Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robot Cluster for Reproducible Research in Dexterous Manipulation

—Dexterous manipulation remains an open problem in robotics. To coordinate efforts of the research community towards tackling this problem, we propose a shared benchmark. We designed and built robotic platforms that are hosted at the MPI-IS 1 and can be accessed remotely. Each platform consists of three robotic ﬁngers that are capable of dexterous object manipulation. Users are able to control the platforms remotely by submitting code that is executed automatically, akin to a computational cluster. Using this setup, i) we host robotics competitions, where teams from anywhere in the world access our platforms to tackle challenging tasks, ii) we publish the datasets collected during these competitions (consisting of hundreds of robot hours), and iii) we give researchers access to these platforms for their own projects.
Automated Fact-Checking: A Survey

As online false information continues to grow, automated fact-checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact-checking datasets, devising automated fact-checking pipelines and proposing NLP methods to further research in the development of different components. This paper reviews relevant research on automated fact-checking covering both the claim detection and claim validation components.
Joint Speaker Diarisation and Tracking in Switching State-Space Model

Speakers may move around while diarisation is being performed. When a microphone array is used, the instantaneous locations of where the sounds originated from can be estimated, and previous investigations have shown that such information can be complementary to speaker embeddings in the diarisation task. However, these approaches often assume that speakers are fairly stationary throughout a meeting. This paper relaxes this assumption, by proposing to explicitly track the movements of speakers while jointly performing diarisation within a unified model. A state-space model is proposed, where the hidden state expresses the identity of the current active speaker and the predicted locations of all speakers. The model is implemented as a particle filter. Experiments on a Microsoft rich meeting transcription task show that the proposed joint location tracking and diarisation approach is able to perform comparably with other methods that use location information.
Named Entity Recognition and Classification in Historical Documents: A Survey

After decades of massive digitisation, an unprecedented number of historical documents are available in digital format, along with their machine-readable texts. While this represents a major step forward with respect to preservation and accessibility, it also opens up new opportunities in terms of content mining and the next fundamental challenge is to develop appropriate technologies to efficiently search, retrieve, and explore information from this ‘big data of the past’. Among semantic indexing opportunities, the recognition and classification of named entities are in great demand among humanities scholars. Yet, named entity recognition (NER) systems are heavily challenged with diverse, historical, and noisy inputs. In this survey, we present the array of challenges posed by historical documents to NER, inventory existing resources, describe the main approaches deployed so far, and identify key priorities for future developments.
Dynamic inference of user context through social tag embedding for music recommendation

Dynamic inference of user context through social tag embedding for music recommendation Dynamic inference of user context for music recommendation Diego Sánchez-Moreno University of Salamanca, Department of Computer Science and Automation, sanchez91@gmail.com Álvaro Lozano Murciego University of Salamanca, Department of Computer Science and Automation, loza@usal.es Vivian F. López Batista University of Salamanca, Department of Computer Science and Automation, vivian@usal.es María Dolores Muñoz Vicente University of Salamanca, Department of Computer Science and Automation, mariado@usal.es María N. Moreno-García* University of Salamanca, Department of Computer Science and Automation, mmg@usal.es
Can One Predict a Drop Contact Angle?

The study of wetting phenomena is of great interest due to the multifaceted technological applications of hydrophobic and hydrophilic surfaces. The theoretical approaches proposed by Wenzel and later by Cassie and Baxter to describe the behavior of a droplet of water on a rough solid are extensively used and continuously updated to characterize the apparent contact angle of a droplet. However, the equilibrium hypothesis implied in these models means that they are not always predictive of experimental contact angles due to the strong metastabilities typically occurring in the wetting of heterogeneous surfaces. A predictive scheme for contact angles is thus urgently needed both to characterize a surface by contact angle measurements and to design super‐hydrophobic and ‐oleophobic surfaces with the desired properties, for example, contact angle hysteresis. In this work, a combination of Monte Carlo simulation and the string method is employed to calculate the free energy profile of a liquid droplet deposited on a pillared surface. For the analyzed surfaces, it is shown that there is only one minimum of the free energy that corresponds to the superhydrophobic wetting state while the wet state can present multiple minima. Furthermore, when the surface roughness decreases the amount of local minima observed in the free energy profile increases. The presented approach clarifies the origin of contact angle hysteresis providing quantitative tools for understanding and controlling wetting at structured surfaces.
Exploring Machine Teaching with Children

Iteratively building and testing machine learning models can help children develop creativity, flexibility, and comfort with machine learning and artificial intelligence. We explore how children use machine teaching interfaces with a team of 14 children (aged 7–13 years) and adult co-designers. Children trained image classifiers and tested each other's models for robustness. Our study illuminates how children reason about ML concepts, offering these insights for designing machine teaching experiences for children: (i) ML metrics (e.g. confidence scores) should be visible for experimentation; (ii) ML activities should enable children to exchange models for promoting reflection and pattern recognition; and (iii) the interface should allow quick data inspection (e.g. images vs. gestures).
Covariant formulation of refracted gravity

We propose a covariant formulation of refracted gravity (RG), a classical theory of gravity based on the introduction of gravitational permittivity (GP), a monotonic function of the local mass density, in the standard Poisson equation. GP mimics dark matter (DM) phenomenology. The covariant formulation of RG (CRG) that we propose belongs to the class of scalar-tensor theories, where the scalar field $\varphi$ has a self-interaction potential $V(\varphi)=-\Xi\varphi$, with $\Xi$ a normalization constant. We show that $\varphi$ is twice the GP in the weak-field limit. Far from a spherical source of density $\rho_s(r)$, the transition between the Newtonian and the RG regime appears below the acceleration scale $a_\Xi=(2\Xi-8\pi G\rho/\varphi)^{1/2}$, with $\rho=\rho_s+\rho_{bg}$, $\rho_{bg}$ being an isotropic and homogeneous background. In the limit $2\Xi\gg 8\pi G\rho/\varphi$, we obtain $a_\Xi\sim 10^{-10}$~m~s$^{-2}$. This is comparable to the acceleration $a_0$ originally introduced in MOND. From CRG, we also derived the modified Friedmann equations for an expanding, homogeneous, and isotropic universe. We find that the same scalar field that mimics DM also drives the accelerated expansion of the Universe. From the stress-energy tensor of $\varphi$, we derived the equation of state of a redshift-dependent effective dark energy (DE) $w_{DE}=p_{DE}/\rho_{DE}$. Current observational constraints on $w_{DE}$ and distance modulus data of SNIa suggest that $\Xi$ has a comparable value to the cosmological constant $\Lambda$ in the standard model. CRG, therefore, suggests a natural explanation of the known relation $a_0\sim \Lambda^{1/2}$ and appears to describe both the dynamics of cosmic structure and the expanding Universe with a single scalar field, highlighting a possible deep connection between phenomena currently attributed to DM and DE separately.
WRENCH: A Comprehensive Benchmark for Weak Supervision

Recent Weak Supervision (WS) approaches have had widespread success in easing the bottleneck of labeling training data for machine learning by synthesizing labels from multiple potentially noisy supervision sources. However, proper measurement and analysis of these approaches remain a challenge. First, datasets used in existing works are often private and/or custom, limiting standardization. Second, WS datasets with the same name and base data often vary in terms of the labels and weak supervision sources used, a significant"hidden"source of evaluation variance. Finally, WS studies often diverge in terms of the evaluation protocol and ablations used. To address these problems, we introduce a benchmark platform, WRENCH, for thorough and standardized evaluation of WS approaches. It consists of 22 varied real-world datasets for classification and sequence tagging; a range of real, synthetic, and procedurally-generated weak supervision sources; and a modular, extensible framework for WS evaluation, including implementations for popular WS methods. We use WRENCH to conduct extensive comparisons over more than 120 method variants to demonstrate its efficacy as a benchmark platform. The code is available at https://github.com/JieyuZ2/wrench.
Sextans: A Streaming Accelerator for General-Purpose Sparse-Matrix Dense-Matrix Multiplication

Sparse-Matrix Dense-Matrix multiplication (SpMM) is the key operator for a wide range of applications including scientific computing, graph processing, and deep learning. Architecting accelerators for SpMM is faced with three challenges - (1) the random memory accessing and unbalanced load in processing because of random distribution of elements in sparse matrices, (2) inefficient data handling of the large matrices which can not be fit on-chip, and (3) a non-general-purpose accelerator design where one accelerator can only process a fixed-size problem. In this paper, we present Sextans, an accelerator for general-purpose SpMM processing. Sextans accelerator features (1) fast random access using on-chip memory, (2) streaming access to off-chip large matrices, (3) PE-aware non-zero scheduling for balanced workload with an II=1 pipeline, and (4) hardware flexibility to enable prototyping the hardware once to support SpMMs of different size as a general-purpose accelerator. We leverage high bandwidth memory (HBM) for the efficient accessing of both sparse and dense matrices. In the evaluation, we present an FPGA prototype Sextans which is executable on a Xilinx U280 HBM FPGA board and a projected prototype Sextans-P with higher bandwidth competitive to V100 and more frequency optimization. We conduct a comprehensive evaluation on 1,400 SpMMs on a wide range of sparse matrices including 50 matrices from SNAP and 150 from SuiteSparse. We compare Sextans with NVIDIA K80 and V100 GPUs. Sextans achieves a 2.50x geomean speedup over K80 GPU, and Sextans-P achieves a 1.14x geomean speedup over V100 GPU (4.94x over K80). The code is available at https://github.com/linghaosong/Sextans.
Improved Evolutionary Generative Adversarial Networks

—Evolutionary generative adversarial networks (E-GAN) tries to alleviate mode collapse and gradient vanish that plague generative adversarial networks by introducing evolutionary computation. But because of the lack of a reasonable evaluation mechanism, it did not achieve its design purpose. And it contains only mutation operators in its evolutionary step, but not crossover operator which are equally common with it. In this paper, we ﬁrstly point out the shortcomings of the diversity ﬁtness function of E-GAN and propose a new function. Then we propose a universal crossover operator over knowledge distillation, which can be widely applied to evolutionary GANs and complement the missing crossover variation of E-GAN. Incorporating the ﬁtness function and crossover operator we design an evolutionary GAN framework named improved evolutionary generative adversarial networks (IE-GAN) and combine E-GAN to complete an algorithm implementation. Experiments on various datasets demonstrate the effectiveness of IE-GAN and show that our method is competitive in terms of generated samples quality and time efﬁciency.
Peripheral nucleon-nucleon scattering at next-to-next-to-leading order in SU(3) heavy baryon chiral perturbation theory

We calculate the complete T matrices of the elastic nucleon-nucleon scattering up to third order in SU(3) heavy baryon chiral perturbation theory. The phase shifts with orbital angular momentum L ≥ 2 and the mixing angles with J ≥ 2 are evaluated by using lowenergy constants that were extracted from the meson-baryon analysis. It turns out that our prediction is consistent with the empirical phase shifts and mixing angles data, and also the results from SU(2) heavy baryon chiral perturbation theory. The errors from the low-energy constants are analyzed in detail. Our calculation provides reliable evidence that the nucleon-nucleon interaction calculated in SU(3) heavy baryon chiral perturbation theory leads to reasonable predictions.
Computing Funnels Using Numerical Optimization Based Falsifiers*

In this paper, we present an algorithm that computes funnels along trajectories of systems of ordinary differential equations. A funnel is a time-varying set of states containing the given trajectory, for which the evolution from within the set at any given time stays in the funnel. Hence it generalizes the behavior of single trajectories to sets around them, which is an important task, for example, in robot motion planning. In contrast to approaches based on sum-of-squares programming, which poorly scale to high dimensions, our approach is based on falsification and tackles the funnel computation task directly, through numerical optimization. This approach computes accurate funnel estimates far more efficiently and leaves formal verification to the end, outside all funnel size optimization loops.
Three-dimensionality of mobile electrons at X-ray-irradiated LaAlO$_3$/SrTiO$_3$ interfaces

Effects of X-ray irradiation on the electronic structure of LaAlO$_3$/SrTiO$_3$ (LAO/STO) samples, grown at low oxygen pressure and post-annealed ex-situ till recovery of their stoichiometry, were investigated by soft-X-ray ARPES. The irradiation at low sample temperature below ~100K creates oxygen vacancies (VOs) injecting Ti t2g-electrons into the interfacial mobile electron system (MES). At this temperature the oxygen out-diffusion is suppressed, and the VOs are expected to appear mostly in the top STO layer. However, we observe a pronounced three-dimensional (3D) character of the X-ray generated MES in our samples, indicating its large extension into the STO depth, which contrasts to the purely two-dimensional (2D) character of the MES in standard stoichiometric LAO/STO samples. Based on self-interaction-corrected DFT calculations of the MES induced by VOs at the interface and in STO bulk, we discuss possible mechanisms of this puzzling three-dimensionality. They may involve VOs remnant in the deeper STO layers, photoconductivity-induced metallic states as well as more exotic mechanisms such as X-ray induced formation of Frenkel pairs.
Efﬁcient Migrations Between Storage Tiers with PrismDB

In recent years, emerging hardware storage technologies have focused on divergent goals: better performance or lower cost-per-bit. Correspondingly, data systems that employ these technologies are typically optimized either to be fast (but expensive) or cheap (but slow). We take a different approach: by architecting a storage engine to natively utilize two tiers of fast and low-cost storage technologies, we can achieve a Pareto-efﬁcient balance between performance and cost-per-bit. This paper presents the design and implementation of PrismDB, a novel key-value store that exploits two extreme ends of the spectrum of modern NVMe storage technologies (3D XPoint and QLC NAND) simultaneously. Unlike prior work that has retroﬁtted a data structure designed for a single tier to multi-tier storage, PrismDB is intrinsically designed for different storage layers. The key design contributions of PrismDB is a novel hybrid data structure for tiered storage, and an adaptive and lightweight migration mechanism, which is able to efﬁciently move objects between the storage tiers. Compared to the standard use of RocksDB on ﬂash in datacenters today, PrismDB’s average throughput on tiered storage is 3.3 × faster and its read tail latency is 2 × better, using equivalently-priced hardware.
N-LTP: An Open-source Neural Language Technology Platform for Chinese

We introduce N-LTP, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: lexical analysis (Chinese word segmentation, part-of-speech tagging, and named entity recognition), syntactic parsing (dependency parsing), and semantic parsing (semantic dependency parsing and semantic role labeling). Unlike the existing state-of-the-art toolkits, such as Stanza, that adopt an independent model for each task, N-LTP adopts the multi-task framework by using a shared pre-trained model, which has the advantage of capturing the shared knowledge across relevant Chinese tasks. In addition, a knowledge distillation method (Clark et al., 2019) where the single-task model teaches the multi-task model is further introduced to encourage the multi-task model to surpass its single-task teacher. Finally, we provide a collection of easy-to-use APIs and a visualization tool to make users to use and view the processing results more easily and directly. To the best of our knowledge, this is the first toolkit to support six Chinese NLP fundamental tasks. Source code, documentation, and pre-trained models are available at https://github.com/HIT-SCIR/ltp.
Dracaena arborea (Dracaenaceae) Increases Sexual Hormones and Sperm Parameters, Lowers Oxidative Stress, and Ameliorates Testicular Architecture in Rats with 3 Weeks of Experimental Varicocele

Varicocele is a disease characterized by an abnormal dilation of the pampiniform plexus that drains the testis. The main objective of this work was to evaluate the curative effects of aqueous and ethanolic extracts of Dracaena arborea on some reproductive and antioxidant markers in rats with experimental varicocele. Following varicocele induction, rats (5 per group) were randomly partitioned into untreated varicocele, vitamin E-treated (150 mg/kg), aqueous extract-treated (500 mg/kg), and ethanolic extract-treated (100 mg/kg) animals. Two other groups served as normal and sham-operated. After 2 or 4 weeks of treatments, body and sex organ weights, spermatozoa characteristics, antioxidant status, NO level, sex hormones, and testis histology were measured. Animals with 3 weeks of varicocele showed a significant (p < 0.05–0.001) decrease in body and sex organ weights, total proteins, sperm characteristics, testosterone concentration, SOD, catalase, and total peroxidase activities. An increase in the plasmatic FSH, LH, and testicular MDA and NO concentrations was also recorded. Moreover, marked disorganization of the testicular architecture was observed. Treatment with D. arborea significantly reversed these impairments due to varicocele. For instance, after 4 weeks, treatment with aqueous extract of D. arborea significantly (p < 0.05–0.001) increased testes and epididymis weights, sperm viability (89.12 ± 1.09 vs 68.22 ± 1.42), sperm density (148.50 ± 2.59 vs 110.25 ± 2.51), and sperm motility (68.16 ± 2.39 vs 55.88 ± 3.20) in the left side, compared with varicocele-untreated rats. The extract also significantly (p < 0.05–0.001) decreased malondialdehyde level (2.19 ± 0.04 vs 3.50 ± 0.13) but elevated catalase (0.97 ± 0.03 vs 0.55 ± 0.03), SOD (0.5 ± 0.03 vs 0.15 ± 0.03), and peroxidase (65.80 ± 2.9 vs 40.95 ± 2.44) activities. Present results showed that D. arborea extracts possess antioxidant effects and improve sperm quality in male rats with an existing varicocele.
Facilitating Adaptive Emotion Processing and Somatic Reappraisal via Sustained Mindful Interoceptive Attention

Emotions are by nature embodied, as the brain has evolved to quickly assess the emotional significance of stimuli and output signals to the body’s viscera and periphery to aid adaptive responses. Emotions involve both implicit bodily and explicit narrative processes, and patients may experience transdiagnostic distress when bodily signals are not attended to and holistically integrated with explicit narratives about experience. Similarly, therapists may be trained in more implicit body-based approaches (i.e., massage/bodywork, physical and occupational therapy, and nursing/medicine) or more explicit narrative-based approaches (i.e., psychotherapy), and may lack training in skills that integrate both levels of emotion processing to aid healing and growth. To address these gaps, we propose a framework where the bridge between implicit bodily sensations and explicit narratives lies in cultivating mindful awareness of bodily sensations associated with emotions. This process brings subjective awareness to notice inner body experience (or interoceptive awareness) that is often outside of conscious awareness, so that it may be understood and re-integrated in more adaptive ways, which we call somatic reappraisal. Using clinical theory and example vignettes, we present mindful interoceptive awareness for adaptive emotion processing as a framework to cultivate and enhance somatic reappraisal. Mindful interoceptive awareness brings more focused and sustained attention to inner body experience; likewise, internal sensations associated with emotions become more granular, vivid, and can shift in ways that facilitate somatic reappraisal. Learning to sustain interoceptive awareness when engaged with mindfulness qualities of nonjudgment and compassion promotes an experience where new associations between emotions, meanings, and memories can be made that generate insights that are holistic and integrative. A clinical vignette is used in this paper to provide examples of this approach in psychotherapy. An example script for use in mindfulness groups is included, and resources are suggested for clinicians to gain more experience. Mindful interoceptive awareness for adaptive emotion processing is a clinical process that can be learned and applied by a range of clinicians to treat mental and physical health conditions that may benefit greater embodied awareness.