Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequence Modeling with Multiresolution Convolutional Memory

Efficiently capturing the long-range patterns in sequential data sources salient to a given task -- such as classification and generative modeling -- poses a fundamental challenge. Popular approaches in the space tradeoff between the memory burden of brute-force enumeration and comparison, as in transformers, the computational burden of complicated sequential dependencies, as in recurrent neural networks, or the parameter burden of convolutional networks with many or large filters. We instead take inspiration from wavelet-based multiresolution analysis to define a new building block for sequence modeling, which we call a MultiresLayer. The key component of our model is the multiresolution convolution, capturing multiscale trends in the input sequence. Our MultiresConv can be implemented with shared filters across a dilated causal convolution tree. Thus it garners the computational advantages of convolutional networks and the principled theoretical motivation of wavelet decompositions. Our MultiresLayer is straightforward to implement, requires significantly fewer parameters, and maintains at most a $\mathcal{O}(N\log N)$ memory footprint for a length $N$ sequence. Yet, by stacking such layers, our model yields state-of-the-art performance on a number of sequence classification and autoregressive density estimation tasks using CIFAR-10, ListOps, and PTB-XL datasets.
Physical Adversarial Attacks for Surveillance: A Survey

Modern automated surveillance techniques are heavily reliant on deep learning methods. Despite the superior performance, these learning systems are inherently vulnerable to adversarial attacks-maliciously crafted inputs that are designed to mislead, or trick, models into making incorrect predictions. An adversary can physically change their appearance by wearing adversarial t-shirts, glasses, or hats or by specific behavior, to potentially avoid various forms of detection, tracking, and recognition of surveillance systems; and obtain unauthorized access to secure properties and assets. This poses a severe threat to the security and safety of modern surveillance systems. This article reviews recent attempts and findings in learning and designing physical adversarial attacks for surveillance applications. In particular, we propose a framework to analyze physical adversarial attacks and provide a comprehensive survey of physical adversarial attacks on four key surveillance tasks: detection, identification, tracking, and action recognition under this framework. Furthermore, we review and analyze strategies to defend against physical adversarial attacks and the methods for evaluating the strengths of the defense. The insights in this article present an important step in building resilience within surveillance systems to physical adversarial attacks.
Steady-state quantum thermodynamics with synthetic negative temperatures

A bath with a negative temperature is a subject of intense debate in recent times. It raises fundamental questions not only on our understanding of negative temperature of a bath in connection with thermodynamics but also on the possibilities of constructing devices using such baths. In this work, we study steady-state quantum thermodynamics involving baths with negative temperatures. A bath with a negative temperature is created synthetically using two baths of positive temperatures and weakly coupling these with a qutrit system. These baths are then coupled to each other via a working system. At steady-state, the laws of thermodynamics are analyzed. We find that whenever the temperatures of these synthetic baths are identical, there is no heat flow, which reaffirms the zeroth law. There is always a spontaneous heat flow for different temperatures. In particular, heat flows from a bath with a negative temperature to a bath with a positive temperature which, in turn, implies that a bath with a negative temperature is `hotter' than a bath with a positive temperature. This warrants an amendment in the Kelvin-Planck statement of the second law, as suggested in earlier studies. In all these processes, the overall entropy production is positive, as required by the Clausius statement of the second law. We construct continuous heat engines operating between positive and negative temperature baths. These engines yield maximum possible heat-to-work conversion efficiency, that is, unity. We also study the thermodynamic nature of heat from a bath with a negative temperature and find that it is thermodynamic work but with negative entropy.
Properties of Non-Equilibrium Steady States for the non-linear BGK equation on the torus

We study the non-linear BGK model in 1d coupled to a spatially varying thermostat. We show existence, local uniqueness and linear stability of a steady state when the linear coupling term is large compared to the non-linear self interaction term. This model possesses a non-explicit spatially dependent non-equilibrium steady state. We are able to successfully use hypocoercivity theory in this case to prove that the linearised operator around this steady state posesses a spectral gap.
On Uni-Modal Feature Learning in Supervised Multi-Modal Learning

We abstract the features (i.e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which can only be learned from cross-modal interactions. Multi-modal models are expected to benefit from cross-modal interactions on the basis of ensuring uni-modal feature learning. However, recent supervised multi-modal late-fusion training approaches still suffer from insufficient learning of uni-modal features on each modality. We prove that this phenomenon does hurt the model's generalization ability. To this end, we propose to choose a targeted late-fusion learning method for the given supervised multi-modal task from Uni-Modal Ensemble(UME) and the proposed Uni-Modal Teacher(UMT), according to the distribution of uni-modal and paired features. We demonstrate that, under a simple guiding strategy, we can achieve comparable results to other complex late-fusion or intermediate-fusion methods on various multi-modal datasets, including VGG-Sound, Kinetics-400, UCF101, and ModelNet40.
Using planet migration and dust drift to weigh protoplanetary discs

ALMA has spatially resolved over 200 annular structures in protoplanetary discs, many of which are suggestive of the presence of planets. Constraining the mass of these putative planets is quite degenerate for it depends on the disc physical properties, and for simplicity a steady-state is often assumed whereby the planet position is kept fixed and there is a constant source of dust at the outer edge of the disc. Here, we argue against this approach by demonstrating how the planet and dust dynamics can lift degeneracies of such steady-state models. We take main disc parameters from the well-known protoplanetary disc HD 163296 with a suspected planet at R ≈ 86 au as an example. By running gas and dust hydrodynamical simulations post-processed with dust radiative transfer calculations, we first find steady-state disc and planet parameters that reproduce ALMA continuum observations fairly well. For the same disc mass, but now allowing the planet to migrate in the simulation, we find that the planet undergoes runaway migration and reaches the inner disc in ∼0.2 Myr. Further, decreasing the disc mass slows down planet migration, but it then also increases the dust’s radial drift, thereby depleting the disc dust faster. We find that the opposing constraints of planet migration and dust drift require the disc mass to be at most $0.025~{\rm {\rm M}_{\odot }}$, must less massive than previously estimated, and for the dust to be porous rather than compact. We propose that similar analysis should be extended to other sources with suspected planetary companions.
Large Global Volatility Matrix Analysis Based on Structural Information

In this paper, we develop a novel large volatility matrix estimation procedure for analyzing global financial markets. Practitioners often use lower-frequency data, such as weekly or monthly returns, to address the issue of different trading hours in the international financial market. However, this approach can lead to inefficiency due to information loss. To mitigate this problem, our proposed method, called Structured Principal Orthogonal complEment Thresholding (Structured-POET), incorporates observation structural information for both global and national factor models. We establish the asymptotic properties of the Structured-POET estimator, and also demonstrate the drawbacks of conventional covariance matrix estimation procedures when using lower-frequency data. Finally, we apply the Structured-POET estimator to an out-of-sample portfolio allocation study using international stock market data.
MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset

Sentence Boundary Detection (SBD) is one of the foundational building blocks of Natural Language Processing (NLP), with incorrectly split sentences heavily influencing the output quality of downstream tasks. It is a challenging task for algorithms, especially in the legal domain, considering the complex and different sentence structures used. In this work, we curated a diverse multilingual legal dataset consisting of over 130'000 annotated sentences in 6 languages. Our experimental results indicate that the performance of existing SBD models is subpar on multilingual legal data. We trained and tested monolingual and multilingual models based on CRF, BiLSTM-CRF, and transformers, demonstrating state-of-the-art performance. We also show that our multilingual models outperform all baselines in the zero-shot setting on a Portuguese test set. To encourage further research and development by the community, we have made our dataset, models, and code publicly available.
Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation

The ability to collect a large dataset of human preferences from text-to-image users is usually limited to companies, making such datasets inaccessible to the public. To address this issue, we create a web app that enables text-to-image users to generate images and specify their preferences. Using this web app we build Pick-a-Pic, a large, open dataset of text-to-image prompts and real users' preferences over generated images. We leverage this dataset to train a CLIP-based scoring function, PickScore, which exhibits superhuman performance on the task of predicting human preferences. Then, we test PickScore's ability to perform model evaluation and observe that it correlates better with human rankings than other automatic evaluation metrics. Therefore, we recommend using PickScore for evaluating future text-to-image generation models, and using Pick-a-Pic prompts as a more relevant dataset than MS-COCO. Finally, we demonstrate how PickScore can enhance existing text-to-image models via ranking.
Robust disturbance attenuation with stability for a class of uncertain singularly perturbed systems

In this paper, the problem of robust stability and robust disturbance attenuation is investigated for a class of singularly perturbed linear systems with norm-bounded parameter uncertainties in both state and output equations. Based on the slow and fast subsystems, a composite linear controller is designed such that both robust stability and a prescribed H infinity performance for the full-order system are achieved, irrespective of the uncertainties. Our results show that the above problem can be converted to an H infinity control problem for a related singularly perturbed linear system without parameter uncertainty. Thus, the existing results on H infinity control of singularly perturbed systems can be applied to obtain solutions to the problem of robust H infinity control for the uncertain systems, which is independent of the singular perturbation epsilon when epsilon is sufficiently small. An example is given to show the potential of the proposed technique.
From Words to Code: Harnessing Data for Program Synthesis from Natural Language

Creating programs to correctly manipulate data is a difficult task, as the underlying programming languages and APIs can be challenging to learn for many users who are not skilled programmers. Large language models (LLMs) demonstrate remarkable potential for generating code from natural language, but in the data manipulation domain, apart from the natural language (NL) description of the intended task, we also have the dataset on which the task is to be performed, or the"data context". Existing approaches have utilized data context in a limited way by simply adding relevant information from the input data into the prompts sent to the LLM. In this work, we utilize the available input data to execute the candidate programs generated by the LLMs and gather their outputs. We introduce semantic reranking, a technique to rerank the programs generated by LLMs based on three signals coming the program outputs: (a) semantic filtering and well-formedness based score tuning: do programs even generate well-formed outputs, (b) semantic interleaving: how do the outputs from different candidates compare to each other, and (c) output-based score tuning: how do the outputs compare to outputs predicted for the same task. We provide theoretical justification for semantic interleaving. We also introduce temperature mixing, where we combine samples generated by LLMs using both high and low temperatures. We extensively evaluate our approach in three domains, namely databases (SQL), data science (Pandas) and business intelligence (Excel's Power Query M) on a variety of new and existing benchmarks. We observe substantial gains across domains, with improvements of up to 45% in top-1 accuracy and 34% in top-3 accuracy.
GA Optimization Model for Time/cost Trade-off Problem in Repetitive Projects Considering Resource Continuity

Discrete time/cost trade-off problem (DTCTP) is very common in project scheduling. In repetitive projects, some activities are required to maintain resource continuity. This paper proposes an optimization model based on the genetic algorithm for DTCTP in repetitive projects considering different requirements for resource continuity of activities. First, two kinds of resource continuity are defined, namely, the constraint of work continuity and resource consistency. Impact of resource continuity on the time and cost is analyzed. Then the model including its five modules is described in detail. The model can provide a set of Pareto near optimal solutions representing the time/cost trade-off in repetitive projects. It can also deal with all kinds of situations where the resource continuity of activities is differently required. A project from the pertinent literature has demonstrated its capabilities.
A-posteriori error estimates for systems of hyperbolic conservation laws

We provide rigorous and computable a-posteriori error estimates for first order finite-volume approximations of nonlinear systems of hyperbolic conservation laws in one spatial dimension. Our estimators rely on recent stability results by Bressan, Chiri and Shen and a novel method to compute negative order norms of residuals. Numerical experiments show that the error estimator converges with the rate predicted by a-priori error estimates.
The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers

Applying language models to natural language processing tasks typically relies on the representations in the final model layer, as intermediate hidden layer representations are presumed to be less informative. In this work, we argue that due to the gradual improvement across model layers, additional information can be gleaned from the contrast between higher and lower layers during inference. Specifically, in choosing between the probable next token predictions of a generative model, the predictions of lower layers can be used to highlight which candidates are best avoided. We propose a novel approach that utilizes the contrast between layers to improve text generation outputs, and show that it mitigates degenerative behaviors of the model in open-ended generation, significantly improving the quality of generated texts. Furthermore, our results indicate that contrasting between model layers at inference time can yield substantial benefits to certain aspects of general language model capabilities, more effectively extracting knowledge during inference from a given set of model parameters.
Multi Layer Peeling for Linear Arrangement and Hierarchical Clustering

We present a new multi-layer peeling technique to cluster points in a metric space. A well-known non-parametric objective is to embed the metric space into a simpler structured metric space such as a line (i.e., Linear Arrangement) or a binary tree (i.e., Hierarchical Clustering). Points which are close in the metric space should be mapped to close points/leaves in the line/tree; similarly, points which are far in the metric space should be far in the line or on the tree. In particular we consider the Maximum Linear Arrangement problem \cite{Approximation_algorithms_for_maximum_linear_arrangement} and the Maximum Hierarchical Clustering problem \cite{Hierarchical_Clustering:_Objective_Functions_and_Algorithms} applied to metrics. We design approximation schemes ($1 - \epsilon$ approximation for any constant $\epsilon>0$) for these objectives. In particular this shows that by considering metrics one may significantly improve former approximations ($0.5$ for Max Linear Arrangement and $0.74$ for Max Hierarchical Clustering). Our main technique, which is called multi-layer peeling, consists of recursively peeling off points which are far from the"core"of the metric space. The recursion ends once the core becomes a sufficiently densely weighted metric space (i.e. the average distance is at least a constant times the diameter) or once it becomes negligible with respect to its inner contribution to the objective. Interestingly, the algorithm in the Linear Arrangement case is much more involved than that in the Hierarchical Clustering case, and uses a significantly more delicate peeling.
Questions and Concerns About Google's Quantum Supremacy Claim

In October 2019, Nature published a paper [6] describing an experimental work that was performed at Google. The paper claims to demonstrate quantum (computational) supremacy on a 53-qubit quantum computer. Since then we have been involved in a long-term project to study various statistical aspects of the Google experiment. In [30] we studied Google's statistical framework that we found to be very sound and offered some technical improvements. This document describes three main concerns (based on statistical analysis) about the Google 2019 experiment. The first concern is that the data do not agree with Google's noise model (or any other specific model). The second concern is that a crucial simple formula for a priori estimation of the fidelity seems to involve an unexpected independence assumption, and yet it gives very accurate predictions. The third concern is about statistical properties of the calibration process.
Smoothed Concordance-Assisted Learning for Optimal Treatment Decision in High Dimensional Data

Optimal treatment regime is the individualized treatment decision rule which yields the optimal treatment outcomes in expectation. A simple case of treatment decision rule is the linear decision rule, which is characterized by its coefficients and its threshold. As patients heterogeneity data accumulates, it is of interest to estimate the optimal treatment regime with a linear decision rule in high-dimensional settings. Single timepoint optimal treatment regime can be estimated using Concordance-assisted learning (CAL), which is based on pairwise comparison. CAL is flexible and achieves good results in low dimensions. However, with an indicator function inside it, CAL is difficult to optimize in high dimensions. Recently, researchers proposed a smoothing approach using a family of cumulative distribution functions to replace indicator functions. In this paper, we introduce smoothed concordance-assisted learning (SMCAL), which applies the smoothing method to CAL using a family of sigmoid functions. We then prove the convergence rates of the estimated coefficients by analyzing the approximation and stochastic errors for the cases when the covariates are continuous. We also consider discrete covariates cases, and establish similar results. Simulation studies are conducted, demonstrating the advantage of our method.
Diagonales de fractions rationnelles et équations différentielles

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Potential new species of pseudaliid lung nematode (Metastrongyloidea) from two stranded neonatal orcas (Orcinus orca) characterized by ITS‐2 and COI sequences

Abstract Knowledge about parasite species of orcas, their prevalence, and impact on the health status is scarce. Only two records of lungworm infections in orca exist from male neonatal orcas stranded in Germany and Norway. The nematodes were identified as Halocercus sp. (Pseudaliidae), which have been described in the respiratory tract of multiple odontocete species, but morphological identification to species level remained impossible due to the fragile structure and ambiguous morphological features. Pseudaliid nematodes (Metastrongyloidea) are specific to the respiratory tract of toothed whales and are hypothesized to have become almost extinct in terrestrial mammals. Severe lungworm infections can cause secondary bacterial infections and bronchopneumonia and are a common cause of mortality in odontocetes. DNA isolations and subsequent sequencing of the rDNA ITS‐2 and mtDNA COI revealed nucleotide differences between previously described Halocercus species from common dolphin (H. delphini) and harbor porpoises (H. invaginatus) that were comparatively analyzed, pointing toward a potentially new species of pseudaliid lungworm in orcas. New COI sequences of six additional metastrongyloid lungworms of seals and porpoises were derived to elucidate phylogenetic relationships and differences between nine species of Metastrongyloidea.
Retrospective benefit estimation of motorcycle ABSs based on Swiss insurance data

Abstract Objective With the growing use of motorcycle antilock braking systems (ABSs) in the European market in recent years, we would expect to see a noticeable reduction in the number of motorcycle crashes. Several studies have proved a crash-reducing effect of motorcycle ABSs. The objective of this survey is to expand former studies by analyzing Swiss insurance data to derive estimates about the effect of motorcycle ABSs in Europe. Methods Swiss insurance data are analyzed descriptively, and based on these data, regression models are estimated to determine the effect of motorcycle ABSs retrospectively. Results Motorcycles equipped with standard ABSs were associated with an 18% reduction in claim frequency compared to motorcycles without ABSs, based on Swiss liability insurance data. Conclusions These numbers are in line with the results of other studies estimating the benefit of motorcycle ABSs. This study updates and adds to previous European studies by using insurance data.