Miracle-dz/UniFAR-FTUs huggingface dataset:
Linear branching programs and directional affine extractors

A natural model of read-once linear branching programs is a branching program where queries are F2 linear forms, and along each path, the queries are linearly independent. We consider two restrictions of this model, which we call weakly and strongly read-once, both generalizing standard read-once branching programs and parity decision trees. Our main results are as follows. • Average-case complexity. We define a pseudo-random class of functions which we call directional affine extractors, and show that these functions are hard on average for the strongly read-once model. We then present an explicit construction of such function with good parameters. This strengthens the result of Cohen and Shinkar (ITCS'16) who gave such average-case hardness for parity decision trees. Directional affine extractors are stronger than the more familiar class of affine extractors. Given the significance of these functions, we expect that our new class of functions might be of independent interest. • Proof complexity. We also consider the proof system Res[⊕], which is an extension of resolution with linear queries, and define the regular variant of Res[⊕]. A refutation of a CNF in this proof system naturally defines a linear branching program solving the corresponding search problem. If a refutation is regular, we prove that the resulting program is read-once. Conversely, we show that a weakly read-once linear BP solving the search problem can be converted to a regular Res[⊕] refutation with constant blow up, where the regularity condition comes from the definition of weakly read-once BPs, thus obtaining the equivalence between these proof systems.
Adversarial methods to reduce simulation bias in neutrino interaction event filtering at liquid argon time projection chambers

For current and future neutrino oscillation experiments using large Liquid Argon Time Projection Chambers (LAr-TPCs), a key challenge is identifying neutrino interactions from the pervading cosmic-ray background. Rejection of such background is often possible using traditional cut-based selections, but this typically requires the prior use of computationally expensive reconstruction algorithms. This work demonstrates an alternative approach of using a 3D Submanifold Sparse Convolutional Network trained on low-level information from the scintillation light signal of interactions inside LAr-TPCs. This technique is applied to example simulations from ICARUS, the far detector of the Short Baseline Neutrino (SBN) program at Fermilab. The results of the network, show that cosmic background is reduced by up to 76.3% whilst neutrino interaction selection efficiency remains over 98.9%. We further present a way to mitigate potential biases from imperfect input simulations by applying Domain Adversarial Neural Networks (DANNs), for which modified simulated samples are introduced to imitate real data and a small portion of them are used for adverserial training. A series of mock-data studies are performed and demonstrate the effectiveness of using DANNs to mitigate biases, showing neutrino interaction selection efficiency performances significantly better than that achieved without the adversarial training.
The Price of Strategyproofing Peer Assessment

Strategic behavior is a fundamental problem in a variety of real-world applications that require some form of peer assessment, such as peer grading of assignments, grant proposal review, conference peer review, and peer assessment of employees. Since an individual’s own work is in competition with the submissions they are evaluating, they may provide dishonest evaluations to increase the relative standing of their own submission. This issue is typically addressed by partitioning the individuals and assigning them to evaluate the work of only those from diﬀerent subsets. Although this method ensures strategyproofness, each submission may require a diﬀerent type of expertise for eﬀective evaluation. In this paper, we focus on ﬁnding an assignment of evaluators to submissions that maximizes assigned expertise subject to the constraint of strategyproofness. We analyze the price of strategyproofness: that is, the amount of compromise on the assignment quality required in order to get strategyproofness. We establish several polynomial-time algorithms for strategyproof assignment along with assignment-quality guarantees. Finally, we evaluate the methods on a dataset from conference peer review.
Experimental observations of marginal criticality in granular materials

Significance Amorphous materials, such as grains, foams, colloids, and glasses, are ubiquitous in nature and our daily life. They can undergo glass transitions or jamming transitions to obtain rigidity either by fast quench or compression, but show subtle changes in the structures compared to the liquid states or liquid-like states. Recent progress on the first-principle replica theory unifies the glass transition and the jamming transition and points out the marginal phase with fractal free-energy landscape within the stable glass phase. Independently, marginal stability analysis predicts the relations between the exponents of the marginal phase. Here, we perform experiments with photoelastic disks and provide direct evidence of these theories in real-world amorphous materials.
Extended Placement Delivery Arrays for Multi-Antenna Coded Caching Scheme

This work addresses the multi-antenna coded caching problem where a server with <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> transmit antennas communicates to <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> users through a wireless broadcast link. In the problem setting, the server has a library of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> files, and each user is equipped with a dedicated cache of capacity <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>. A novel solution for the multi-antenna coded caching problem is obtained by designing a combinatorial structure called an extended placement delivery array (EPDA). It is shown that the placement delivery arrays known for the centralized coded caching scheme are a special class of EPDAs with <inline-formula> <tex-math notation="LaTeX">$L=1$ </tex-math></inline-formula>. Furthermore, three constructions of EPDAs are proposed for the settings: a) <inline-formula> <tex-math notation="LaTeX">$K = t+L$ </tex-math></inline-formula>, b) <inline-formula> <tex-math notation="LaTeX">$K = nt+ (n-1)L;L\geq t, n\geq 2$ </tex-math></inline-formula>, and c) <inline-formula> <tex-math notation="LaTeX">$K,L,t$ </tex-math></inline-formula> such that <inline-formula> <tex-math notation="LaTeX">$t + L\leq K$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$t = KM/N$ </tex-math></inline-formula> is an integer. The multi-antenna schemes resulting from the first two constructions achieve the optimal degrees of freedom (DoF) <inline-formula> <tex-math notation="LaTeX">$t+L$ </tex-math></inline-formula> with a subpacketization number -the number of subfiles into which a file is divided- <inline-formula> <tex-math notation="LaTeX">$K/\text {gcd}(K,t,L)$ </tex-math></inline-formula>, which is lower than the subpacketization number of the existing schemes. The scheme obtained from the third construction also achieves the optimal DoF with a subpacketization number <inline-formula> <tex-math notation="LaTeX">$\binom {K/{\gamma }}{(t+L)/{\gamma }}\left ({{t+L}}\right)/{\gamma }$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\gamma =\text {gcd}(K,t,L)$ </tex-math></inline-formula>.
Pair-Level Supervised Contrastive Learning for Natural Language Inference

Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer the relationship between the sentence pair (premise and hypothesis). Many recent works have used contrastive learning by incorporating the relationship of the sentence pair from NLI datasets to learn sentence representation. However, these methods only focus on comparisons with sentence-level representations. In this paper, we propose a Pair-level Supervised Contrastive Learning approach (PairSCL). We adopt a cross attention module to learn the joint representations of the sentence pairs. A contrastive learning objective is designed to distinguish the varied classes of sentence pairs by pulling those in one class together and pushing apart the pairs in other classes. We evaluate PairSCL on two public datasets of NLI where the accuracy of PairSCL outperforms other methods by 2.1% on average. Furthermore, our method outperforms the previous state-of-the-art method on seven transfer tasks of text classification.1
Social Learning under Randomized Collaborations

We study a social learning scheme where at every time instant, each agent chooses to receive information from one of its neighbors at random. We show that under this sparser communication scheme, the agents learn the truth eventually and the asymptotic convergence rate remains the same as the standard algorithms, which use more communication resources. We also derive large deviation estimates of the log-belief ratios for a special case where each agent replaces its belief with that of the chosen neighbor.
Promises and Challenges of Causality for Ethical Machine Learning

Recent years have witnessed an increasing interest in causal reasoning for designing fair decision-making systems due to its compatibility with legal frameworks, interpretability for human stakeholders, and robustness to spurious correlations, among other factors. The recent attention, however, has been accompanied with great skepticism due to practical and epistemological challenges with applying existing causal fairness approaches. Motivated by the long-standing empirical work on causality in econometrics, social sciences, and biomedi- cal sciences, in this paper we lay out the conditions for appropriate application of causal fairness under the “potential outcomes framework. ” To this end, we propose a shift from postulating interventions on immutable social categories to their perceptions and highlight two key aspects of interven- tions that are largely overlooked in the causal fairness literature: timing and nature of manipulations. We argue that such conceptualization is key in evaluating the validity of causal assumptions and conducting sound causal analysis includ- ing avoiding post-treatment bias. Further, we illustrate how causality can address the limitations of existing fairness met- rics that depend upon statistical correlations. Speciﬁcally, we introduce causal variants of common statistical fairness no- tions, and we make a novel observation that under the causal framework there is no fundamental disagreement between different fairness criteria. Finally, extensive experiments on synthetic and real-world datasets including a case study on police stop and search decisions demonstrate the efﬁcacy of our framework in evaluating and mitigating unfairness.
Impact of unphysical meson decays on the parameter fixing in the three-flavour Nambu-Jona-Lasinio model for different regularisation methods

We investigate the influence of the allowed unphysical meson decay into a quark-antiquark pair of the three flavour Nambu–Jona-Lasinio (NJL) model on its parameter fixing. This decay manifests in a non-vanishing imaginary part of the corresponding meson propagator and its polarisation loop, respectively. In order to handle the emerging divergent integrals, we focus on the Pauli-Villars, threeand four momentum cutoff regularisation method. Here, we fix the parameters to certain observables, which are effected by the non-vanishing imaginary part. The resulting parameter-sets for each regularisation scheme are compared to each other with and without taking the imaginary part of the polarisation loop into account.
DONE: Distributed Approximate Newton-type Method for Federated Edge Learning

There is growing interest in applying distributed machine learning to edge computing, forming federated edge learning. Federated edge learning faces non-i.i. d. and heterogeneous data, and the communication between edge workers, possibly through distant locations and with unstable wireless networks, is more costly than their local computational overhead. In this work, we propose DONE, a distributed approximate Newtontype algorithm with a fast convergence rate for communication efficient federated edge learning. First, with strongly convex and smooth loss functions, DONE approximates the Newton direction in a distributed manner using the classical Richardson iteration on each edge worker. Second, we prove that DONE has linear-quadratic convergence and analyze its communication complexities. Finally, the experimental results with non-i.i. d. and heterogeneous data show that DONE attains a comparable performance to Newtons method. Notably, DONE requires fewer communication iterations compared to distributed gradient descent and outperforms DANE, FEDL, and GIANT, state-of-the-art approaches, in the case of non-quadratic loss functions.
Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning

Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general, however, the clustering performance of ART-based algorithms strongly depends on the specification of a similarity threshold, i.e., a vigilance parameter, which is data-dependent and specified by hand. This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from the distribution of data points. In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm. Experimental results demonstrate that the proposed algorithm has high clustering performance comparable with recently-proposed state-of-the-art hierarchical clustering algorithms.
Urban Landscape from the Structure of Road Network: A Complexity Perspective

Spatial road networks have been widely employed to model the structure and connectivity of cities. In such representation, the question of spatial scale of the entities in the network, i.e. what its nodes and edges actually embody in reality, is of particular importance so that redundant information can be identified and eliminated to provide an improved understanding of city structure. To address this, we investigate in this work the relationship between the spatial scale of the modelled network entities against the amount of useful information contained within it. We employ an entropy measure from complexity science and information theory to quantify the amount of information residing in each presentation of the network subject to the spatial scale and show that it peaks at some intermediate scale. The resulting network presentation would allow us to have direct intuition over the hierarchical structure of the urban organisation, which is otherwise not immediately available from the traditional simple road network presentation. We demonstrate our methodology on the Singapore road network and find the critical spatial scale to be 85 m, at which the network obtained corresponds very well to the planning boundaries used by the local urban planners, revealing the essential urban connectivity structure of the city. Furthermore, the complexity measure is also capable of informing the secondary transitions that correspond well to higher-level hierarchical structures associated with larger-scale urban planning boundaries in Singapore.
Learnable Wavelet Packet Transform for Data-Adapted Spectrograms

Capturing high-frequency data concerning the condition of complex systems, e.g. by acoustic monitoring, has become increasingly prevalent. Such high-frequency signals typically contain time dependencies ranging over different time scales and different types of cyclic behaviors. Processing such signals requires careful feature engineering, particularly the extraction of meaningful time-frequency features. This can be time-consuming and the performance is often dependent on the choice of parameters. To address these limitations, we propose a deep learning framework for learnable wavelet packet transforms, enabling to learn features automatically from data and optimise them with respect to the defined objective function. The learned features can be represented as a spectrogram, containing the important time-frequency information of the dataset. We evaluate the properties and performance of the proposed approach by evaluating its improved spectral leakage and by applying it to an anomaly detection task for acoustic monitoring.
Cold and Hot gas distribution around the Milky-Way-M31 system in the HESTIA simulations

Recent observations have revealed remarkable insights into the gas reservoir in the circumgalactic medium (CGM) of galaxy haloes. In this paper, we characterise the gas in the vicinity of Milky Way and Andromeda analogues in the hestia (High resolution Environmental Simulations of The Immediate Area) suite of constrained Local Group (LG) simulations. The hestia suite comprise of a set of three high-resolution arepo-based simulations of the LG, run using the Auriga galaxy formation model. For this paper, we focus only on the z = 0 simulation datasets and generate mock skymaps along with a power spectrum analysis to show that the distributions of ions tracing low-temperature gas (H i and Si iii) are more clumpy in comparison to warmer gas tracers (Ovi, Ovii and Oviii). We compare to the spectroscopic CGM observations of M31 and low-redshift galaxies. hestia under-produces the column densities of the M31 observations, but the simulations are consistent with the observations of low-redshift galaxies. A possible explanation for these findings is that the spectroscopic observations of M31 are contaminated by gas residing in the CGM of the Milky Way.
Boosting 3D Adversarial Attacks with Attacking On Frequency

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial attacks in the image domain. Recently, 3D adversarial attacks, especially adversarial attacks on point clouds, have elicited mounting interest. However, adversarial point clouds obtained by previous methods show weak transferability and are easy to defend. To address these problems, in this paper we propose a strong point cloud attack method named AOF which pays more attention to the low-frequency component of point clouds. We combine the losses from point cloud and its low-frequency component to craft adversarial samples and focus on the low-frequency component of point cloud in the process of optimization. Extensive experiments validate that AOF can improve the transferability significantly compared to state-of-the-art (SOTA) attacks, and is more robust to state-of-the-art 3D defense methods. Otherwise, compared to adversarial point clouds generated by other adversarial attack methods, adversarial point clouds obtained by AOF contain more deformation than outlier.
Generalization Error Bounds on Deep Learning with Markov Datasets

In this paper, we derive upper bounds on generalization errors for deep neural networks with Markov datasets. These bounds are developed based on Koltchinskii and Panchenko's approach for bounding the generalization error of combined classifiers with i.i. d. datasets. The development of new symmetrization inequalities in high-dimensional probability for Markov chains is a key element in our extension, where the spectral gap of the infinitesimal generator of the Markov chain plays a key parameter in these inequalities. We also propose a simple method to convert these bounds and other similar ones in traditional deep learning and machine learning to Bayesian counterparts for both i.i. d. and Markov datasets. Extensions to $m$-order homogeneous Markov chains such as AR and ARMA models and mixtures of several Markov data services are given.
Human brain functional MRS reveals interplay of metabolites implicated in neurotransmission and neuroenergetics

While functional MRI (fMRI) localizes brain activation and deactivation, functional MRS (fMRS) provides insights into the underlying metabolic conditions. There is much interest in measuring task-induced and resting levels of metabolites implicated in neuroenergetics (e.g., lactate, glucose, or β-hydroxybutyrate (BHB)) and neurotransmission (e.g., γ-aminobutyric acid (GABA) or pooled glutamate and glutamine (Glx)). Ultra-high magnetic field (e.g., 7T) has boosted the fMRS quantification precision, reliability, and stability of spectroscopic observations using short echo-time (TE) 1H-MRS techniques. While short TE 1H-MRS lacks sensitivity and specificity for fMRS at lower magnetic fields (e.g., 3T or 4T), most of these metabolites can also be detected by J-difference editing (JDE) 1H-MRS with longer TE to filter overlapping resonances. The 1H-MRS studies show that JDE can detect GABA, Glx, lactate, and BHB at 3T, 4T and 7T. Most recently, it has also been demonstrated that JDE 1H-MRS is capable of reliable detection of metabolic changes in different brain areas at various magnetic fields. Combining fMRS measurements with fMRI is important for understanding normal brain function, but also clinically relevant for mechanisms and/or biomarkers of neurological and neuropsychiatric disorders. We provide an up-to-date overview of fMRS research in the last three decades, both in terms of applications and technological advances. Overall the emerging fMRS techniques can be expected to contribute substantially to our understanding of metabolism for brain function and dysfunction.
Use of ozonation technology to combat viruses and bacteria in aquatic environments: problems and application perspectives for SARS-CoV-2

ABSTRACT COVID-19 is a global health threat with a large number of confirmed cases and deaths worldwide. Person-to-person transmission through respiratory droplets and contact with aerosol-infected surfaces are the main ways in which the virus spreads. However, according to the updated literature, the new coronavirus (SARS-CoV-2) has also been detected in aqueous matrices, with the main route of transmission being feces and masks from patients diagnosed with the disease. Given the emergence of public health and environmental protection from the presence of lethal viruses and bacteria, this review article aims to report the major challenges associated with the application of ozonation in water contaminated with viruses and bacteria, in order to clarify whether these communities can survive or infect after the disinfection process and if it is efficient. Available data suggest that ozonation is able to increase the inactivation effect of microorganisms by about 50% in the logarithmic range, reducing infectivity. In addition, the evidence-based knowledge reported in this article is useful to support water and sanitation safety planning and to protect human health from exposure to cited contaminants through water. GRAPHICAL ABSTRACT