Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential for Definitive Discovery of a 70 GeV Dark Matter WIMP with Only Second-Order Gauge Couplings

As astronomical observations and their interpretation improve, the case for cold dark matter (CDM) becomes increasingly persuasive. A particularly appealing version of CDM is a weakly interacting massive particle (WIMP) with a mass near the electroweak scale, which can naturally have the observed relic abundance after annihilation in the early universe. But in order for a WIMP to be consistent with the currently stringent experimental constraints it must have relatively small cross-sections for indirect, direct, and collider detection. Using our calculations and estimates of these cross-sections, we discuss the potential for discovery of a recently proposed dark matter WIMP which has a mass of about 70 GeV/c$^2$ and only second-order couplings to W and Z bosons. There is evidence that indirect detection may already have been achieved, since analyses of the gamma rays detected by Fermi-LAT and the antiprotons observed by AMS-02 are consistent with 70 GeV dark matter having our calculated $\langle \sigma_{ann} v \rangle \approx 1.2 \times 10^{-26} $ cm$^3$/s. The estimated sensitivities for LZ and XENONnT indicate that these experiments may achieve direct detection within the next few years, since we estimate the relevant cross-section to be slightly above $10^{-48}$ cm$^2$. Other experiments such as PandaX, SuperCDMS, and especially DARWIN should be able to confirm on a longer time scale. The high-luminosity LHC might achieve collider detection within about 15 years, since we estimate a collider cross-section slightly below 1 femtobarn. Definitive confirmation should come from still more powerful planned collider experiments (such as a future circular collider) within 15-35 years.
Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation

The most interesting words in scientific texts will often be novel or rare. This presents a challenge for scientific word embedding models to determine quality embedding vectors for useful terms that are infrequent or newly emerging. We demonstrate how Latent Semantic Imputation (LSI) can address this problem by imputing embeddings for domain-specific words from up-to-date knowledge graphs while otherwise preserving the original word embedding model. We use the MeSH knowledge graph to impute embedding vectors for biomedical terminology without retraining and evaluate the resulting embedding model on a domain-specific word-pair similarity task. We show that LSI can produce reliable embedding vectors for rare and out-of-vocabulary terms in the biomedical domain.
Time-Domain Based Embeddings for Spoofed Audio Representation

Anti-spoofing is the task of speech authentication. That is, identifying genuine human speech compared to spoofed speech. The main focus of this paper is to suggest new representations for genuine and spoofed speech, based on the probability mass function (PMF) estimation of the audio waveforms' amplitude. We introduce a new feature extraction method for speech audio signals: unlike traditional methods, our method is based on direct processing of time-domain audio samples. The PMF is utilized by designing a feature extractor based on different PMF distances and similarity measures. As an additional step, we used filter-bank preprocessing, which significantly affects the discriminative characteristics of the features and facilitates convenient visualization of possible clustering of spoofing attacks. Furthermore, we use diffusion maps to reveal the underlying manifold on which the data lies. The suggested embeddings allow the use of simple linear separators to achieve decent performance. In addition, we present a convenient way to visualize the data, which helps to assess the efficiency of different spoofing techniques. The experimental results show the potential of using multi-channel PMF based features for the anti-spoofing task, in addition to the benefits of using diffusion maps both as an analysis tool and as an embedding tool.
Deep Learning is Provably Robust to Symmetric Label Noise

Deep neural networks (DNNs) are capable of perfectly fitting the training data, including memorizing noisy data. It is commonly believed that memorization hurts generalization. Therefore, many recent works propose mitigation strategies to avoid noisy data or correct memorization. In this work, we step back and ask the question: Can deep learning be robust against massive label noise without any mitigation? We provide an affirmative answer for the case of symmetric label noise: We find that certain DNNs, including under-parameterized and over-parameterized models, can tolerate massive symmetric label noise up to the information-theoretic threshold. By appealing to classical statistical theory and universal consistency of DNNs, we prove that for multiclass classification, $L_1$-consistent DNN classifiers trained under symmetric label noise can achieve Bayes optimality asymptotically if the label noise probability is less than $\frac{K-1}{K}$, where $K \ge 2$ is the number of classes. Our results show that for symmetric label noise, no mitigation is necessary for $L_1$-consistent estimators. We conjecture that for general label noise, mitigation strategies that make use of the noisy data will outperform those that ignore the noisy data.
Quotatives Indicate Decline in Objectivity in U.S. Political News

According to journalistic standards, direct quotes should be attributed to sources with objective quotatives such as ``said'' and ``told,'' since nonobjective quotatives, e.g., ``argued'' and ``insisted,'' would influence the readers' perception of the quote and the quoted person. In this paper, we analyze the adherence to this journalistic norm to study trends in objectivity in political news across U.S. outlets of different ideological leanings. We ask: 1) How has the usage of nonobjective quotatives evolved? 2) How do news outlets use nonobjective quotatives when covering politicians of different parties? To answer these questions, we developed a dependency-parsing-based method to extract quotatives and applied it to Quotebank, a web-scale corpus of attributed quotes, obtaining nearly 7 million quotes, each enriched with the quoted speaker's political party and the ideological leaning of the outlet that published the quote. We find that, while partisan outlets are the ones that most often use nonobjective quotatives, between 2013 and 2020, the outlets that increased their usage of nonobjective quotatives the most were ``moderate'' centrist news outlets (around 0.6 percentage points, or 20% in relative percentage over seven years). Further, we find that outlets use nonobjective quotatives more often when quoting politicians of the opposing ideology (e.g., left-leaning outlets quoting Republicans) and that this ``quotative bias'' is rising at a swift pace, increasing up to 0.5 percentage points, or 25% in relative percentage, per year. These findings suggest an overall decline in journalistic objectivity in U.S. political news.
Improving abstractive summarization with energy-based re-ranking

Current abstractive summarization systems present important weaknesses which prevent their deployment in real-world applications, such as the omission of relevant information and the generation of factual inconsistencies (also known as hallucinations). At the same time, automatic evaluation metrics such as CTC scores (Deng et al., 2021) have been recently proposed that exhibit a higher correlation with human judgments than traditional lexical-overlap metrics such as ROUGE. In this work, we intend to close the loop by leveraging the recent advances in summarization metrics to create quality-aware abstractive summarizers. Namely, we propose an energy-based model that learns to re-rank summaries according to one or a combination of these metrics. We experiment using several metrics to train our energy-based re-ranker and show that it consistently improves the scores achieved by the predicted summaries. Nonetheless, human evaluation results show that the re-ranking approach should be used with care for highly abstractive summaries, as the available metrics are not yet sufficiently reliable for this purpose.
Perception-aware Tag Placement Planning for Robust Localization of UAVs in Indoor Construction Environments

Tag-based visual-inertial localization is a lightweight method for enabling autonomous data collection missions of low-cost unmanned aerial vehicles (UAVs) in indoor construction environments. However, finding the optimal tag configuration (i.e., number, size, and location) on dynamic construction sites remains challenging. This paper proposes a perception-aware genetic algorithm-based tag placement planner (PGA-TaPP) to determine the optimal tag configuration using 4D-BIM, considering the project progress, safety requirements, and UAV's localizability. The proposed method provides a 4D plan for tag placement by maximizing the localizability in user-specified regions of interest (ROIs) while limiting the installation costs. Localizability is quantified using the Fisher information matrix (FIM) and encapsulated in navigable grids. The experimental results show the effectiveness of our method in finding an optimal 4D tag placement plan for the robust localization of UAVs on under-construction indoor sites.
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models

Decoding methods for large language models often trade-off between diversity of outputs and parallelism of computation. Methods such as beam search and Gumbel top-k sampling can guarantee a different output for each element of the beam, but are not easy to parallelize. Alternatively, methods such as temperature sampling and its modifications (top-k sampling, nucleus sampling, typical decoding, and others), are embarrassingly parallel, but have no guarantees about duplicate samples. We present a framework for sampling according to an arithmetic code book implicitly defined by a large language model, compatible with common sampling variations, with provable beam diversity under certain conditions, as well as being embarrassingly parallel and providing unbiased and consistent expectations from the original model. We demonstrate the effectiveness of our approach on WMT machine translation, more than halving the standard deviation when estimating expected BLEU score reward, and closing the BLEU score gap between independent sampling and beam search by up to 63%.
Text2Model: Model Induction for Zero-shot Generalization Using Task Descriptions

We study the problem of generating a training-free task-dependent visual clas-siﬁer from text descriptions without visual samples. This Text-to-Model (T2M) problem is closely related to zero-shot learning, but unlike previous work, a T2M model infers a model tailored to a task, taking into account all classes in the task. We analyze the symmetries of T2M, and characterize the equivariance and invariance properties of corresponding models. In light of these properties we design an architecture based on hypernetworks that given a set of new class descriptions predicts the weights for an object recognition model which classiﬁes images from those zero-shot classes. We demonstrate the beneﬁts of our approach compared to zero-shot learning from text descriptions in image and point-cloud classiﬁcation using various types of text descriptions: From single words to rich text descriptions.
Grokking phase transitions in learning local rules with gradient descent

We discuss two solvable grokking (generalisation beyond overfitting) models in a rule learning scenario. We show that grokking is a phase transition and find exact analytic expressions for the critical exponents, grokking probability, and grokking time distribution. Further, we introduce a tensor-network map that connects the proposed grokking setup with the standard (perceptron) statistical learning theory and show that grokking is a consequence of the locality of the teacher model. As an example, we analyse the cellular automata learning task, numerically determine the critical exponent and the grokking time distributions and compare them with the prediction of the proposed grokking model. Finally, we numerically analyse the connection between structure formation and grokking.
The law of one price in quadratic hedging and mean-variance portfolio selection

The \emph{law of one price (LOP)} broadly asserts that identical financial flows should command the same price. We show that, when properly formulated, LOP is the minimal condition for a well-defined mean--variance portfolio allocation framework without degeneracy. Crucially, the paper identifies a new mechanism through which LOP can fail in a continuous-time $L^2(P)$ setting without frictions, namely `trading from just before a predictable stopping time', which surprisingly identifies LOP violations even for continuous price processes. Closing this loophole allows to give a version of the ``Fundamental Theorem of Asset Pricing'' appropriate in the quadratic context, establishing the equivalence of the economic concept of LOP with the probabilistic property of the existence of a local $\scr{E}$-martingale state price density. The latter provides unique prices for all square-integrable contingent claims in an extended market and subsequently plays an important role in mean-variance portfolio selection and quadratic hedging.
Constrained Approximate Similarity Search on Proximity Graph

Search engines and recommendation systems are built to eﬃciently display relevant information from those massive amounts of candidates. Typically a three-stage mechanism is employed in those systems: (i) a small collection of items are ﬁrst retrieved by (e.g.,) approximate near neighbor search algorithms; (ii) then a collection of constraints are applied on the retrieved items; (iii) a ﬁne-grained ranking neural network is employed to determine the ﬁnal recommendation. We observe a major defect of the original three-stage pipeline: Although we only target to retrieve k vectors in the ﬁnal recommendation, we have to preset a suﬃciently large s ( s > k ) for each query, and “hope” the number of survived vectors after the ﬁltering is not smaller than k . That is, at least k vectors in the s similar candidates satisfy the query constraints. In this paper, we investigate this constrained similarity search problem and attempt to merge the similarity search stage and the ﬁltering stage into one single search operation. We introduce AIRSHIP , a system that integrates a user-deﬁned function ﬁltering into the similarity search framework. The proposed system does not need to build extra indices nor require prior knowledge of the query constraints. We propose three optimization strategies: (1) starting point selection, (2) multi-direction search, and (3) biased priority queue selection. Our ﬁrst starting point selection optimization is to locate good starting points for the graph searching algorithm. We would like to start the graph search inside a cluster of satisﬁed vectors. In this scenario, a small number of satisﬁed vectors can be retrieved along the path to the query vector. Then, we propose a multi-direction search optimization that enables multi-direction searching. Compared with the single priority queue used in the original graph search, we have two priority queues to keep search candidates: on stores the satisﬁed vectors and the other maintains remaining unsatisﬁed vectors. When both priority queues are not empty, we choose each queue with a weight hyper-parameter. Experimental evaluations on
Stellar Populations and Mass Loss in M15: A Spitzer Space Telescope Detection of Dust in the Intracluster Medium

We present Spitzer Space Telescope IRAC and MIPS observations of the Galactic globular cluster M15 (NGC 7078), one of the most metal-poor clusters, with a [Fe/H] = -2.4. Our Spitzer images reveal a population of dusty red giants near the cluster center, a previously detected planetary nebula designated K648, and a possible detection of the intracluster medium arising from mass-loss episodes from the evolved stellar population. Our analysis suggests that (9 ± 2) × 10-4 M⊙ of dust is present in the core of M15, and that this material has accumulated over a period of ≈106 yr, a timescale 10 times shorter than the last Galactic plane crossing event. We also present Spitzer IRS follow-up observations of K648, including the detection of the [Ne II] 12.81 μm line, and discuss abundances derived from infrared fine-structure lines.
Overcritical states of a superconductor strip in a magnetic environment

A current-carrying superconducting strip partly penetrated by magnetic flux and surrounded by a bulk magnet of high permeability is considered. Two types of samples are studied: those with critical current controlled by an edge barrier dominating over the pinning, and those with high pinning-mediated critical current masking the edge barrier. It is shown for both cases that the current distribution in a central flux-free part of the strip is strongly affected by the actual shape of the magnetic surroundings. Explicit analytical solutions for the sheet current and self-field distributions are obtained which show that, depending on the geometry, the effect may suppress the total loss-free transport current of the strip or enhance it by orders of magnitude. The effect depends strongly on the shape of the magnet and its distance to the superconductor but only weakly on the magnetic permeability.
Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems

Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging graph learning techniques to build new temporal recurrent graph neural network models for fault diagnostics. The temporal recurrent graph neural network structures can extract the spatial-temporal features from data of voltage measurement units installed at the critical buses. From these features, fault event detection, fault type/phase classification, and fault location are performed. Compared with previous works, the proposed temporal recurrent graph neural networks provide a better generalization for fault diagnostics. Moreover, the proposed scheme retrieves the voltage signals instead of current signals so that there is no need to install relays at all lines of the distribution system. Therefore, the proposed scheme is generalizable and not limited by the number of relays installed. The effectiveness of the proposed method is comprehensively evaluated on the Potsdam microgrid and IEEE 123-node system in comparison with other neural network structures.
Towards High-Quality Neural TTS for Low-Resource Languages by Learning Compact Speech Representations

This paper aims to enhance low-resource TTS by reducing training data requirements using compact speech representations. A Multi-Stage Multi-Codebook (MSMC) VQ-GAN is trained to learn the representation, MSMCR, and decode it to waveforms. Subsequently, we train the multi-stage predictor to predict MSMCRs from the text for TTS synthesis. Moreover, we optimize the training strategy by leveraging more audio to learn MSMCRs better for low-resource languages. It selects audio from other languages using speaker similarity metric to augment the training set, and applies transfer learning to improve training quality. In MOS tests, the proposed system significantly outperforms FastSpeech and VITS in standard and low-resource scenarios, showing lower data requirements. The proposed training strategy effectively enhances MSMCRs on waveform reconstruction. It improves TTS performance further, which wins 77% votes in the preference test for the low-resource TTS with only 15 minutes of paired data.
B - L cosmic strings and baryogenesis

Cosmic strings arising from breaking of the $\mathrm{U}(1{)}_{B\ensuremath{-}L}$ gauge symmetry that occurs in a wide variety of unified models can carry zero modes of heavy Majorana neutrinos. Decaying and/or repeatedly self-interacting closed loops of these ``$B\ensuremath{-}L$'' cosmic strings can be a nonthermal source of heavy right-handed Majorana neutrinos whose decay can contribute to the observed baryon asymmetry of the Universe (BAU) via the leptogenesis route. The $B\ensuremath{-}L$ cosmic strings are expected in grand unified theory (GUT) models such as SO(10), where they can be formed at an intermediate stage of symmetry breaking well below the GUT scale $\ensuremath{\sim}{10}^{16}\text{ }\text{ }\mathrm{G}\mathrm{e}\mathrm{V}$; such light strings are not excluded by the cosmic microwave background (CMB) anisotropy data and may well exist. We estimate the contribution of $B\ensuremath{-}L$ cosmic string loops to the baryon-to-photon ratio of the Universe in the light of current knowledge on neutrino masses and mixings implied by atmospheric and solar neutrino measurements. We find that $B\ensuremath{-}L$ cosmic string loops can contribute significantly to the BAU for $\mathrm{U}(1{)}_{B\ensuremath{-}L}$ symmetry-breaking scale ${\ensuremath{\eta}}_{B\ensuremath{-}L}\ensuremath{\gtrsim}1.7\ifmmode\times\else\texttimes\fi{}{10}^{11}\text{ }\text{ }\mathrm{G}\mathrm{e}\mathrm{V}$. At the same time, in order for the contribution of decaying $B\ensuremath{-}L$ cosmic string loops not to exceed the observed baryon-to-photon ratio inferred from the recent Wilkinson microwave anisotropy probe (WMAP) results, the lightest heavy right-handed Majorana neutrino mass ${M}_{1}$ must satisfy the constraint ${M}_{1}\ensuremath{\le}2.4\ifmmode\times\else\texttimes\fi{}{10}^{12}({\ensuremath{\eta}}_{B\ensuremath{-}L}/{10}^{13}\text{ }\text{ }\mathrm{G}\mathrm{e}\mathrm{V}{)}^{1/2}\text{ }\text{ }\mathrm{G}\mathrm{e}\mathrm{V}$. This may have interesting implications for the associated Yukawa couplings in the heavy neutrino sector and consequently for the light neutrino masses generated through seesaw mechanism.
Explaining the Explainers in Graph Neural Networks: a Comparative Study

Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need for methods to understand their decision process. GNN explainers have started to emerge in recent years, with a multitude of methods both novel or adapted from other domains. To sort out this plethora of alternative approaches, several studies have benchmarked the performance of different explainers in terms of various explainability metrics. However, these earlier works make no attempts at providing insights into why different GNN architectures are more or less explainable, or which explainer should be preferred in a given setting. In this survey, we fill these gaps by devising a systematic experimental study, which tests ten explainers on eight representative architectures trained on six carefully designed graph and node classification datasets. With our results we provide key insights on the choice and applicability of GNN explainers, we isolate key components that make them usable and successful and provide recommendations on how to avoid common interpretation pitfalls. We conclude by highlighting open questions and directions of possible future research.