Miracle-dz/UniFAR-FTUs huggingface dataset:
Entropy-Learned Hashing 10x Faster Hashing with Controllable Uniformity

Hashing is a widely used technique for creating uniformly random numbers from arbitrary input data. It is a core component in relational data systems, key-value stores, compilers, networks and many more areas used for a wide range of operations including indexing, partitioning, filters, and sketches. Due to both the computational and data heavy nature of hashing in such operations, numerous recent studies observe that hashing emerges as a core bottleneck in modern systems. For example, a typical complex database query (TPC-H) could spend 50% of its total cost in hash tables, while Google spends at least 2% of its total computational cost across all systems on the cloud in hash tables, resulting in a massive yearly footprint coming from just a single operation. In this paper we propose a new method, called Entropy-Learned Hashing, which reduces the computational cost of hashing by up to an order of magnitude. The key question we ask is “how much randomness is needed? ”: We look at hashing from a pseudorandom point of view, wherein hashing is viewed as extracting randomness from a data source to create random outputs and we show that state-of-the-art hash functions do too much work. Entropy-Learned Hashing 1) models and estimates the randomness (entropy) of the input data, and then 2) creates data-specific hash functions that use only the parts of the data that are needed to differentiate the outputs. Thus the resulting hash functions can minimize the amount of computation needed while we prove that they act similarly to traditional hash functions in terms of the uniformity of their outputs. We test Entropy-Learned Hashing across diverse and core hashing operations such as hash tables, Bloom filters, and partitioning and we observe an increase in throughput in the order of 3.7X, 4.0X, and 14X respectively compared to the best in-class hash functions
Steps Towards Real-world Ethics for Self-driving Cars: Beyond the Trolley Problem

Research on self-driving cars is transdisciplinary and its different aspects have attracted interest in general public debates as well as among specialists. To this day, ethical discourses are dominated by the Trolley Problem, a hypothetical ethical dilemma that is by construction unsolvable. It obfuscates much bigger real-world ethical challenges in the design, development, and operation of self-driving cars. We propose a systematic approach that connects processes, components, systems, and stakeholders to analyze the real-world ethical challenges for the ecology of socio-technological system of self-driving cars. We take a closer look at the regulative instruments, standards, design, and implementations of components, systems, and services and we present practical social and ethical challenges that must be met and that imply novel expectations for engineering in car industry.
Limitations of the Sherali-Adams Lift and Project System: Compromising Local and Global Arguments

We consider several Optimization problems that are originated in the study of linear systems capturing Satisfaction problems. We obtain lower bounds for the rank of the associated polytopes, and integrality gaps after linear many liftings of the Sherali-Adams Lift and Project system. Finally, we show how some of these results can be extended to the stronger Lift and Project system of Lasserre. Speciﬁcally, we show linear rank lower bounds for the Pigeonhole Principle, for Tseitin Tautology on random graphs, and for random k -LIN and k -CNF instances, k ≥ 3 . As an immediate corollary, we obtain tight integrality gaps for linear many rounds of Sherali-Adams for MAX-k -LIN and MAX-k -CNF, and a 7/6 integrality gap for V ERTEX C OVER . We reﬁne the connection between the Sherali-Adams system and probabilistic reasoning, as was laid forth by Fern´andez de la Vega and Kenyon-Mathieu and by Charikar, Makarychev and Makarychev, and supply a methodology which we call “between local and global” that deﬁnes probability distributions on small sets using a simple distribution over large, yet not-too-large sets.
The Effect of Two Methods of Child-Centered and Teacher-Centered on the Development of Gross Motor Skill of Third Grade Elementary Students

Paying attention to the motor skill training approach shows that the type and nature of teaching are one of the most important effective variables in motor skills training. The purpose of this study was to evaluate the effect of child-centered and teacher-centered methods on the development of the gross motor skill of thirdgrade elementary students. To do so, 53 female beginners with an average age of 11.9±16.0 volunteered for this study. After performing the pre-test, they were randomly divided into three experimental groups (deliberate play, deliberate practice, organized competition) and one control group. The groups practiced based on the educational method for 12 weeks and two sessions per week. The mid-test and post-test were carried out at the end of the sixth and twelfth weeks, respectively. The Bruininks-Oseretsky Test of Motor Proficiency (1978) was used to collect data, and then the analysis of variance with repeated measures at a significant level of P≤0.05 was utilized to analyze the data. The results indicated that the teaching method has been effective in improving the gross motor skill of 1. Email: f_rezaee@semnan.ac. ir 2. Email: bagherzad@ut.ac. ir 3. Email: msheikh@ut.ac. ir 4. Email: rhemayat@ut.ac. ir 5. Email: hominian@ut.ac. ir Rezaei: The Effect of Two Methods of Child-Centered and Teacher. 18 the students and its effectiveness in the organized competition group was more than that in other groups. Moreover, there was no significant difference between subscales of the running, balance, bilateral coordination and upper limb coordination in midand post-test, and the strength level was higher in the organized competition group than other groups in the post-test. As a result, it becomes clear that the teaching method is effective in the development of student’s gross motor skill. Keyword: Teaching Method, Deliberate Play, Deliberate Practice, Organized Competition, Gross Motor Skill
Research of Visualized 3d Substation Simulation Based on Virtual Reality Technology

The three-dimensional virtual reality technology was introduced into the visualized substation. The virtual substation models of main electrical equipment and the whole scene were built by using 3dsMax modeling software. After the design of special events response routing and editting the corresponding script language, users can interact with virtual objects in the scene model, resulting in feelings and experiences on the ground. Compared with the real substation, the virtual substation has the advantages of low cost, interactive operation safety, and is of great significance to improve the technical level of the operators.
Discretization and Re-synthesis: an alternative method to solve the Cocktail Party Problem

Deep learning based models have significantly improved the performance of speech separation with input mixtures like the cocktail party. Prominent methods (e.g., frequency-domain and time-domain speech separation) usually build regression models to predict the ground-truth speech from the mixture, using the masking-based design and the signal-level loss criterion (e.g., MSE or SI-SNR). This study demonstrates, for the first time, that the synthesis-based approach can also perform well on this problem, with great flexibility and strong potential. Specifically, we propose a novel speech separation/enhancement model based on the recognition of discrete symbols, and convert the paradigm of the speech separation/enhancement related tasks from regression to classification. By utilizing the synthesis model with the input of discrete symbols, after the prediction of discrete symbol sequence, each target speech could be re-synthesized. Evaluation results based on the WSJ0-2mix and VCTK-noisy corpora in various settings show that our proposed method can steadily synthesize the separated speech with high speech quality and without any interference, which is difficult to avoid in regression-based methods. In addition, with negligible loss of listening quality, the speaker conversion of enhanced/separated speech could be easily realized through our method.
Confining density functional approach for color superconducting quark matter and mesonic correlations

We present a novel relativistic density-functional approach to modeling quark matter with a mechanism to mimic confinement. The quasiparticle treatment of quarks provides their suppression due to large quark selfenergy already at the mean-field level. We demonstrate that our approach is equivalent to a chiral quark model with medium-dependent couplings. The dynamical restoration of the chiral symmetry is ensured by construction of the density functional. Beyond the mean field, quark correlations in the pseudoscalar channel are described within the Gaussian approximation. This explicitly introduces pionic states into the model. Their contribution to the thermodynamic potential is analyzed within the Beth–Uhlenbeck framework. The modification of the meson mass spectrum in the vicinity of thee (de)confinement transition is interpreted as the Mott transition. Supplemented with the vector repulsion and diquark pairing the model is applied to construct a hybrid quark-hadron EoS of cold compact-star matter. We study the connection of such a hybrid EoS with the stellar mass-radius relation and tidal deformability. The model results are compared to various observational constraints including the NICER radius measurement of PSR J0740+6620 and the tidal deformability constraint from GW170817. The model is shown to be consistent with the constraints, still allowing for further improvement by adjusting the vector repulsion and diquark pairing couplings.
Neural Architectures for Biological Inter-Sentence Relation Extraction

We introduce a family of deep-learning architectures for inter-sentence relation extraction, i.e., relations where the participants are not necessarily in the same sentence. We apply these architectures to an important use case in the biomedical domain: assigning biological context to biochemical events. In this work, biological context is defined as the type of biological system within which the biochemical event is observed. The neural architectures encode and aggregate multiple occurrences of the same candidate context mentions to determine whether it is the correct context for a particular event mention. We propose two broad types of architectures: the first type aggregates multiple instances that correspond to the same candidate context with respect to event mention before emitting a classification; the second type independently classifies each instance and uses the results to vote for the final class, akin to an ensemble approach. Our experiments show that the proposed neural classifiers are competitive and some achieve better performance than previous state of the art traditional machine learning methods without the need for feature engineering. Our analysis shows that the neural methods particularly improve precision compared to traditional machine learning classifiers and also demonstrates how the difficulty of inter-sentence relation extraction increases as the distance between the event and context mentions increase.
Quantum phase modulation with acoustic cavities and quantum dots

Fast, efficient, and low power modulation of light at microwave frequencies is crucial for chip-scale classical and quantum processing as well as for long-range networks of superconducting quantum processors. A successful approach to bridge the gap between microwave and optical photons has been to use intermediate platforms such as acoustic waves, which can couple efficiently to a variety of quantum systems. Here, we use gigahertz-frequency focusing surface acoustic wave cavities on GaAs that are piezo-electrically coupled to superconducting circuits and parametrically coupled, via strain, to photons scattered from InAs quantum dots . We demonstrate strong modulation of single photons with a half-wave voltage as low as 44 mV, and subnatural modulation sideband linewidths. These demonstrations pave the way for efficient and low-noise transduction of quantum information between microwave and optical domains.
A flexible Bayesian hierarchical modeling framework for spatially dependent peaks-over-threshold data

In this work, we develop a constructive modeling framework for extreme threshold exceedances in repeated observations of spatial fields, based on general product mixtures of random fields possessing light or heavy-tailed margins and various spatial dependence characteristics, which are suitably designed to provide high flexibility in the tail and at sub-asymptotic levels. Our proposed model is akin to a recently proposed Gamma-Gamma model using a ratio of processes with Gamma marginal distributions, but it possesses a higher degree of flexibility in its joint tail structure, capturing strong dependence more easily. We focus on constructions with the following three product factors, whose different roles ensure their statistical identifiability: a heavy-tailed spatially-dependent field, a lighter-tailed spatially-constant field, and another lighter-tailed spatially-independent field. Thanks to the model’s hierarchical formulation, inference may be conveniently performed based on Markov chain Monte Carlo methods. We leverage the Metropolis adjusted Langevin algorithm (MALA) with random block proposals for latent variables, as well as the stochastic gradient Langevin dynamics (SGLD) algorithm for hyperparameters, in order to fit our proposed model very efficiently in relatively high spatio-temporal dimensions, while simultaneously censoring non-threshold exceedances and performing spatial prediction at multiple sites. The censoring mechanism is applied to the spatially independent component, such that only univariate cumulative distribution functions have to be evaluated. We explore the theoretical propComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia. E-mails: rishikesh.yadav@kaust.edu. sa; raphael.huser@kaust.edu. sa INRAE, UR546 Biostatistics and Spatial Processes, 228, Route de l’Aérodrome, CS 40509, 84914 Avignon, France. E-mail: thomas.opitz@inra.fr ar X iv :2 11 2. 09 53 0v 1 [ st at . M E ] 1 7 D ec 2 02 1 erties of the novel model, and illustrate the proposed methodology by simulation and application to daily precipitation data from North-Eastern Spain measured at about 100 stations over the period 2011–2020.
Locating the $\gamma$-ray emitting region in the quasar 4C +01.28

F. Rösch,a,∗ M. Kadler, E. Ros, M. Gurwell, T. Hovatta, M. Kreter, f N. R. MacDonald and A. C. S. Readhead Institut für Theoretische Physik und Astrophysik, Universität Würzburg, Emil-Fischer-Str. 31, 97074 Würzburg, Germany Max-Planck-Institut für Radioastronomie, Auf dem Hügel 69, 53121 Bonn, Germany Center for Astrophysics Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA Finnish Centre for Astronomy with ESO (FINCA), University of Turku, FI-20014 Turku, Finland Aalto University Metsähovi Radio Observatory, Metsähovintie 114, FI-02540 Kylmälä, Finland f Centre for Space Research, North-West University, Private Bag X6001, Potchefstroom 2520, South Africa Owens Valley Radio Observatory, California Institute of Technology, Pasadena, CA 91125, USA E-mail: florian.roesch@physik.uni-wuerzburg.de
Sparse Coding with Multi-Layer Decoders using Variance Regularization

Sparse representations of images are useful in many computer vision applications. Sparse coding with an $l_1$ penalty and a learned linear dictionary requires regularization of the dictionary to prevent a collapse in the $l_1$ norms of the codes. Typically, this regularization entails bounding the Euclidean norms of the dictionary's elements. In this work, we propose a novel sparse coding protocol which prevents a collapse in the codes without the need to regularize the decoder. Our method regularizes the codes directly so that each latent code component has variance greater than a fixed threshold over a set of sparse representations for a given set of inputs. Furthermore, we explore ways to effectively train sparse coding systems with multi-layer decoders since they can model more complex relationships than linear dictionaries. In our experiments with MNIST and natural image patches, we show that decoders learned with our approach have interpretable features both in the linear and multi-layer case. Moreover, we show that sparse autoencoders with multi-layer decoders trained using our variance regularization method produce higher quality reconstructions with sparser representations when compared to autoencoders with linear dictionaries. Additionally, sparse representations obtained with our variance regularization approach are useful in the downstream tasks of denoising and classification in the low-data regime.
From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization

In the setting of federated optimization, where a global model is aggregated periodically, step asynchronism occurs when participants conduct model training by efficiently utilizing their computational resources. It is well acknowledged that step asynchronism leads to objective inconsistency under non-i.i. d. data, which degrades the model’s accuracy. To address this issue, we propose a new algorithm FedaGrac, which calibrates the local direction to a predictive global orientation. Taking advantage of the estimated orientation, we guarantee that the aggregated model does not excessively deviate from the global optimum while fully utilizing the local updates of faster nodes. We theoretically prove that FedaGrac holds an improved order of convergence rate than the state-of-the-art approaches and eliminates the negative effect of step asynchronism. Empirical results show that our algorithm accelerates the training and enhances the final accuracy.
Observability Estimate for the Wave Equation with Variable Coefficients

This paper is devoted to a study of observability estimate for the wave equation with variable coefficients $(h^{jk}(x))_{n\times n}$ ($n\in\mathbb{N})$. We consider both the observation point lies outside the domain and the observation point lies inside the domain. Based on a Carleman estimate for the ultra-hyperbolic operator and a delicate treatment of observation region, we obtain two observability estimates with explicit observability constants. The key improvements are: (1) we improve the requirement of waiting time $T$; (2) we improve the size of the observation region (see Fingure 1 and Fingure 2 for the case of $(h^{jk}(x))_{n\times n}=I_n)$ .
LRE Fault Detection and Isolation Based on Fuzzy Direction Neural Network

A fuzzy directions neural network used for fault detection and isolation (FDI) of a liquid rocket engine (LRE) is presented in this paper. Neural network utilizes fuzzy sets as engine fault classes. Each fuzzy set is an aggregate of fuzzy direction bodies. A fuzzy direction body is described by a direction vector, an included angle and two radii. FDI simulation of the turbo-pump fed liquid rocket engine demonstrates the strong qualities of the fuzzy direction neural network.
Semantic-Based Few-Shot Learning by Interactive Psychometric Testing

Few-shot classification tasks aim to classify images in query sets based on only a few labeled examples in support sets. Most studies usually assume that each image in a task has a single and unique class association. Under these assumptions, these algorithms may not be able to identify the proper class assignment when there is no exact matching between support and query classes. For example, given a few images of lions, bikes, and apples to classify a tiger. However, in a more general setting, we could consider the higher-level concept, the large carnivores, to match the tiger to the lion for semantic classification. Existing studies rarely considered this situation due to the incompatibility of label-based supervision with complex conception relationships. In this work, we advance the few-shot learning towards this more challenging scenario, the semantic-based few-shot learning, and propose a method to address the paradigm by capturing the inner semantic relationships using interactive psychometric learning. The experiment results on the CIFAR-100 dataset show the superiority of our method for the semantic-based few-shot learning compared to the baseline.
iGraphMatch: an R Package for the Analysis of Graph Matching

iGraphMatch is an R package for finding corresponding vertices between two graphs, also known as graph matching. The package implements three categories of prevalent graph matching algorithms including relaxation-based, percolation-based, and spectralbased, which are applicable to matching graphs under general settings: weighted directed graphs of different order and graphs of multiple layers. We provide versatile options to incorporate prior information in the form of seeds with or without noise and similarity scores. In addition, iGraphMatch provides functions to summarize the graph matching results in terms of several evaluation measures and visualize the matching performance. Finally, the package enables users to sample correlated random graph pairs from classic random graph models to generate data for simulations. This paper illustrates the practical applications of the package to the analysis of graph matching by detailed examples using real data from communication, neuron, and transportation networks.
Impedance matrix of a folded dipole pair under eleven configuration

A new decade wideband antenna, the Eleven antenna, has been developed during the last years. The basic configuration of Eleven antenna is two parallel dipoles separated by half wavelength above the ground plane, which is referred to as the eleven configuration. In order to cascade the dipoles in log-periodic array for obtaining wideband performance, folded dipoles are often used. The input impedance of shorted folded dipole in free space has been analysed by using two-mode method: a transmission line mode and an antenna mode. In the present study, the impedance matrix of a pair of folded dipoles as a two-port network under the eleven configuration will be analysed by including the mutual coupling among elements of folded dipoles into the two-mode method. As veri. cation, an Eleven antenna with one pair of folded dipoles has been analysed by the present method, modelled in a commercial software - CST MS and manufactured. The results by using the present method agree well with the measured and simulated one.
NOMA Inspired Interference Cancellation for Integrated Sensing and Communication

A non-orthogonal multiple access (NOMA) inspired integrated sensing and communication (ISAC) system is investigated. A dual-functional base station (BS) serves multiple communication users while sensing multiple targets, by trans-mitting the non-orthogonal superposition of the communication and sensing signals. A NOMA inspired interference cancellation scheme is proposed, where part of the dedicated sensing signal is treated as the virtual communication signals to be mitigated at each communication user via successive interference cancellation (SIC). Based on this framework, the transmitted communication and sensing signals are jointly optimized to match the desired sensing beampattern, while satisfying the minimum rate requirement and the SIC condition at the communication users. Then, the formulated non-convex optimization problem is solved by invoking the successive convex approximation (SCA) to obtain a near-optimal solution. The numerical results show the proposed NOMA-inspired ISAC system can achieve better performance than the conventional ISAC system and comparable performance to the ideal ISAC system where all sensing interference is assumed to be removed unconditionally.