Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Better Representation for Tables by Self-Supervised Tasks

Table-to-text generation aims at automatically generating natural text to help people to conveniently obtain the important information in tables. Although neural models for table-to-text have achieved remarkable progress, some problems still overlooked. The first is that the values recorded in many tables are mostly numbers in practice. The existing approaches do not do special treatment for these, and still regard these as words in natural language text. Secondly, the target texts in training dataset may contain redundant information or facts do not exist in the input tables. These may give wrong supervision signals to some methods based on content selection and planning and auxiliary supervision. To solve these problems, we propose two self-supervised tasks, Number Ordering and Significance Ordering, to help to learn better table representation. The former works on the column dimension to help to incorporate the size property of numbers into table representation. The latter acts on row dimension and help to learn a significance-aware table representation. We test our methods on the widely used dataset ROTOWIRE which consists of NBA game statistic and related news. The experimental results demonstrate that the model trained together with these two self-supervised tasks can generate text that contains more salient and well-organized facts, even without modeling context selection and planning. And we achieve the state-of-the-art performance on automatic metrics.
Valentine: Evaluating Matching Techniques for Dataset Discovery

Data scientists today search large data lakes to discover and integrate datasets. In order to bring together disparate data sources, dataset discovery methods rely on some form of schema matching: the process of establishing correspondences between datasets. Traditionally, schema matching has been used to find matching pairs of columns between a source and a target schema. However, the use of schema matching in dataset discovery methods differs from its original use. Nowadays schema matching serves as a building block for indicating and ranking inter-dataset relationships. Surprisingly, although a discovery method’s success relies highly on the quality of the underlying matching algorithms, the latest discovery methods employ existing schema matching algorithms in an ad-hoc fashion due to the lack of openly-available datasets with ground truth, reference method implementations, and evaluation metrics.In this paper, we aim to rectify the problem of evaluating the effectiveness and efficiency of schema matching methods for the specific needs of dataset discovery. To this end, we propose Valentine, an extensible open-source experiment suite to execute and organize large-scale automated matching experiments on tabular data. Valentine includes implementations of seminal schema matching methods that we either implemented from scratch (due to absence of open source code) or imported from open repositories. The contributions of Valentine are: i) the definition of four schema matching scenarios as encountered in dataset discovery methods, ii) a principled dataset fabrication process tailored to the scope of dataset discovery methods and iii) the most comprehensive evaluation of schema matching techniques to date, offering insight on the strengths and weaknesses of existing techniques, that can serve as a guide for employing schema matching in future dataset discovery methods.
Conditional Level Generation and Game Blending

Prior research has shown variational autoencoders (VAEs) to be useful for generating and blending game levels by learning latent representations of existing level data. We build on such models by exploring the level design affordances and applications enabled by conditional VAEs (CVAEs). CVAEs augment VAEs by allowing them to be trained using labeled data, thus enabling outputs to be generated conditioned on some input. We studied how increased control in the level generation process and the ability to produce desired outputs via training on labeled game level data could build on prior PCGML methods. Through our results of training CVAEs on levels from Super Mario Bros. , Kid Icarus and Mega Man, we show that such models can assist in level design by generating levels with desired level elements and patterns as well as producing blended levels with desired combinations of games.
Entropic Proofs of Singleton Bounds for Quantum Error-Correcting Codes

We show that a relatively simple reasoning using von Neumann entropy inequalities yields a robust proof of the quantum Singleton bound for quantum error-correcting codes (QECC). For entanglement-assisted quantum error-correcting codes (EAQECC) and catalytic codes (CQECC), a type of generalized quantum Singleton bound [Brun <italic>et al.</italic>, IEEE Trans. Inf. Theory 60(6):3073–3089 (2014)] was believed to hold for many years until recently one of us found a counterexample [MG, Phys. Rev. A 103, 020601 (2021)]. Here, we rectify this state of affairs by proving the correct generalized quantum Singleton bound, extending the above-mentioned proof method for QECC; we also prove information-theoretically tight bounds on the entanglement-communication tradeoff for EAQECC. All of the bounds relate block length <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> and code length <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> for given minimum distance <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> and we show that they are robust, in the sense that they hold with small perturbations for codes which only correct most of the erasure errors of less than <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> letters. In contrast to the classical case, the bounds take on qualitatively different forms depending on whether the minimum distance is smaller or larger than half the block length. We also provide a propagation rule: any pure QECC yields an EAQECC with the same distance and dimension, but of shorter block length.
An Open-Source Dataset on Dietary Behaviors and DASH Eating Plan Optimization Constraints

Linear constrained optimization techniques have been applied to many real-world settings. In recent years, inferring the unknown parameters and functions inside an optimization model has also gained traction. This inference is often based on existing observations and/or known parameters. Consequently, such models require reliable, easily accessed, and easily interpreted examples to be evaluated. To facilitate research in such directions, we provide a modified dataset based on dietary behaviors of different groups of people, their demographics, and pre-existing conditions, among other factors. This data is gathered from the National Health and Nutrition Examination Survey (NHANES) and complemented with the nutritional data from the United States Department of Agriculture (USDA). We additionally provide tailored datasets for hypertension and pre-diabetic patients as groups of interest who may benefit from targetted diets such as the Dietary Approaches to Stop Hypertension (DASH) eating plan. The data is compiled and curated in such a way that it is suitable as input to linear optimization models. We hope that this data and its supplementary, open-accessed materials can accelerate and simplify interpretations and research on linear optimization and constrained inference models. The complete dataset can be found in the following repository: this https URL
On Convergence of Nearest Neighbor Classifiers over Feature Transformations

The k-Nearest Neighbors (kNN) classifier is a fundamental non-parametric machine learning algorithm. However, it is well known that it suffers from the curse of dimensionality, which is why in practice one often applies a kNN classifier on top of a (pre-trained) feature transformation. From a theoretical perspective, most, if not all theoretical results aimed at understanding the kNN classifier are derived for the raw feature space. This leads to an emerging gap between our theoretical understanding of kNN and its practical applications. In this paper, we take a first step towards bridging this gap. We provide a novel analysis on the convergence rates of a kNN classifier over transformed features. This analysis requires in-depth understanding of the properties that connect both the transformed space and the raw feature space. More precisely, we build our convergence bound upon two key properties of the transformed space: (1) safety -- how well can one recover the raw posterior from the transformed space, and (2) smoothness -- how complex this recovery function is. Based on our result, we are able to explain why some (pre-trained) feature transformations are better suited for a kNN classifier than other. We empirically validate that both properties have an impact on the kNN convergence on 30 feature transformations with 6 benchmark datasets spanning from the vision to the text domain.
Avoiding Side Effects By Considering Future Tasks

Designing reward functions is difficult: the designer has to specify what to do (what it means to complete the task) as well as what not to do (side effects that should be avoided while completing the task). To alleviate the burden on the reward designer, we propose an algorithm to automatically generate an auxiliary reward function that penalizes side effects. This auxiliary objective rewards the ability to complete possible future tasks, which decreases if the agent causes side effects during the current task. The future task reward can also give the agent an incentive to interfere with events in the environment that make future tasks less achievable, such as irreversible actions by other agents. To avoid this interference incentive, we introduce a baseline policy that represents a default course of action (such as doing nothing), and use it to filter out future tasks that are not achievable by default. We formally define interference incentives and show that the future task approach with a baseline policy avoids these incentives in the deterministic case. Using gridworld environments that test for side effects and interference, we show that our method avoids interference and is more effective for avoiding side effects than the common approach of penalizing irreversible actions.
Constraints on the [C ii] luminosity of a proto-globular cluster at z ∼ 6 obtained with ALMA

We report on ALMA observations of D1, a system at z~6.15 with stellar mass M_* ~ 10^7 M_sun containing globular cluster (GC) precursors, strongly magnified by the galaxy cluster MACS J0416.1-2403. Since the discovery of GC progenitors at high redshift, ours is the first attempt to probe directly the physical properties of their neutral gas through infrared observations. A careful analysis of our dataset, performed with a suitable procedure designed to identify faint narrow lines and which can test various possible values for the unknown linewidth value, allowed us to identify a 4-sigma tentative detection of [CII] emission with intrinsic luminosity L_CII=(2.9 +/- 1.4) 10^6 L_sun, one of the lowest values ever detected at high redshift. This study offers a first insight on previously uncharted regions of the L_CII-SFR relation. Despite large uncertainties affecting our measure of the star formation rate, if taken at face value our estimate lies more than 1 dex below the values observed in local and high redshift systems. Our weak detection indicates a deficiency of [CII] emission, possibly ascribed to various explanations, such as a low-density gas and/or a strong radiation field caused by intense stellar feedback, and a low metal content. From the non-detection in the continuum we derive constraints on the dust mass, with 3-sigma upper limit values as low as a few 10^4 M_sun, consistent with the values measured in local metal-poor galaxies.
Dynamic Walking: Toward Agile and Efficient Bipedal Robots

Dynamic walking on bipedal robots has evolved from an idea in science fiction to a practical reality. This is due to continued progress in three key areas: a mathematical understanding of locomotion, the computational ability to encode this mathematics through optimization, and the hardware capable of realizing this understanding in practice. In this context, this review outlines the end-to-end process of methods that have proven effective in the literature for achieving dynamic walking on bipedal robots. We begin by introducing mathematical models of locomotion, from reduced-order models that capture essential walking behaviors to hybrid dynamical systems that encode the full-order continuous dynamics along with discrete foot-strike dynamics. These models form the basis for gait generation via (nonlinear) optimization problems. Finally, models and their generated gaits merge in the context of real-time control, wherein walking behaviors are translated to hardware. The concepts presented are illustrated throughout in simulation, and experimental instantiations on multiple walking platforms are highlighted to demonstrate the ability to realize dynamic walking on bipedal robots that is both agile and efficient.
A Novel Quantum Algorithm for Ant Colony Optimization

Ant colony optimization is one of the potential solutions to tackle intractable NP-Hard discrete combinatorial optimization problems. The metaphor of ant colony can be thought of as the evolution of the best path from a given graph as a globally optimal solution, which is unaffected by earlier local convergence to achieve improved optimization efficiency. Earlier Quantum Ant Colony Optimization research work was primarily based on Quantum-inspired Evolutionary Algorithms, which deals with customizing and improving the quantum rotation gate through upgraded formation of the lookup table of rotation angle. Instead of relying on evolutionary algorithms, we have proposed a discrete-time quantum algorithm based on adaptive quantum circuit for pheromone updation. The algorithm encodes all possible paths in the exhaustive search space as input to the ORACLE. Iterative model of exploration and exploitation of all possible paths by quantum ants results in global optimal path convergence through probabilistic measurement of selected path. Our novel approach attempts to accelerate the search space exploitation in a significant manner to obtain the best optimal path as a solution through quantum arallelization achieving polynomial time speed-up over its classical counter part.
PCR-RFLP of the COI gene reliably differentiates Cx. pipiens, Cx. pipiens f. molestus and Cx. torrentium of the Pipiens Complex

Culex pipiens Linnaeus, Culex pipiens f. molestus Forskal and the closely related species Culex torrentium Martini are difficult to identify within the limits of traditional taxonomy. Polymorphism was noted between these species in a 603bp fragment of the 5’end of the mitochondrial cytochrome oxidase C subunit I gene and used as the basis for developing polymerase chain reaction-restriction fragment-length polymorphism (PCRRFLP) assays for the differentiation of Cx. pipiens and Cx. torrentium by BclI restrictase, and Cx. pipiens and Cx. pipiens f. molestus by HaeIII restrictase. This method allows the identification of samples at any developmental stage.
Towards reflectivity profile inversion through artificial neural networks

The goal of specular neutron and x-ray reflectometry is to infer a material’s scattering length density (SLD) profile from its experimental reflectivity curves. This paper focuses on the investigation of an original approach to the ill-posed non-invertible problem which involves the use of artificial neural networks (ANNs). In particular, the numerical experiments described here deal with large data sets of simulated reflectivity curves and SLD profiles, and aim to assess the applicability of data science and machine learning technology to the analysis of data generated at large-scale neutron scattering facilities. It is demonstrated that, under certain circumstances, properly trained deep neural networks are capable of correctly recovering plausible SLD profiles when presented with previously unseen simulated reflectivity curves. When the necessary conditions are met, a proper implementation of the described approach would offer two main advantages over traditional fitting methods when dealing with real experiments, namely (1) sample physical models are described under a new paradigm: detailed layer-by-layer descriptions (SLDs, thicknesses, roughnesses) are replaced by parameter-free curves ρ(z), allowing a priori assumptions to be used in terms of the sample family to which a given sample belongs (e.g. ‘thin film,’ ‘lamellar structure’,etc.); (2) the time required to reach a solution is shrunk by orders of magnitude, enabling faster batch analysis for large datasets.
One-way introgressive hybridisation between Sorbus aria and S. torminalis (Rosaceae) in southern Britain

Material of Sorbus aria-like plants from the Wye Valley with acutely-lobed leaves has been investigated using comparative morphology and reproductive fertility, which show that at least some such plants are S. aria introgressed with S. torminalis. The introgression was found to occur only from S. aria into S. torminalis. The primary and introgressed hybrids have lower pollen and fruit fertility than the parents. The parents show widespread overlap in distribution in southern Britain but introgression has been confirmed only for the Wye Valley. Sorbus × vagensis should be treated as a hybrid, not as a species.
Integrating Coarse Granularity Part-level Features with Supervised Global-level Features for Person Re-identification

Holistic person re-identification (Re-ID) and partial person re-identification have achieved great progress respectively in recent years. However, scenarios in reality often include both holistic and partial pedestrian images, which makes single holistic or partial person Re-ID hard to work. In this paper, we propose a robust coarse granularity part-level person Re-ID network (CGPN), which not only extracts robust regional level body features, but also integrates supervised global features for both holistic and partial person images. CGPN gains two-fold benefit toward higher accuracy for person Re-ID. On one hand, CGPN learns to extract effective body part features for both holistic and partial person images. On the other hand, compared with extracting global features directly by backbone network, CGPN learns to extract more accurate global features with a supervision strategy. The single model trained on three Re-ID datasets including Market-1501, DukeMTMC-reID and CUHK03 achieves state-of-the-art performances and outperforms any existing approaches. Especially on CUHK03, which is the most challenging dataset for person Re-ID, in single query mode, we obtain a top result of Rank-1/mAP=87.1\%/83.6\% with this method without re-ranking, outperforming the current best method by +7.0\%/+6.7\%.
Traveling Wave Tube Eigenmode Solver for Interacting Hot Slow Wave Structure Based on Particle-In-Cell Simulations

An eigenmode solver based on particle-in-cell simulations is proposed to find the hybrid beam-electromagnetic eigenmodes in a hot slow-wave structure (SWS), i.e., where the electromagnetic mode interacts with an electron beam. The proposed scheme is based on the determination of the transfer matrix of the unit-cell of the hot SWS that takes into account the interaction of the electromagnetic mode with the electron beam, followed by the determination of its eigenvalues and eigenvectors using Floquet theory. The proposed method is applied to find the hot modes, with complex wavenumber, in a traveling wave tube (TWT) amplifier with helix SWS. We show dispersion relations of the modal complex wavenumber of the electromagnetic wave interacting with the electron beam when varying frequency and beam voltage, with results in agreement with Pierce theory. The method is also applied to find the complex-wavenumber modes of the hot SWS of a millimeter wave TWT amplifier based on a serpentine waveguide. The technique is general and can be applied to any SWS geometry where the electromagnetic modes interact with an electron beam.
Lightweight End-to-End Speech Recognition from Raw Audio Data Using Sinc-Convolutions

Many end-to-end Automatic Speech Recognition (ASR) systems still rely on pre-processed frequency-domain features that are handcrafted to emulate the human hearing. Our work is motivated by recent advances in integrated learnable feature extraction. For this, we propose Lightweight Sinc-Convolutions (LSC) that integrate Sinc-convolutions with depthwise convolutions as a low-parameter machine-learnable feature extraction for end-to-end ASR systems. We integrated LSC into the hybrid CTC/attention architecture for evaluation. The resulting end-to-end model shows smooth convergence behaviour that is further improved by applying SpecAugment in time-domain. We also discuss filter-level improvements, such as using log-compression as activation function. Our model achieves a word error rate of 10.7% on the TEDlium v2 test dataset, surpassing the corresponding architecture with log-mel filterbank features by an absolute 1.9%, but only has 21% of its model size.
A search of proteins that interact with Sin3A-associated proteins 30 and 30-like

Background: Histone deacetylase complex (HDAC complex) removes acetyl groups from histones herewith leading to a tighter DNA structure and repression of transcription. A novel member of this complex is a Sin3A-associated protein 30-like (SAP30L) which is 70% identical with a Sin3A-associate protein 30 (SAP30), a protein previously identified to participate in the HDAC complex. Aberrant acetylation is associated with several solid tumours and haematological malignancies. Altered acetylation degree of histones may also play a role in other diseases. In order to better understanding the function of SAP proteins we set out to study their interacting proteins. Methods: The glutathione-S-transferase pull-down method was used to “fish” the interacting proteins from human cell lysates using SAP30 and SAP30L as baits. The bound proteins were separated on gel and analysed by mass spectrometry. Suspected interactions were ensured by immunoblotting. Results: Mass spectrometric analysis gave numerous possible interacting proteins, including ribosomal proteins, which were the majority, proteins needed for ribosome biogenesis and cancer related proteins. The interacting proteins were pretty much the same for SAP30 and SAP30L. Interestingly, none of the interacting proteins, found by this experiment, was a member of the HDAC complex. Immunoblotting studies confirmed that SAP30 and SAP30L interact with nucleolin and fibrillarin. Conclusion: SAP30 and SAP30L interact mainly with the same proteins suggesting that they have similar functions. Mapping of protein interactions suggest a nucleolar role for SAP30 and SAP30L.
Named Entity Recognition and Relation Extraction using Enhanced Table Filling by Contextualized Representations

In this study, a novel method for extracting named entities and relations from unstructured text based on the table representation is presented. By using contextualized word embeddings, the proposed method computes representations for entity mentions and long-range dependencies without complicated hand-crafted features or neural-network architectures. We also adapt a tensor dot-product to predict relation labels all at once without resorting to history-based predictions or search strategies. These advances significantly simplify the model and algorithm for the extraction of named entities and relations. Despite its simplicity, the experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on the CoNLL04 and ACE05 English datasets. We also confirm that the proposed method achieves a comparable performance with the state-of-the-art NER models on the ACE05 datasets when multiple sentences are provided for context aggregation.