Miracle-dz/UniFAR-FTUs huggingface dataset:
Tensor-Based Modulation for Unsourced Massive Random Access

We introduce a modulation for unsourced massive random access whereby the transmitted symbols are rank-1 tensors constructed from Grassmannian sub-constellations. The use of a low-rank tensor structure, together with tensor decomposition in order to separate the users at the receiver, allows a convenient uncoupling between multi-user separation and single-user demapping. The proposed signaling scheme is designed for the block fading channel and multiple-antenna settings, and is shown to perform well in comparison to state-of-the-art approaches.
Generation of polarized particle beams at relativistic laser intensities

Abstract The acceleration of polarized electrons, positrons, protons and ions in strong laser and plasma fields is a very attractive option for obtaining polarized beams in the multi-mega-electron volt range. Recently, there has been substantial progress in the understanding of the dominant mechanisms leading to high degrees of polarization, in the numerical modeling of these processes and in their experimental implementation. This review paper presents an overview on the current state of the field, and on the concepts of polarized laser–plasma accelerators and of beam polarimetry.
SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)

We present the results and the main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval-2020). The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish. OffensEval-2020 was one of the most popular tasks at SemEval-2020, attracting a large number of participants across all subtasks and languages: a total of 528 teams signed up to participate in the task, 145 teams submitted official runs on the test data, and 70 teams submitted system description papers.
Kernel Distributionally Robust Optimization

This paper is an in-depth investigation of using kernel methods to immunize optimization solutions against distributional ambiguity. We propose kernel distributionally robust optimization (K-DRO) using insights from the robust optimization theory and functional analysis. Our method uses reproducing kernel Hilbert spaces (RKHS) to construct ambiguity sets. It can be reformulated as a tractable program by using the conic duality of moment problems and an extension of the RKHS representer theorem. Our insights reveal that universal RKHSs are large enough for K-DRO to be effective. This paper provides both theoretical analyses that extend the robustness properties of kernel methods, as well as practical algorithms that can be applied to general optimization problems, not limited to kernelized models.
Can you take Akemann--Weaver's $\diamondsuit$ away?

A counterexample to Naimark's problem can be constructed without using Jensen's diamond principle. We also construct, using our weakening of diamond, a separably represented, simple $\textrm{C}^*$-algebra with exactly $m$ inequivalent irreducible representations for all $m\geq 2$. Our principal technical contribution is the introduction of a forcing notion that generically adds an automorphism of a given $\textrm{C}^*$-algebra with a prescribed action on its space of pure states.
First coherent structure in elasto-inertial turbulence

Two dimensional channel flow simulations of FENE-P fluid in the elasto-inertial turbulence regime reveal distinct regimes ranging from chaos to a steady travelling wave which takes the form of an arrowhead structure. This coherent structure provides new insights in the polymer/flow interactions driving EIT, which are observed in a set of controlled numerical experiments and the study of transfer between elastic and turbulent kinetic energy.
Marvel: A Data-centric Compiler for DNN Operators on Spatial Accelerators

The efficiency of a spatial DNN accelerator depends heavily on the compiler and its cost model ability to generate optimized mappings for various operators of DNN models on to the accelerator's compute and memory resources. But, existing cost models lack a formal boundary over the operators for precise and tractable analysis, which poses adaptability challenges for new DNN operators. To address this challenge, we leverage the recently introduced Maestro Data-Centric (MDC) notation. We develop a formal understanding of DNN operators whose mappings can be described in the MDC notation, because any mapping adhering to the notation is always analyzable by the MDC's cost model. Furthermore, we introduce a transformation for translating mappings into the MDC notation for exploring the mapping space. Searching for the optimal mappings is challenging because of the large space of mappings, and this challenge gets exacerbated with new operators and diverse accelerator this http URL address this challenge, we propose a decoupled off-chip/on-chip approach that decomposes the mapping space into off-chip and on-chip subspaces, and first optimizes the off-chip subspace followed by the on-chip subspace. The motivation for this decomposition is to reduce the size of the search space dramatically and also to prioritize the optimization of off-chip data movement, which is 2-3 orders of magnitude more compared to the on-chip data movement. We implemented our approach in a tool called {\em Marvel}, and another major benefit of our approach is that it is applicable to any DNN operator conformable with the MDC notation.
A BIOPSY POLE SYSTEM FOR BOW‐RIDING DOLPHINS: SAMPLING SUCCESS, BEHAVIORAL RESPONSES, AND TEST FOR SAMPLING BIAS

The collection of biopsy samples from free-ranging cetaceans has proven useful for addressing questions regarding population and social structure (e.g., Baker et al. 1990), evolutionary relationships (e.g., LeDuc et al. 1999), feeding ecology (e.g., Walker et al. 1999), and contaminant levels (e.g., Fossi et al. 2000). In the past, modified crossbows and rifles have been used to sample both large and small cetaceans (e.g., Weinrich et al. 1991, Barrett-Lennard et al. 1996, Krutzen et al. 2002). These systems have been shown to elicit only short-term behavioral responses by sampled animals, and no physiological complications have been reported during wound healing (e.g., Weller et al. 1997, Krutzen et al. 2002). The International Whaling Commission has deemed these methods acceptable because there is no evidence of long-term detrimental effects to sampled individuals or populations (International Whaling Commission 1991). However, such techniques are not without risk. For example, the use of a crossbow has led to the death of a common dolphin (Delphinus delphis) in the central Mediterranean Sea (Bearzi 2000). Less invasive techniques to obtain tissue samples from free-ranging small cetaceans are desirable, and other methods developed for this purpose include skin swabbing (Harlin et al. 1999) and fecal sampling (Parsons et al. 1999). When selecting a sampling technique, the conservation status of the species and target population, as well as the potential behavioral response of the animals to sampling, should be considered. It is also important to assess if the research question can be answered with the amount of tissue obtained with a specific technique.
Implicit bias of gradient descent for mean squared error regression with wide neural networks

We investigate gradient descent training of wide neural networks and the corresponding implicit bias in function space. Focusing on 1D regression, we show that the solution of training a width-$n$ shallow ReLU network is within $n^{- 1/2}$ of the function which fits the training data and whose difference from initialization has smallest 2-norm of the second derivative weighted by $1/\zeta$. The curvature penalty function $1/\zeta$ is expressed in terms of the probability distribution that is utilized to initialize the network parameters, and we compute it explicitly for various common initialization procedures. For instance, asymmetric initialization with a uniform distribution yields a constant curvature penalty, and thence the solution function is the natural cubic spline interpolation of the training data. The statement generalizes to the training trajectories, which in turn are captured by trajectories of spatially adaptive smoothing splines with decreasing regularization strength.
Molecular Gas in 21 and 30 μm Sources: The 2 mm and 1.3 mm Spectra of IRAS 21318+5631 and 22272+5435

The carriers of the 21 and 30 μm emission features in infrared spectra of circumstellar envelopes are a long-standing enigma. In this paper, we present the results of molecular line observations toward two circumstellar envelopes exhibiting the 21 and/or 30 μm features, IRAS 21318+5631 and 22272+5435. We investigate whether they have unusual gas-phase chemistry and search for a possible gas-phase precursor of the carriers of the two dust features. The spectra cover several discrete frequency ranges of 130–164 GHz and 216.5–273 GHz, resulting in a detection of 13 molecular species and isotopologues in each object. A rotation-diagram analysis is carried out to determine the molecular abundances, column densities, and excitation temperatures. We did not discover any molecular species that would be unexpected in a normal C-rich star. Nevertheless, there exists subtle differences between their molecular abundances. IRAS 22272+5435 shows stronger SiC2 and HC3N lines and weaker SiS lines than IRAS 21318+5631, suggesting that this 21 μm source is more carbon-rich and has experienced more efficient dust formation. We discuss the potential implications of the results for the carriers of the 21 μm and 30 μm features.
The proteolytic activity of selected lactic acid bacteria in fermenting cow's and camel's milk and the resultant sensory characteristics of the products

The aim of this study was to evaluate the proteolytic activities of 14 strains of lactic acid bacteria and their impact on sensory characteristics of the resultant fermented cow and camel milk. The results showed that Lactobacillus rhamnosus PTCC 1637 and Lactobacillus fermentum PTCC 1638 had high protease activity and high mean values for sensory quality in fermented cow's and camel's milk. Lactobacillus plantarum PTCC 1058 revealed high protease activity and sensory scores in camel milk. Consequently, milk fermentation using such strains might be recommended for the development of dairy products containing bioactive peptides.
UV Shadows in EFTs: Accidental Symmetries, Robustness and No‐Scale Supergravity

We argue that accidental approximate scaling symmetries are robust predictions of weakly coupled string vacua, and show that their interplay with supersymmetry and other (generalised) internal symmetries underlies the ubiquitous appearance of no‐scale supergravities in low‐energy 4D EFTs. We identify 4 nested types of no‐scale supergravities, and show how leading quantum corrections can break scale invariance while preserving some no‐scale properties (including non‐supersymmetric flat directions). We use these ideas to classify corrections to the low‐energy 4D supergravity action in perturbative 10D string vacua, including both bulk and brane contributions. Our prediction for the Kähler potential at any fixed order in α′ and string loops agrees with all extant calculations. p‐form fields play two important roles: they spawn many (generalised) shift symmetries; and space‐filling 4‐forms teach 4D physics about higher‐dimensional phenomena like flux quantisation. We argue that these robust symmetry arguments suffice to understand obstructions to finding classical de Sitter vacua, and suggest how to get around them in UV complete models.
End-to-End Sinkhorn Autoencoder With Noise Generator

In this work, we propose a novel end-to-end Sinkhorn Autoencoder with a noise generator for efficient data collection simulation. Simulating processes that aim at collecting experimental data is crucial for multiple real-life applications, including nuclear medicine, astronomy, and high energy physics. Contemporary methods, such as Monte Carlo algorithms, provide high-fidelity results at a price of high computational cost. Multiple attempts are taken to reduce this burden, e.g. using generative approaches based on Generative Adversarial Networks or Variational Autoencoders. Although such methods are much faster, they are often unstable in training and do not allow sampling from an entire data distribution. To address these shortcomings, we introduce a novel method dubbed end-to-end Sinkhorn Autoencoder, that leverages the Sinkhorn algorithm to explicitly align distribution of encoded real data examples and generated noise. More precisely, we extend autoencoder architecture by adding a deterministic neural network trained to map noise from a known distribution onto autoencoder latent space representing data distribution. We optimise the entire model jointly. Our method outperforms co mpeting approaches on a challenging dataset of simulation data from Zero Degree Calorimeters of ALICE experiment in LHC. as well as standard benchmarks, such as MNIST and CelebA.
Effects of wetness period and temperature on development of dark pod spot (Alternaria brassicae) on oilseed rape (Brassica napus)

In controlled environment experiments, when oilseed rape pods or leaves were inoculated with spore suspensions of Alternaria brassicae,the maximum disease incidence (proportion of pods or leaves diseased) increased as wetness period after inoculation increased from 4 to 24 h and as temperature increased to 20 C. There was a clear relationship between disease incidence on pods and incidence on leaves with the same wetness/temperature conditions. Logistic equations described the effects of wetness period after inoculation on disease incidence (number of pods or leaves infected) or disease severity (number of lesions on pods or leaves) using temperature-dependent and tissue-dependent parameters. The time from inoculation to the appearance of the first lesions was shorter on pods than on leaves at temperatures 15 C and wetness periods 12 h. Two-dimensional response surface equations or simple interpolations from one-dimensional equations were used to develop contour maps of expected disease incidence and severity, respectively, on leaves or pods to estimate the effects of different combinations of wetness period during infection and temperature on disease development.
Tight Approximate Differential Privacy for Discrete-Valued Mechanisms Using FFT

We propose a numerical accountant for evaluating the tight $(\varepsilon,\delta)$-privacy loss for algorithms with discrete one-dimensional output. The method is based on the privacy loss distribution formalism and it is able to exploit the recently introduced Fast Fourier Transform based accounting technique. We carry out a complete error analysis of the method in terms of the moment bounds for the numerical estimate of the privacy loss distribution. We demonstrate the performance on the binomial mechanism and show that our approach allows decreasing noise variance up to an order of magnitude at equal privacy compared to existing bounds in the literature. We also give a novel approach for evaluating $(\varepsilon,\delta)$-upper bound for the subsampled Gaussian mechanism. This completes the previously proposed analysis by giving a strict upper bound for $(\varepsilon,\delta)$. We also illustrate how to compute tight bounds for the exponential mechanism applied to counting queries.
High-Precision Extraction of Emerging Concepts from Scientific Literature

Identification of new concepts in scientific literature can help power faceted search, scientific trend analysis, knowledge-base construction, and more, but current methods are lacking. Manual identification can't keep up with the torrent of new publications, while the precision of existing automatic techniques is too low for many applications. We present an unsupervised concept extraction method for scientific literature that achieves much higher precision than previous work. Our approach relies on a simple but novel intuition: each scientific concept is likely to be introduced or popularized by a single paper that is disproportionately cited by subsequent papers mentioning the concept. From a corpus of computer science papers on arXiv, we find that our method achieves a Precision@1000 of 99%, compared to 86% for prior work, and a substantially better precision-yield trade-off across the top 15,000 extractions. To stimulate research in this area, we release our code and data.
The cost of approximate controllability, Carleman estimate, observability inequality, dynamic boundary conditions.

We consider the heat equation with dynamic bounary conditions involving gradient terms in a bounded domain. In this paper we study the cost of approximate controllability for this equation. Combining new developed Carleman estimates and some optimization techniques, we obtain explicit bounds of the minimal norm control. We consider the linear and the semilinear cases.
Global estimation and scenario-based projections of sex ratio at birth and missing female births using a Bayesian hierarchical time series mixture model

The sex ratio at birth (SRB) is defined as the ratio of male to female live births. The SRB imbalance in parts of the world over the past several decades is a direct consequence of sex-selective abortion, driven by the co-existence of son preference, readily available technology of prenatal sex determination, and fertility decline. Estimation and projection of the degree of SRB imbalance is complicated because of variability in SRB reference levels and because of the uncertainty associated with SRB observations. We develop Bayesian hierarchical time series mixture models for SRB estimation and scenario-based projections for all countries from 1950 to 2100. We model the SRB regional and national reference levels, and the fluctuation around national reference levels. We identify countries at risk of SRB imbalances and model both (i) the absence or presence of sex ratio transitions in such countries and, if present, (ii) the transition process. The transition model of SRB imbalance captures three stages (increase, stagnation and convergence back to SRB baselines). The model identifies countries with statistical evidence of SRB inflation in a fully Bayesian approach. The scenario-based SRB projections are based on the sex ratio transition model with varying assumptions regarding the occurrence of a sex ratio transition in at-risk countries. Projections are used to quantify the future burden of missing female births due to sex-selective abortions under different scenarios.
Basic Properties of Non-Stationary Ruijsenaars Functions

For any variable number, a non-stationary Ruijsenaars function was recently introduced as a natural generalization of an explicitly known asymptotically free solution of the trigonometric Ruijsenaars model, and it was conjectured that this non-stationary Ruijsenaars function provides an explicit solution of the elliptic Ruijsenaars model. We present alternative series representations of the non-stationary Ruijsenaars functions, and we prove that these series converge. We also introduce novel difference operators called $\mathcal{T}$ which, as we prove in the trigonometric limit and conjecture in the general case, act diagonally on the non-stationary Ruijsenaars functions.
Probing fMRI brain connectivity and activity changes during emotion regulation by EEG neurofeedback

Despite the existence of several emotion regulation studies using neurofeedback, interactions among a small number of regions were evaluated, and therefore, further investigation is needed to understand the interactions of the brain regions involved in emotion regulation. We implemented electroencephalography (EEG) neurofeedback with simultaneous functional magnetic resonance imaging (fMRI) using a modified happiness-inducing task through autobiographical memories to upregulate positive emotion. Then, an explorative analysis of whole brain regions was done to understand the effect of neurofeedback on brain activity and the interaction of whole brain regions involved in emotion regulation. The participants in the control and experimental groups were asked to do emotion regulation while viewing positive images of autobiographical memories and getting sham or real (based on alpha asymmetry) EEG neurofeedback, respectively. The proposed multimodal approach quantified the effects of EEG neurofeedback in changing EEG alpha power, fMRI blood oxygenation level-dependent (BOLD) activity of prefrontal, occipital, parietal, and limbic regions (up to 1.9% increase), and functional connectivity in/between prefrontal, parietal, limbic system, and insula in the experimental group. New connectivity links were identified by comparing the brain functional connectivity between experimental conditions (Upregulation and View blocks) and also by comparing the brain connectivity of the experimental and control groups. Psychometric assessments confirmed significant changes in positive and negative mood states in the experimental group by neurofeedback. Based on the exploratory analysis of activity and connectivity among all brain regions involved in emotion regions, we found significant BOLD and functional connectivity increases due to EEG neurofeedback in the experimental group, but no learning effect was observed in the control group. The results reveal several new connections among brain regions as a result of EEG neurofeedback which can be justified according to emotion regulation models and the role of those regions in emotion regulation and recalling positive autobiographical memories.
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks

The state-of-the art machine learning approach to training deep neural networks, backpropagation, is implausible for real neural networks: neurons need to know their outgoing weights; training alternates between a forward pass (computation) and a backward pass (learning); and the algorithm needs a large amount of labeled data. Biologically plausible approximations to backpropagation, such as feedback alignment, solve the weight transport problem, but not the other two. Thus, fully biologically plausible learning rules have so far remained elusive. Here we present a family of learning rules that does not suffer from any of these problems. It is motivated by the information bottleneck principle (extended with kernel methods), in which networks learn to squeeze as much information as possible out of the input without sacrificing prediction of the output. The resulting rules have a 3-factor Hebbian structure: they require pre- and post-synaptic firing rates and a global error signal - the third factor - that can be supplied by a neuromodulator. Moreover, they do not require precise labels; instead, they rely on the similarity between the desired outputs. They thus solve all three implausibility issues of backpropagation. Moreover, to obtain good performance on hard problems and retain biologically plausible learning rules, our rules need divisive normalization - a known feature of biological networks. Finally, simulations show that our rule performs nearly as well as backpropagation on image classification tasks.