Miracle-dz/UniFAR-FTUs huggingface dataset:
SemMT: A Semantic-Based Testing Approach for Machine Translation Systems

Machine translation has wide applications in daily life. In mission-critical applications such as translating official documents, incorrect translation can have unpleasant or sometimes catastrophic consequences. This motivates recent research on the testing methodologies for machine translation systems. Existing methodologies mostly rely on metamorphic relations designed at the textual level (e.g., Levenshtein distance) or syntactic level (e.g., distance between grammar structures) to determine the correctness of translation results. However, these metamorphic relations do not consider whether the original and the translated sentences have the same meaning (i.e., semantic similarity). To address this problem, in this article we propose SemMT, an automatic testing approach for machine translation systems based on semantic similarity checking. SemMT applies round-trip translation and measures the semantic similarity between the original and the translated sentences. Our insight is that the semantics concerning logical relations and quantifiers in sentences can be captured by regular expressions (or deterministic finite automata) where efficient semantic equivalence/similarity checking algorithms can be applied. Leveraging the insight, we propose three semantic similarity metrics and implement them in SemMT. We compared SemMT with related state-of-the-art testing techniques, demonstrating the effectiveness of mistranslation detection. The experiment results show that SemMT outperforms existing metrics, achieving an increase of 34.2% and 15.4% on accuracy and F-score, respectively. We also study the possibility of further enhancing the performance by combining various metrics. Finally, we discuss a solution to locate the suspicious trip in round-trip translation, which provides hints for bug diagnosis.
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning

While many deep learning methods have seen significant success in tackling the problem of domain adaptation and few-shot learning separately, far fewer methods are able to jointly tackle both problems in Cross-Domain Few-Shot Learning (CD-FSL). This problem is exacerbated under sharp domain shifts that typify common computer vision applications. In this paper, we present a novel, flexible and effective method to address the CD-FSL problem. Our method, called Score-based Meta Transfer-Learning (SB-MTL), combines transfer-learning and meta-learning by using a MAML-optimized feature encoder and a score-based Graph Neural Network. First, we have a feature encoder with specific layers designed to be fine-tuned. To do so, we apply a first-order MAML algorithm to find good initializations. Second, instead of directly taking the classification scores after fine-tuning, we interpret the scores as coordinates by mapping the pre-softmax classification scores onto a metric space. Subsequently, we apply a Graph Neural Network to propagate label information from the support set to the query set in our score-based metric space. We test our model on the Broader Study of Cross-Domain Few-Shot Learning (BSCD-FSL) benchmark, which includes a range of target domains with highly varying dissimilarity to the miniImagenet source domain. We observe significant improvements in accuracy across 5, 20 and 50 shot, and on the four target domains. In terms of average accuracy, our model outperforms previous transfer-learning methods by 5.93% and previous meta-learning methods by 14.28%.
Human Tactile Gesture Interpretation for Robotic Systems

Human-robot interactions are less efficient and communicative than human-to-human interactions, and a key reason is a lack of informed sense of touch in robotic systems. Existing literature demonstrates robot success in executing handovers with humans, albeit with substantial reliance on external sensing or with primitive signal processing methods, deficient compared to the rich set of information humans can detect. In contrast, we present models capable of distinguishing between four classes of human tactile gestures at a robot's end effector, using only a non-collocated six-axis force sensor at the wrist. Due to the absence in the literature, this work describes 1) the collection of an extensive force dataset characterized by human-robot contact events, and 2) classification models informed by this dataset to determine the nature of the interaction. We demonstrate high classification accuracies among our proposed gesture definitions on a test set, emphasizing that neural network classifiers on the raw data outperform several other combinations of algorithms and feature sets.
A small note on variation in segmentation annotations

We report on the results of a small crowdsourcing experiment conducted at a workshop on machine learning for segmentation held at the Danish Bio Imaging network meeting 2020. During the workshop we asked participants to manually segment mitochondria in three 2D patches. The aim of the experiment was to illustrate that manual annotations should not be seen as the ground truth, but as a reference standard that is subject to substantial variation. In this note we show how the large variation we observed in the segmentations can be reduced by removing the annotators with worst pair-wise agreement. Having removed the annotators with worst performance, we illustrate that the remaining variance is semantically meaningful and can be exploited to obtain segmentations of cell boundary and cell interior.
Make One-Shot Video Object Segmentation Efficient Again

Video object segmentation (VOS) describes the task of segmenting a set of objects in each frame of a video. In the semi-supervised setting, the first mask of each object is provided at test time. Following the one-shot principle, fine-tuning VOS methods train a segmentation model separately on each given object mask. However, recently the VOS community has deemed such a test time optimization and its impact on the test runtime as unfeasible. To mitigate the inefficiencies of previous fine-tuning approaches, we present efficient One-Shot Video Object Segmentation (e-OSVOS). In contrast to most VOS approaches, e-OSVOS decouples the object detection task and predicts only local segmentation masks by applying a modified version of Mask R-CNN. The one-shot test runtime and performance are optimized without a laborious and handcrafted hyperparameter search. To this end, we meta learn the model initialization and learning rates for the test time optimization. To achieve optimal learning behavior, we predict individual learning rates at a neuron level. Furthermore, we apply an online adaptation to address the common performance degradation throughout a sequence by continuously fine-tuning the model on previous mask predictions supported by a frame-to-frame bounding box propagation. e-OSVOS provides state-of-the-art results on DAVIS 2016, DAVIS 2017, and YouTube-VOS for one-shot fine-tuning methods while reducing the test runtime substantially. Code is available at https://github.com/dvl-tum/e-osvos.
Self-supported nickel sulfide derived from nickel foam for hydrogen evolution and oxygen evolution reaction: effect of crystal phase switching

Designing and fabricating economically viable, high active and stable electrocatalysts play an important role for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Crystal phase is the crucial factor that governs the electrochemical property and electrocatalytic reaction pathways. Here, a one-step nickel foam derived sulfidation method was presented to synthesize self-supported NiS2 and Ni3S2. The crystal phase-dependent chemical properties related to electrocatalytic behavior were evaluated by a series of advanced characterization and density functional theory calculations. Overall, the self-supported Ni3S2 shows high electrochemical activity towards both HER and OER in alkaline conditions, which afford the current density of 10 mA cm−2 with overpotentials of 245 mV for OER and 123 mV for HER, respectively. When employed the self-supported Ni3S2 as the bifunctional electrocatalysts for overall water splitting, the entire device provides the current density of 10 mA cm−2 at 1.61 V. These results indicate that the electrocatalytic properties can be exert greater improved by controlling the crystal phase, offering the prospect for advanced materials design and development.
End to End ASR System with Automatic Punctuation Insertion

Recent Automatic Speech Recognition systems have been moving towards end-to-end systems that can be trained together. Numerous techniques that have been proposed recently enabled this trend, including feature extraction with CNNs, context capturing and acoustic feature modeling with RNNs, automatic alignment of input and output sequences using Connectionist Temporal Classifications, as well as replacing traditional n-gram language models with RNN Language Models. Historically, there has been a lot of interest in automatic punctuation in textual or speech to text context. However, there seems to be little interest in incorporating automatic punctuation into the emerging neural network based end-to-end speech recognition systems, partially due to the lack of English speech corpus with punctuated transcripts. In this study, we propose a method to generate punctuated transcript for the TEDLIUM dataset using transcripts available from this http URL. We also propose an end-to-end ASR system that outputs words and punctuations concurrently from speech signals. Combining Damerau Levenshtein Distance and slot error rate into DLev-SER, we enable measurement of punctuation error rate when the hypothesis text is not perfectly aligned with the reference. Compared with previous methods, our model reduces slot error rate from 0.497 to 0.341.
Artificial Intelligence Accelerators based on Graphene Optoelectronic Devices

Optical and optoelectronic approaches of performing matrix-vector multiplication (MVM) operations have shown the great promise of accelerating machine learning (ML) algorithms with unprecedented performance. The incorporation of nanomaterials into the system can further improve the performance thanks to their extraordinary properties, but the non-uniformity and variation of nanostructures in the macroscopic scale pose severe limitations for large-scale hardware deployment. Here, we report a new optoelectronic architecture consisting of spatial light modulators and photodetector arrays made from graphene to perform MVM. The ultrahigh carrier mobility of graphene, nearly-zero-power-consumption electro-optic control, and extreme parallelism suggest ultrahigh data throughput and ultralow-power consumption. Moreover, we develop a methodology of performing accurate calculations with imperfect components, laying the foundation for scalable systems. Finally, we perform a few representative ML algorithms, including singular value decomposition, support vector machine, and deep neural networks, to show the versatility and generality of our platform.
Multi-mode Core Tensor Factorization based Low-Rankness and Its Applications to Tensor Completion

Low-rank tensor completion has been widely used in computer vision and machine learning. This paper develops a novel multi-modal core tensor factorization (MCTF) method combined with a tensor low-rankness measure and a better nonconvex relaxation form of this measure (NC-MCTF). The proposed models encode low-rank insights for general tensors provided by Tucker and T-SVD, and thus are expected to simultaneously model spectral low-rankness in multiple orientations and accurately restore the data of intrinsic low-rank structure based on few observed entries. Furthermore, we study the MCTF and NC-MCTF regularization minimization problem, and design an effective block successive upper-bound minimization (BSUM) algorithm to solve them. This efficient solver can extend MCTF to various tasks, such as tensor completion. A series of experiments, including hyperspectral image (HSI), video and MRI completion, confirm the superior performance of the proposed method.
Low field electron mobility in GaN

Temperature and doping dependencies of electron mobility in GaN have been calculated using an iterative technique. The following scattering mechanisms, i.e., impurity, polar optical phonon, acoustic phonon, piezoelectric, and electron plasmon are included in the calculation. Ionized impurity scattering has been treated beyond the Born approximation using a phase-shift analysis. The compensation ratio is used as a parameter with a realistic charge neutrality condition. Comparisons with experimental data confirm the present calculation over a wide range of temperatures and electron concentrations. Mobility and electron concentration data from Hall measurements reveal a degenerate layer at the GaN-substrate interface. This degenerate layer affects the bulk mobility and electron concentration and needs to be accounted for in order to extract reliable experimental values of the bulk electron mobility.
Object-Driven Active Mapping for More Accurate Object Pose Estimation and Robotic Grasping

This paper presents the first active object mapping framework for complex robotic grasping tasks. The framework is built on an object SLAM system integrated with a simultaneous multi-object pose estimation process. Aiming to reduce the observation uncertainty on target objects and increase their pose estimation accuracy, we also design an object-driven exploration strategy to guide the object mapping process. By combining the mapping module and the exploration strategy, an accurate object map that is compatible with robotic grasping can be generated. Quantitative evaluations also show that the proposed framework has a very high mapping accuracy. Manipulation experiments, including object grasping, object placement, and the augmented reality, significantly demonstrate the effectiveness and advantages of our proposed framework.
Kinetics of crystallization and morphology of poly(pivalolactone): regime II .fwdarw. III transition and nucleation constants

Cristallisation a partir de l'etat fondu entre 180 et 218 o C; formation de spherolites sur la plupart de ce domaine, mais formation de structures de type axialite aux temperatures les plus elevees. Transition du regime II au regime III vers 203 o C. Determination et discussion des valeurs d'energie libre de surface laterale et de surface de repli
Patient similarity: methods and applications

Patient similarity analysis is important in health care applications. It takes patient information such as their electronic medical records and genetic data as input and computes the pairwise similarity between patients. Procedures of typical a patient similarity study can be divided into several steps including data integration, similarity measurement, and neighborhood identification. And according to an analysis of patient similarity, doctors can easily find the most suitable treatments. There are many methods to analyze the similarity such as cluster analysis. And during machine learning become more and more popular, Using neural networks such as CNN is a new hot topic. This review summarizes representative methods used in each step and discusses applications of patient similarity networks especially in the context of precision medicine.
A semi-parametric model for target localization in distributed systems.

Distributed systems serve as a key technological infrastructure for monitoring diverse systems across space and time. Examples of their widespread applications include: precision agriculture, surveillance, ecosystem and physical infrastructure monitoring, animal behavior and tracking, disaster response and recovery to name a few. Such systems comprise of a large number of sensor devices at fixed locations, wherein each individual sensor obtains measurements that are subsequently fused and processed at a central processing node. A key problem for such systems is to detect targets and identify their locations, for which a large body of literature has been developed focusing primarily on employing parametric models for signal attenuation from target to device. In this paper, we adopt a nonparametric approach that only assumes that the signal is nonincreasing as function of the distance between the sensor and the target. We propose a simple tuning parameter free estimator for the target location, namely, the simple score estimator (SSCE). We show that the SSCE is $\sqrt{n}$ consistent and has a Gaussian limit distribution which can be used to construct asymptotic confidence regions for the location of the target. We study the performance of the SSCE through extensive simulations, and finally demonstrate an application to target detection in a video surveillance data set.
On effective $\epsilon$-integrality in orbits of rational maps over function fields and multiplicative dependence

We give effective bounds for the set quasi-integral points in orbits of non-isotrivial rational maps over function fields under some conditions, generalizing previous work of Hsia and Silverman (2011) for orbits over function fields of characteristic zero. We then use this to prove finiteness results for algebraic functions whose orbit under a rational function has multiplicative dependent elements modulo rings of $S$-integers, generalizing recent results over number fields.
Full-Resolution Correspondence Learning for Image Translation

We present the full-resolution correspondence learning for cross-domain images, which aids image translation. We adopt a hierarchical strategy that uses the correspondence from coarse level to guide the finer levels. In each hierarchy, the correspondence can be efficiently computed via PatchMatch that iteratively leverages the matchings from the neighborhood. Within each PatchMatch iteration, the ConvGRU module is employed to refine the current correspondence considering not only the matchings of larger context but also the historic estimates. The proposed GRU-assisted PatchMatch is fully differentiable and highly efficient. When jointly trained with image translation, full-resolution semantic correspondence can be established in an unsupervised manner, which in turn facilitates the exemplar-based image translation. Experiments on diverse translation tasks show our approach performs considerably better than state-of-the-arts on producing high-resolution images.
Dark‐Field Electron Microscopy: II. Studies of colloidal carbon

Several commercial carbon blacks were examined with dark‐field illumination in the electron microscope. Anomalous intensities in the dark‐field images are interpreted as resulting from coherent scattering by parallel layer groups of graphitic planes, which were previously identified by Biscoe and Warren using x‐ray diffraction. The dark‐field micrographs indicate that in a nonreinforcing and a semireinforcing black, the parallel layer groups near the surface of the roughly spherical particles are oriented with their representative vector approximately normal to the surface. No definite evidence was obtainable for such an arrangement in a reinforcing channel black with average particle diameter about 300A. After prolonged heat treatment to the point where graphitization occurs the channel black exhibits numerous diffraction images in darkfield, indicating the presence of crystals with dimensions in the range 20 to 90A.
S-Matrix and Anomaly of de Sitter

S-matrix formulation of gravity excludes de Sitter vacua. In particular, this is organic to string theory. The S-matrix constraint is enforced by an anomalous quantum break-time proportional to the inverse values of gravitational and/or string couplings. Due to this, de Sitter can satisfy the conditions for a valid vacuum only at the expense of trivializing the graviton and closed-string S-matrices. At non-zero gravitational and string couplings, de Sitter is deformed by corpuscular 1/N effects, similarly to Witten–Veneziano mechanism in QCD with N colors. In this picture, an S-matrix formulation of Einstein gravity, such as string theory, nullifies an outstanding cosmological puzzle. We discuss possible observational signatures which are especially interesting in theories with a large number of particle species. Species can enhance the primordial quantum imprints to potentially observable level even if the standard inflaton fluctuations are negligible.
Oxidative stress and decreased tissue HSP70 are involved in the genesis of sepsis: HSP70 as a therapeutic target

Sepsis is a systemic infection that causes multiple organ dysfunction. HSP70 is a protein responsive to cell stress, in particular oxidative stress. Therefore, this literature review sought to investigate the roles of HSP70 and oxidative stress in the pathophysiology of sepsis and the possibility of HSP70 as a therapeutic target. HSP70 exerts a protective effect when located in cells (iHSP70), and its decrease, as well as its increase in the extracellular environment (eHSP70), under oxidative stress is a biomarker of sepsis severity. In addition, therapies that increase iHSP70 and treatment with HSP70 promote sepsis improvement.
Using Serious Games for Antismoking Health Campaigns: Experimental Study

Background Serious games for health have been gaining in popularity among scholars and practitioners. However, there remain a few questions to be addressed. Objective This study tests the effects of a serious game and fear appeals on smoking-related outcomes. More specifically, this research aims to understand how serious games function as a more effective vehicle for a health campaign than a traditional medium, such as a print-based pamphlet. Further, while serious games utilize a variety of persuasive strategies in the game’s content, it is not clear whether fear appeals, which are widely used persuasive-message strategies for health, can be an effective strategy in serious games. Thus, we are testing the effect of fear appeals in a serious game. Methods We created a computer game and a print brochure to educate participants about the risks of smoking. More specifically, a flash-based single-player game was developed in which players were asked to avoid cigarettes in the gameplay context. We also developed an online brochure based on existing smoking-related brochures at a university health center; antismoking messages on the computer game and in the brochure were comparable. Then, an experiment using a 2 (media type: game vs. print) x 2 (fearful image: fear vs. no-fear) between-subjects design was conducted. The study recruitment was announced to undergraduate students enrolled in a large, public Midwestern university in the United States. After a screening test, a total of 72 smokers, who reported smoking in the past 30 days, participated in the experiment. Results Overall, gameplay, when compared to print-based pamphlets, had greater impacts on attitudes toward smoking and the intention to quit smoking. Further, the game’s persuasive effects were especially pronounced when messages contained fear appeals. When fearful images were presented, participants in the game condition reported significantly more negative attitudes toward social smoking than those in the print condition [F(1,67)=7.28; P=. 009; ηp2=0.10]. However, in the no-fear condition, there was no significant difference between the conditions [F(1,67)=0.25; P=. 620]. Similarly, the intention to quit smoking [F(1,67)=4.64; P=. 035; ηp2=0.07] and susceptibility [F(1,67)=6.92; P=. 011; ηp2=0.09] were also significantly different between the conditions, but only when fear appeals were used. Conclusions This study extends fear appeal research by investigating the effects of different media types. It offers empirical evidence that a serious game can be an effective vehicle for fear appeals.