Miracle-dz/UniFAR-FTUs huggingface dataset:
CiteCaseLAW: Citation Worthiness Detection in Caselaw for Legal Assistive Writing

In legal document writing, one of the key elements is properly citing the case laws and other sources to substantiate claims and arguments. Understanding the legal domain and identifying appropriate citation context or cite-worthy sentences are challenging tasks that demand expensive manual annotation. The presence of jargon, language semantics, and high domain specificity makes legal language complex, making any associated legal task hard for automation. The current work focuses on the problem of citation-worthiness identification. It is designed as the initial step in today's citation recommendation systems to lighten the burden of extracting an adequate set of citation contexts. To accomplish this, we introduce a labeled dataset of 178M sentences for citation-worthiness detection in the legal domain from the Caselaw Access Project (CAP). The performance of various deep learning models was examined on this novel dataset. The domain-specific pre-trained model tends to outperform other models, with an 88% F1-score for the citation-worthiness detection task.
An Extensive Literature Review on Reversible Logic Gates

Reversible logic is promising as it is able to compute with various applications in very low power like nano- computing for example quantum computing. Reversible circuits are like conventional circuits despite they are build from reversible gates. Reversible circuits, have single, one-to-one mapping between the input and output vectors.Thus all output vectors are permutations of input vectors. A concise review of reversible logic gates basics will be studied. The basic reversible logic gates need to be optimized in reversible logic design and synthesis. Reversible gates need steady inputs for configuration of gate functions and junk outputs that helps in keeping reversibility. Therefore, it is very important to lessen the parameters such as junk bytes, quantum cost and delay in the scheming of reversible circuits. As reversible circuits have tremendous applications in a vairety of emerging technologies such as quantum computing and quantum dot. Consequently this research work would also cover the promising nanotechnologies.
Sign-Coded Exposure Sensing for Noise-Robust High-Speed Imaging

We present a novel Fourier camera, an in-hardware optical compression of high-speed frames employing pixel-level sign-coded exposure where pixel intensities temporally modulated as positive and negative exposure are combined to yield Hadamard coefficients. The orthogonality of Walsh functions ensures that the noise is not amplified during high-speed frame reconstruction, making it a much more attractive option for coded exposure systems aimed at very high frame rate operation. Frame reconstruction is carried out by a single-pass demosaicking of the spatially multiplexed Walsh functions in a lattice arrangement, significantly reducing the computational complexity. The simulation prototype confirms the improved robustness to noise compared to the binary-coded exposure patterns, such as one-hot encoding and pseudo-random encoding. Our hardware prototype demonstrated the reconstruction of 4 kHz frames of a moving scene lit by ambient light only.
Multimodal Understanding Through Correlation Maximization and Minimization

Multimodal learning has mainly focused on learning large models on, and fusing feature representations from, different modalities for better performances on downstream tasks. In this work, we take a detour from this trend and study the intrinsic nature of multimodal data by asking the following questions: 1) Can we learn more structured latent representations of general multimodal data? ; and 2) can we intuitively understand, both mathematically and visually, what the latent representations capture? To answer 1), we propose a general and lightweight framework, Multimodal Understanding Through Correlation Maximization and Minimization (MUCMM), that can be incorporated into any large pre-trained network. MUCMM learns both the common and individual representations. The common representations capture what is common between the modalities; the individual representations capture the unique aspect of the modalities. To answer 2), we propose novel scores that summarize the learned common and individual structures and visualize the score gradients with respect to the input, visually discerning what the different representations capture. We further provide mathematical intuitions of the computed gradients in a linear setting, and demonstrate the effectiveness of our approach through a variety of experiments.
Games Under Network Uncertainty

We consider an incomplete information network game in which agents' information is restricted only to the identity of their immediate neighbors. Agents form beliefs about the adjacency pattern of others and play a linear-quadratic effort game to maximize interim payoffs. We establish the existence and uniqueness of Bayesian-Nash equilibria in pure strategies. In this equilibrium agents use local information, i.e., knowledge of their direct connections to make inferences about the complementarity strength of their actions with those of other agents which is given by their updated beliefs regarding the number of walks they have in the network. Our model clearly demonstrates how asymmetric information based on network position and the identity of agents affect strategic behavior in such network games. We also characterize agent behavior in equilibria under different forms of ex-ante prior beliefs such as uniform priors over the set of all networks, Erdos-Renyi network generation, and homophilic linkage.
Causal Discovery with Stage Variables for Health Time Series

Using observational data to learn causal relationships is essential when randomized experiments are not possible, such as in healthcare. Discovering causal relationships in time-series health data is even more challenging when relationships change over the course of a disease, such as medications that are most effective early on or for individuals with severe disease. Stage variables such as weeks of pregnancy, disease stages, or biomarkers like HbA1c, can influence what causal relationships are true for a patient. However, causal inference within each stage is often not possible due to limited amounts of data, and combining all data risks incorrect or missed inferences. To address this, we propose Causal Discovery with Stage Variables (CDSV), which uses stage variables to reweight data from multiple time-series while accounting for different causal relationships in each stage. In simulated data, CDSV discovers more causes with fewer false discoveries compared to baselines, in eICU it has a lower FDR than baselines, and in MIMIC-III it discovers more clinically relevant causes of high blood pressure.
Chain-of-Skills: A Configurable Model for Open-Domain Question Answering

The retrieval model is an indispensable component for real-world knowledge-intensive tasks, e.g., open-domain question answering (ODQA). As separate retrieval skills are annotated for different datasets, recent work focuses on customized methods, limiting the model transfer- ability and scalability. In this work, we propose a modular retriever where individual modules correspond to key skills that can be reused across datasets. Our approach supports flexible skill configurations based on the target domain to boost performance. To mitigate task interference, we design a novel modularization parameterization inspired by sparse Transformer. We demonstrate that our model can benefit from self-supervised pretraining on Wikipedia and fine-tuning using multiple ODQA datasets, both in a multi-task fashion. Our approach outperforms recent self-supervised retrievers in zero-shot evaluations and achieves state-of-the-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA.
V618 Sgr: Galactic eclipsing symbiotic nova detected in repeated outbursts

V618 Sgr was previously classified as an R CrB-type variable and later as a possible symbiotic star. Our study aims to analyse the nature of this target, which is currently undergoing significant brightening in properties similar to those of known symbiotic novae. We analyse literature information, photometric observations, and 35 new optical spectra. Our findings strongly suggest that V618 Sgr is an eclipsing symbiotic nova currently in outburst. Additionally, since the star has demonstrated at least two similar brightenings in the past, we propose that V618 Sgr could be the first known galactic symbiotic nova observed in repeated outbursts of this type and may host a relatively massive white dwarf.
Homomorphisms between graphs embedded on surfaces

We extend the notion of graph homomorphism to cellularly embedded graphs (maps) by designing operations on vertices and edges that respect the surface topology; we thus obtain the first definition of map homomorphism that preserves both the combinatorial structure (as a graph homomorphism) and the topological structure of the surface (in particular, orientability and genus). Notions such as the core of a graph and the homomorphism order on cores are then extended to maps. We also develop a purely combinatorial framework for various topological features of a map such as the contractibility of closed walks, which in particular allows us to characterize map cores. We then show that the poset of map cores ordered by the existence of a homomorphism is connected and, in contrast to graph homomorphisms, does not contain any dense interval (so it is not universal for countable posets). Finally, we give examples of a pair of cores with an infinite number of cores between them, an infinite chain of gaps, and arbitrarily large antichains with a common homomorphic image.
High-level context representation for emotion recognition in images

Emotion recognition is the task of classifying perceived emotions in people. Previous works have utilized various nonverbal cues to extract features from images and correlate them to emotions. Of these cues, situational context is particularly crucial in emotion perception since it can directly influence the emotion of a person. In this paper, we propose an approach for high-level context representation extraction from images. The model relies on a single cue and a single encoding stream to correlate this representation with emotions. Our model competes with the state-of-the-art, achieving an mAP of 0.3002 on the EMOTIC dataset while also being capable of execution on consumer-grade hardware at ≈ 90 frames per second. Overall, our approach is more efficient than previous models and can be easily deployed to address real-world problems related to emotion recognition.
CAMEL: Co-Designing AI Models and Embedded DRAMs for Efficient On-Device Learning

—The emergence of the Internet of Things (IoT) has resulted in a remarkable amount of data generated on edge devices, which are often processed using AI algorithms. On-device learning enables edge platforms to continually adapt the AI models to user personal data and further allows for a better service quality. However, AI training on resource-limited devices is extremely difﬁcult because of the intensive computing workload and the signiﬁcant amount of on-chip memory consumption exacted by deep neural networks (DNNs). To mitigate this, we pro-pose to use embedded dynamic random-access memory (eDRAM) as the main storage medium of training data. Compared with static random-access memory (SRAM), eDRAM introduces more than 2 × improvement on storage density, enabling reduced off-chip memory trafﬁc. However, to keep the stored data intact, eDRAM is required to perform the power-hungry data refresh operations. eDRAM refresh can be eliminated if the data is stored for a period of time that is shorter than the eDRAM retention time. To achieve this, we design a novel reversible DNN architecture that enables a signiﬁcantly reduced data lifetime during the training process and removes the need for eDRAM refresh. We further design an efﬁcient on-device training engine, termed CAMEL , that uses eDRAM as the main on-chip memory. CAMEL enables the intermediate results during training to ﬁt fully in on-chip eDRAM arrays and completely eliminates the off-chip DRAM trafﬁc during the training process. We evaluate our CAMEL system on multiple DNNs with different datasets, demonstrating a more than 3 × saving on total DNN training energy consumption than the other baselines, while achieving a similar (even better) performance in validation accuracy.
Debiased inference for dynamic nonlinear models with two-way fixed effects

Panel data models often use fixed effects to account for unobserved heterogeneities. These fixed effects are typically incidental parameters and their estimators converge slowly relative to the square root of the sample size. In the maximum likelihood context, this induces an asymptotic bias of the likelihood function. Test statistics derived from the asymptotically biased likelihood, therefore, no longer follow their standard limiting distributions. This causes severe distortions in test sizes. We consider a generic class of dynamic nonlinear models with two-way fixed effects and propose an analytical bias correction method for the likelihood function. We formally show that the likelihood ratio, the Lagrange-multiplier, and the Wald test statistics derived from the corrected likelihood follow their standard asymptotic distributions. A bias-corrected estimator of the structural parameters can also be derived from the corrected likelihood function. We evaluate the performance of our bias correction procedure through simulations and an empirical example.
Fine-Grained Product Classification on Leaflet Advertisements

In this paper, we describe a first publicly available fine-grained product recognition dataset based on leaflet images. Using advertisement leaflets, collected over several years from different European retailers, we provide a total of 41.6k manually annotated product images in 832 classes. Further, we investigate three different approaches for this fine-grained product classification task, Classification by Image, by Text, as well as by Image and Text. The approach"Classification by Text"uses the text extracted directly from the leaflet product images. We show, that the combination of image and text as input improves the classification of visual difficult to distinguish products. The final model leads to an accuracy of 96.4% with a Top-3 score of 99.2%. We release our code at https://github.com/ladwigd/Leaflet-Product-Classification.
Model-free Reinforcement Learning of Semantic Communication by Stochastic Policy Gradient

Following the recent success of Machine Learning tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon's classic design paradigm by aiming to transmit the meaning, i.e., semantics, of a message instead of its exact version, allowing for information rate savings. In this work, we apply the Stochastic Policy Gradient (SPG) to design a semantic communication system by reinforcement learning, separating transmitter and receiver, and not requiring a known or differentiable channel model -- a crucial step towards deployment in practice. Further, we derive the use of SPG for both classic and semantic communication from the maximization of the mutual information between received and target variables. Numerical results show that our approach achieves comparable performance to a model-aware approach based on the reparametrization trick, albeit with a decreased convergence rate.
Testing and Learning Convex Sets in the Ternary Hypercube

We study the problems of testing and learning high-dimensional discrete convex sets. The simplest high-dimensional discrete domain where convexity is a non-trivial property is the ternary hypercube, $\{-1,0,1\}^n$. The goal of this work is to understand structural combinatorial properties of convex sets in this domain and to determine the complexity of the testing and learning problems. We obtain the following results. Structural: We prove nearly tight bounds on the edge boundary of convex sets in $\{0,\pm 1\}^n$, showing that the maximum edge boundary of a convex set is $\widetilde \Theta(n^{3/4}) \cdot 3^n$, or equivalently that every convex set has influence $\widetilde{O}(n^{3/4})$ and a convex set exists with influence $\Omega(n^{3/4})$. Learning and sample-based testing: We prove upper and lower bounds of $3^{\widetilde{O}(n^{3/4})}$ and $3^{\Omega(\sqrt{n})}$ for the task of learning convex sets under the uniform distribution from random examples. The analysis of the learning algorithm relies on our upper bound on the influence. Both the upper and lower bound also hold for the problem of sample-based testing with two-sided error. For sample-based testing with one-sided error we show that the sample-complexity is $3^{\Theta(n)}$. Testing with queries: We prove nearly matching upper and lower bounds of $3^{\widetilde{\Theta}(\sqrt{n})}$ for one-sided error testing of convex sets with non-adaptive queries.
A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels

In the last years, several machine learning-based techniques have been proposed to monitor human movements from Wi-Fi channel readings. However, the development of domain-adaptive algorithms that robustly work across different environments is still an open problem, whose solution requires large datasets characterized by strong domain diversity, in terms of environments, persons and Wi-Fi hardware. To date, the few public datasets available are mostly obsolete - as obtained via Wi-Fi devices operating on 20 or 40 MHz bands - and contain little or no domain diversity, thus dramatically limiting the advancements in the design of sensing algorithms. The present contribution aims to fill this gap by providing a dataset of IEEE 802.11 ac channel measurements over an 80 MHz bandwidth channel featuring notable domain diversity, through measurement campaigns that involved thirteen subjects across different environments, days, and with different hardware. Novel experimental data is provided by blocking the direct path between the transmitter and the monitor, and collecting measurements in a semi-anechoic chamber (no multi-path fading). Overall, the dataset - available on IEEE DataPort [1] - contains more than thirteen hours of channel state information readings (23.6 GB), allowing researchers to test activity/identity recognition and people counting algorithms.
Uniform convergence of Dyson Ferrari-Spohn diffusions to the Airy line ensemble

We consider the Dyson Ferrari-Spohn diffusion $\mathcal{X}^N = (\mathcal{X}^N_1,\dots,\mathcal{X}^N_N)$, consisting of $N$ non-intersecting Ferrari-Spohn diffusions $\mathcal{X}^N_1>\cdots>\mathcal{X}^N_N>0$ on $\mathbb{R}$. This object was introduced by Ioffe, Velenik, and Wachtel (2018) as a scaling limit for line ensembles of $N$ non-intersecting random walks above a hard wall with area tilts, which model certain three-dimensional interfaces in statistical physics. It was shown by Ferrari and Shlosman (2023) that as $N\to\infty$, after a spatial shift of order $N^{2/3}$ and constant rescaling in time, the top curve $\mathcal{X}^N_1$ converges to the $\mathrm{Airy}_2$ process in the sense of finite-dimensional distributions. We extend this result by showing that the full ensemble $\mathcal{X}^N$ converges with the same shift and time scaling to the Airy line ensemble in the topology of uniform convergence on compact sets. In our argument we formulate a Brownian Gibbs property with area tilts for $\mathcal{X}^N$, which we show is equivalent after a global parabolic shift to the usual Brownian Gibbs property introduced by Corwin and Hammond (2014).
5D tomographic phase-space reconstruction of particle bunches

We propose a new beam diagnostics method to reconstruct the phase space of charged particle bunches in 5 dimensions, which consist of the horizontal and vertical positions and divergences as well as the time axis. This is achieved by combining a quadrupole-based transverse phase-space tomography with the adjustable streaking angle of a polarizable X-band transverse deflection structure (PolariX TDS). We demonstrate with detailed simulations that the method is able to reconstruct various complex phase-space distributions and that the quality of the reconstruction depends on the number of input projections. This method allows for the identification and visualization of previously unnoticed detailed features in the phase-space distribution, and can thereby be used as a tool towards improving the performance of particle accelerators, or performing more accurate simulation studies.
Composite Motion Learning with Task Control

We present a deep learning method for composite and task-driven motion control for physically simulated characters. In contrast to existing data-driven approaches using reinforcement learning that imitate full-body motions, we learn decoupled motions for specific body parts from multiple reference motions simultaneously and directly by leveraging the use of multiple discriminators in a GAN-like setup. In this process, there is no need of any manual work to produce composite reference motions for learning. Instead, the control policy explores by itself how the composite motions can be combined automatically. We further account for multiple task-specific rewards and train a single, multi-objective control policy. To this end, we propose a novel framework for multi-objective learning that adaptively balances the learning of disparate motions from multiple sources and multiple goal-directed control objectives. In addition, as composite motions are typically augmentations of simpler behaviors, we introduce a sample-efficient method for training composite control policies in an incremental manner, where we reuse a pre-trained policy as the meta policy and train a cooperative policy that adapts the meta one for new composite tasks. We show the applicability of our approach on a variety of challenging multi-objective tasks involving both composite motion imitation and multiple goal-directed control. Code is available at https://motion-lab.github. io/CompositeMotion.
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question

Hybrid question answering (HybridQA) over the financial report contains both textual and tabular data, and requires the model to select the appropriate evidence for the numerical reasoning task. Existing methods based on encoder-decoder framework employ a expression tree-based decoder to solve numerical reasoning problems. However, encoders rely more on Machine Reading Comprehension (MRC) methods, which take table serialization and text splicing as input, damaging the granularity relationship between table and text as well as the spatial structure information of table itself. In order to solve these problems, the paper proposes a Multi-View Graph (MVG) Encoder to take the relations among the granularity into account and capture the relations from multiple view. By utilizing MVGE as a module, we constuct Tabular View, Relation View and Numerical View which aim to retain the original characteristics of the hybrid data. We validate our model on the publicly available table-text hybrid QA benchmark (TAT-QA) and outperform the state-of-the-art model.