline simultaneously reconstructs prompt tracks and large radius tracks with high efficiencies. This new capability offered by the Exa.TrkX pipeline may enable us to search for new physics in real time. Comment: 5 pages, 3 figures. Proceedings of 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research

The stability of a dark matter detector on the timescale of a few years is a key requirement due to the large exposure needed to achieve a competitive sensitivity. It is especially crucial to enable the detector to potentially detect any annual event rate modulation, an expected dark matter signature. In this work, we present the performance history of the DarkSide-50 dual-phase argon time projection chamber over its almost three-year low-radioactivity argon run. In particular, we focus on the electroluminescence signal that enables sensitivity to sub-keV energy depositions. The stability of the electroluminescence yield is found to be better than 0.5%. Finally, we show the temporal evolution of the observed event rate around the sub-keV region being consistent to the background prediction. Comment: 16 pages, 5 figures

A trainable neural equalizer based on the long short-term memory (LSTM) neural network architecture is proposed in this article to recover the channel output signal. The current widely used solution for the transmission line signal recovery is generally realized through a decision feedback equalizer (DFE) or : Feed forward equalizer (FFE) combination. The novel learning-based equalizer is suitable for highly nonlinear signal restoration, thanks to its recurrent design. The effectiveness of the LSTM equalizer (LSTME) is shown through an advance design system simulation channel signal equalization task, including a quantitative and qualitative comparison with an FFE–DFE combination. The LSTM neural network shows good equalization results compared with that of the FFE–DFE combination. The advantage of a trainable LSTME lies in its ability to learn its parameters in a flexible manner and to tackle complex scenarios without any hardware modification. This can reduce the equalizer implantation cost for variant transmission channels and bring additional portability in practical applications. International audience

In this paper, we consider the problem of a Principal aiming at designing a reward function for a population of heterogeneous agents. We construct an incentive based on the ranking of the agents, so that a competition among the latter is initiated. We place ourselves in the limit setting of mean-field type interactions and prove the existence and uniqueness of the equilibrium distribution for a given reward, for which we can find an explicit representation. Focusing first on the homogeneous setting, we characterize the optimal reward function using a convex reformulation of the problem and provide an interpretation of its behaviour. We then show that this characterization still holds for a sub-class of heterogeneous populations. For the general case, we propose a convergent numerical method which fully exploits the characterization of the mean-field equilibrium. We develop a case study related to the French market of Energy Saving Certificates based on the use of realistic data, which shows that the ranking system allows to achieve the sobriety target imposed by the European commission.

DarkSide-20k is a rare-event search experiment aimed at finding signals of dark matter particles. It is a dual-phase detector that registers ionisation and scintillation signals originating from the particles interacting with the liquid argon detector medium. It is enclosed in a single-phase liquid argon neutron veto tank, equipped with Gd-loaded panels for capturing neutrons. Since vetoing and particle identification are carried out using the light signal, it is crucial to maximise the light yield. Light collection efficiency depends on optical properties of the detector and particularly for the veto detector, which has a photosensor coverage of the order of a per cent, the reflectivity of the walls has a big impact. To quantify the amount of collected light, a comprehensive Geant4 simulation is performed, which uses optical characterisation data. In this work, a detailed description of the optics model for the veto of the experiment will be discussed. Comment: Proceedings of 17th International Conference on Topics in Astroparticle and Underground Physics (TAUP 2021)

Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks. However, their hefty computational and memory demands make them challenging to deploy to resource-constrained edge platforms with strict latency requirements. We present EdgeBERT, an in-depth algorithm-hardware co-design for latency-aware energy optimization for multi-task NLP. EdgeBERT employs entropy-based early exit predication in order to perform dynamic voltage-frequency scaling (DVFS), at a sentence granularity, for minimal energy consumption while adhering to a prescribed target latency. Computation and memory footprint overheads are further alleviated by employing a calibrated combination of adaptive attention span, selective network pruning, and floating-point quantization. Furthermore, in order to maximize the synergistic benefits of these algorithms in always-on and intermediate edge computing settings, we specialize a 12nm scalable hardware accelerator system, integrating a fast-switching low-dropout voltage regulator (LDO), an all-digital phase-locked loop (ADPLL), as well as, high-density embedded non-volatile memories (eNVMs) wherein the sparse floating-point bit encodings of the shared multi-task parameters are carefully stored. Altogether, latency-aware multi-task NLP inference acceleration on the EdgeBERT hardware system generates up to 7x, 2.5x, and 53x lower energy compared to the conventional inference without early stopping, the latency-unbounded early exit approach, and CUDA adaptations on an Nvidia Jetson Tegra X2 mobile GPU, respectively. Comment: 12 pages plus references. Paper to appear at the 54th IEEE/ACM International Symposium on Microarchitecture (MICRO 2021)