Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental study for leptonic and semileptonic decays in the charm sector

Leptonic and semileptonic decays in the charm sector have been well studied in recent years. With the largest data sample near $D\bar D$ threshold, precision measurements of leptonic and semileptonic decays of charm meson and baryon are perfromed at BESIII. Test for letpon flavor universality is also performed. Sensitivity for rare leptonic and semileptonic charm decays is significantly improved taking advantage of the huge statistics in LHCb and the $B$ factories.
Computation of forces and torque in electric machines

This paper deals with computational analysis of cogging torque in small PM motors. A new approach has been implemented in calculating electromagnetic torque using finite element analysis. Many examples have been used to verify these calculations. Torque profiles for the cases of many small HDD motors are presented for the verification of results obtained. Test results on spindle motors show that the method is accurate.
Whiz: A Fast and Flexible Data Analytics System

Today's data analytics frameworks are compute-centric, with analytics execution almost entirely dependent on the pre-determined physical structure of the high-level computation. Relegating intermediate data to a second class entity in this manner hurts flexibility, performance, and efficiency. We present Whiz, a new analytics framework that cleanly separates computation from intermediate data. It enables runtime visibility into data via programmable monitoring, and data-driven computation (where intermediate data values drive when/what computation runs) via an event abstraction. Experiments with a Whiz prototype on a large cluster using batch, streaming, and graph analytics workloads show that its performance is 1.3-2x better than state-of-the-art.
Countably and entropy expansive homeomorphisms with the shadowing property

We discuss the dynamics beyond topological hyperbolicity considering homeomorphisms satisfying the shadowing property and generalizations of expansivity. It is proved that transitive countably expansive homeomorphisms satisfying the shadowing property are expansive in the set of transitive points. This is in contrast with pseudo-Anosov diffeomorphisms of the two-dimensional sphere that are transitive, cw-expansive, satisfy the shadowing property but the dynamical ball in each transitive point contains a Cantor subset. We exhibit examples of countably expansive homeomorphisms that are not finite expansive, satisfy the shadowing property and admits an infinite number of chain-recurrent classes. We further explore the relation between countable and entropy expansivity and prove that for surface homeomorphisms $f:S\to S$ satisfying the shadowing property and $\Omega(f)=S$, both countably expansive and entropy cw-expansive are equivalent to being topologically conjugate to an Anosov diffeomorphism.
A new strategy for estimating photometric redshifts of quasars

Based on the SDSS and SDSS-WISE quasar datasets, we put forward two schemes to estimate the photometric redshifts of quasars. Our schemes are based on the idea that the samples are firstly classified into subsamples by a classifier and then a photometric redshift estimation of different subsamples is performed by a regressor. Random Forest is adopted as the core algorithm of the classifiers, while Random Forest and kNN are applied as the key algorithms of regressors. The samples are divided into two subsamples and four subsamples, depending on the redshift distribution. The performances based on different samples, different algorithms and different schemes are compared. The experimental results indicate that the accuracy of photometric redshift estimation for the two schemes generally improves to some extent compared to the original scheme in terms of the percents in |Δz|1+zi<0.1 and |Δz|1+zi<0.2 and mean absolute error. Only given the running speed, kNN shows its superiority to Random Forest. The performance of Random Forest is a little better than or comparable to that of kNN with the two datasets. The accuracy based on the SDSS-WISE sample outperforms that based on the SDSS sample no matter by kNN or by Random Rorest. More information from more bands is considered and helpful to improve the accuracy of photometric redshift estimation. Evidently, it can be found that our strategy to estimate photometric redshift is applicable and may be applied to other datasets or other kinds of objects. Only talking about the percent in |Δz|1+zi<0.3 , there is still large room for further improvement in the photometric redshift estimation.
CUNI System for the WMT19 Robustness Task

We present our submission to the WMT19 Robustness Task. Our baseline system is the Charles University (CUNI) Transformer system trained for the WMT18 shared task on News Translation. Quantitative results show that the CUNI Transformer system is already far more robust to noisy input than the LSTM-based baseline provided by the task organizers. We further improved the performance of our model by fine-tuning on the in-domain noisy data without influencing the translation quality on the news domain.
A Geometry-aided Message Passing Method for AoA-Based Short Range MIMO Channel Estimation

Short range channels commonly arise in millimeter wave (mmWave) wearable settings, where the length of the antenna arrays can be comparable to the distance between the radios. Conventional mmWave MIMO channel estimation techniques based on the far field assumption may perform poorly in short range settings due to the large angular spread and, hence, high available rank. We propose a geometry-aided message passing algorithm that exploits structure in short range line-of-sight (LoS) channels for spatial sub-Nyquist channel estimation. Our approach parametrizes the channel using angle-of-arrivals (AoAs) that are locally defined for subarrays of an antenna array. Furthermore, it leverages the dependencies between the local AoAs using factors based on the array geometry. We show that the LoS MIMO channel can be reconstructed using the derived local AoA estimates and the known transceiver geometry. The proposed approach achieves a reasonable rate with greatly reduced pilot transmissions when compared to exhaustive beam search-based local AoA estimation.
Cluster Computing Review

Machines for Highly Parallel Computing, Univ of Leeds, 14th−16th April 1993. [31] R Miller, A Library for Bulk−synchronous Parallel Computing, British Computer Society Parallel Processing Specialist Group, General Purpose Parallel Computing, December 1993. [32] R Miller and J Reed, The Oxford BSP Library: Users’ Guide, version 1.0, Oxford Parallel, 1993. [33] The OSF Distributed Computing Environment, Overview − URL http://www.osf. org/comm/lit/OSF−DCE−PD−1090−4.html [34] J. Arabe, A. Beguelin, B. Lowekamp, E. Seligman, M. Starkey, and P. Stephan Dome: Parallel programming in a heterogeneous multi−user environment, Carnegie Mellon University, March 1995. Submitted for presentation at Supercomputing ’95. [35] E. Seligman and A. Beguelin, High−Level Fault Tolerance in Distributed Programs, Technical Report CMU−CS−94−223, School of Computer, Science, Carnegie Mellon University, December 199 [36] GLU Programmer’s Guide v0.9, SRI CSL Technical Report 94−06, 1994. [37] LAM Overview − URL http://www.osc. edu/Lam/lam/overview.html [38] T. Anderson, D. Culler, D. Paterson, et. at. , A Case for Networks of Workstations (NOW), Computer Science Division, EECS Department, University of California, Berkeley, 1994. [39] A. Geist, A. Beguelin, J. Dongarra, W. Jiang, R. Manchek and V. Sunderam, PVM: Parallel Virtual Machine − A Users’ Guide and Tutorial for Networked Parallel Computing, Scientific and Engineering Series, The MIT Press, ISBN 0−262−57108−0 [40] M. A. Blumrich, K. Li, R. Alpert, C. Dubnicki, E. W. Felten, and J. Sandberg, Virtual Memory Mapped Network Interface for the SHRIMP Multicomputer, Int’l Symposium on Computer Architecture, April 1994, pp. 142 − 153. [41] I. McLean, A Multiple pager approach to distributed memory coherence. Dec 1994. URL http://info.mcc. ac.uk/MultiComp/reports.html [42] M. Kelly, The Method Of Imaginary Machines, a Systems’ Analysis Tool. http://www.genias. de/genias/english/codine/cluster−review.html Page: 88
Exoplanets and University-Industry Collaboration

A brief review is given of a university outreach programme by a commercial organisation, which uses the Kepler exoplanet data. Key insights derived from this research are presented, along with discussion of the benefi ts and challenges of such a collaboration between industry and academia. It is hoped that this account will be an inspiring example for others to emulate.
Metastability of the proximal point algorithm with multi-parameters

In this article we use techniques of proof mining to analyse a result, due to Yonghong Yao and Muhammad Aslam Noor, concerning the strong convergence of a generalized proximal point algorithm which involves multiple parameters. Yao and Noor's result ensures the strong convergence of the algorithm to the nearest projection point onto the set of zeros of the operator. Our quantitative analysis, guided by Fernando Ferreira and Paulo Oliva's bounded functional interpretation, provides a primitive recursive bound on the metastability for the convergence of the algorithm, in the sense of Terence Tao. Furthermore, we obtain quantitative information on the asymptotic regularity of the iteration. The results of this paper are made possible by an arithmetization of the $\limsup$.
A Robust Biped Locomotion Based on Linear-Quadratic-Gaussian Controller and Divergent Component of Motion

Generating robust locomotion for a humanoid robot in the presence of disturbances is difficult because of its high number of degrees of freedom and its unstable nature. In this paper, we used the concept of Divergent Component of Motion (DCM) and propose an optimal closed-loop controller based on Linear-Quadratic-Gaussian to generate a robust and stable walking for humanoid robots. The biped robot dynamics has been approximated using the Linear Inverted Pendulum Model (LIPM). Moreover, we propose a controller to adjust the landing location of the swing leg to increase the withstanding level of the robot against a severe external push. The performance and also the robustness of the proposed controller is analyzed and verified by performing a set of simulations using MATLAB. The simulation results showed that the proposed controller is capable of providing a robust walking even in the presence of disturbances and in challenging situations.
Neural Stored-program Memory

Neural networks powered with external memory simulate computer behaviors. These models, which use the memory to store data for a neural controller, can learn algorithms and other complex tasks. In this paper, we introduce a new memory to store weights for the controller, analogous to the stored-program memory in modern computer architectures. The proposed model, dubbed Neural Stored-program Memory, augments current memory-augmented neural networks, creating differentiable machines that can switch programs through time, adapt to variable contexts and thus resemble the Universal Turing Machine. A wide range of experiments demonstrate that the resulting machines not only excel in classical algorithmic problems, but also have potential for compositional, continual, few-shot learning and question-answering tasks.
A Deep Generative Model for Code-Switched Text

Code-switching, the interleaving of two or more languages within a sentence or discourse is pervasive in multilingual societies. Accurate language models for code-switched text are critical for NLP tasks. State-of-the-art data-intensive neural language models are difficult to train well from scarce language-labeled code-switched text. A potential solution is to use deep generative models to synthesize large volumes of realistic code-switched text. Although generative adversarial networks and variational autoencoders can synthesize plausible monolingual text from continuous latent space, they cannot adequately address code-switched text, owing to their informal style and complex interplay between the constituent languages. We introduce VACS, a novel variational autoencoder architecture specifically tailored to code-switching phenomena. VACS encodes to and decodes from a two-level hierarchical representation, which models syntactic contextual signals in the lower level, and language switching signals in the upper layer. Sampling representations from the prior and decoding them produced well-formed, diverse code-switched sentences. Extensive experiments show that using synthetic code-switched text with natural monolingual data results in significant (33.06\%) drop in perplexity.
Performance Comparison Between OpenCV Built in CPU and GPU Functions on Image Processing Operations

Image Processing is a specialized area of Digital Signal Processing which contains various mathematical and algebraic operations such as matrix inversion, transpose of matrix, derivative, convolution, Fourier Transform etc. Operations like those require higher computational capabilities than daily usage purposes of computers. At that point, with increased image sizes and more complex operations, CPUs may be unsatisfactory since they use Serial Processing by default. GPUs are the solution that come up with greater speed compared to CPUs because of their Parallel Processing/Computation nature. A parallel computing platform and programming model named CUDA was created by NVIDIA and implemented by the graphics processing units (GPUs) which were produced by them. In this paper, computing performance of some commonly used Image Processing operations will be compared on OpenCV's built in CPU and GPU functions that use CUDA.
Hardware-friendly Neural Network Architecture for Neuromorphic Computing

The hardware-software co-optimization of neural network architectures is becoming a major stream of research especially due to the emergence of commercial neuromorphic chips such as the IBM Truenorth and Intel Loihi. Development of specific neural network architectures in tandem with the design of the neuromorphic hardware considering the hardware constraints will make a huge impact in the complete system level application. In this paper, we study various neural network architectures and propose one that is hardware-friendly for a neuromorphic hardware with crossbar array of synapses. Considering the hardware constraints, we demonstrate how one may design the neuromorphic hardware so as to maximize classification accuracy in the trained network architecture, while concurrently, we choose a neural network architecture so as to maximize utilization in the neuromorphic cores. We also proposed a framework for mapping a neural network onto a neuromorphic chip named as the Mapping and Debugging (MaD) framework. The MaD framework is designed to be generic in the sense that it is a Python wrapper which in principle can be integrated with any simulator tool for neuromorphic chips.
A data-driven approach to sampling matrix selection for compressive sensing

Sampling is a fundamental aspect of any implementation of compressive sensing. Typically, the choice of sampling method is guided by the reconstruction basis. However, this approach can be problematic with respect to certain hardware constraints and is not responsive to domain-specific context. We propose a method for defining an order for a sampling basis that is optimal with respect to capturing variance in data, thus allowing for meaningful sensing at any desired level of compression. We focus on the Walsh-Hadamard sampling basis for its relevance to hardware constraints, but our approach applies to any sampling basis of interest. We illustrate the effectiveness of our method on the Physical Sciences Inc. Fabry-P\'{e}rot interferometer sensor multispectral dataset, the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset, and a Colorado State University Swiss Ranger depth image dataset. The spectral datasets consist of simulant experiments, including releases of chemicals such as GAA and SF6. We combine our sampling and reconstruction with the adaptive coherence estimator (ACE) and bulk coherence for chemical detection and we incorporate an algorithmic threshold for ACE values to determine the presence or absence of a chemical. We compare results across sampling methods in this context. We have successful chemical detection at a compression rate of 90%. For all three datasets, we compare our sampling approach to standard orderings of sampling basis such as random, sequency, and an analog of sequency that we term `frequency. ' In one instance, the peak signal to noise ratio was improved by over 30% across a test set of depth images.
Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios

This work introduces an evaluation benchmark for depth estimation and completion using high-resolution depth measurements with angular resolution of up to 25" (arcsecond), akin to a 50 megapixel camera with per-pixel depth available. Existing datasets, such as the KITTI benchmark, provide only sparse reference measurements with an order of magnitude lower angular resolution - these sparse measurements are treated as ground truth by existing depth estimation methods. We propose an evaluation methodology in four characteristic automotive scenarios recorded in varying weather conditions (day, night, fog, rain). As a result, our benchmark allows us to evaluate the robustness of depth sensing methods in adverse weather and different driving conditions. Using the proposed evaluation data, we demonstrate that current stereo approaches provide significantly more stable depth estimates than monocular methods and lidar completion in adverse weather. Data and code are available at https://github.com/gruberto/PixelAccurateDepthBenchmark.git.
PhD thesis "Extreme value statistics of strongly correlated systems: fermions, random matrices and random walks"

In this thesis, we study three physically relevant models of strongly correlated random variables: trapped fermions, random matrices and random walks. In the first part, we show several exact mappings between the ground state of a trapped Fermi gas and ensembles of random matrix theory. The Fermi gas is inhomogeneous in the trapping potential and in particular there is a finite edge beyond which its density vanishes. Going beyond standard semi-classical techniques (such as local density approximation), we develop a precise description of the spatial statistics close to the edge. This description holds for a large universality class of hard edge potentials. We apply these results to compute the statistics of the position of the fermion the farthest away from the centre of the trap, the number of fermions in a given domain (full counting statistics) and the related bipartite entanglement entropy. Our analysis also provides solutions to open problems of extreme value statistics in random matrix theory. We obtain for instance a complete description of the fluctuations of the largest eigenvalue in the complex Ginibre ensemble. In the second part of the thesis, we study extreme value questions for random walks. We consider the gap statistics, which requires to take explicitly into account the discreteness of the process. This question cannot be solved using the convergence of the process to its continuous counterpart, the Brownian motion. We obtain explicit analytical results for the gap statistics of the walk with a Laplace distribution of jumps and provide numerical evidence suggesting the universality of these results.
Shall we turn off the media? Global information can destroy local cooperation in the one-dimensional ring

In this paper we investigate the evolution of cooperation when the interaction structure is strictly local, and hence fitness only depends on local behaviors, while the competition structure is partly global, and hence selection can happen also between distant agents. We explore this novel setup by means of numerical simulations in a model where agents are arranged in a one-dimensional ring. Preliminary results suggest that the extent of global comparison systematically favors defection under strong selection, while its effect under weak selection is less systematic, but overall still favors defection. Further, the extent of global comparison seems to reduce sensibly fixation times.
The Research of Moving Target Detection and Tracking System Based on DM6437

In order to enhance the rapidity and the accuracy of moving target detection and tracking, and improve the speed of the algorithm on the DSP (digital signal processor), an active visual tracking system was designed based on the gaussian mixture background model and Meanshift algorithm on DM6437. The system use the VLIB library developed by TI, and through the method of gaussian mixture background model to detect the moving objects and use the Meanshift tracking algorithm based on color features to track the target in RGB space. Finally, the system is tested on the hardware platform, and the system is verified to be quickness and accuracy.
Forest species discrimination in an Alpine mountain area using a fuzzy classification of multi-temporal SPOT (HRV) data

Forest cover maps, showing the location and the extent of different forest types, are essential tools for forest monitoring and planning. Nowadays, remote sensing is one of the most important source of forest cover classifications at different scales. In this paper, the usefulness of middle resolution images (SPOT HRV) for forest cover mapping at local scale is evaluated (1) and a fuzzy classification approach is tested for increasing forest discrimination at the specific level (2). The study was carried out in a mountain test area located in the eastern Alps of Italy within the RI.SELV. ITALIA project founded by the Italian Ministry of Agriculture and Forests. It was based on a data-set composed of two SPOT images, topographically corrected, and some ground inventory data. The SPOT images, taken in summer and autumn, were selected in order to use the phenology characteristics of the different forest species for improving the separability between evergreen and deciduous species. The bi-temporal images were firstly combined in a single multi-temporal image which was later classified using both maximum likelihood and fuzzy classification methods. In order to identify the best classification procedure, the results of both classifications were evaluated using independent ground inventory data and then compared. The fuzzy classification approach gave more accurate results in forest species discrimination, while the maximum likelihood algorithm showed some limits in classifying the forest cover in such a complex landscapes characterized by rugged terrain and by the frequent presence of mixed forests.
Computing with Graphs and Graph Rewriting

Graphs are a popular data structure. Programmers are faced with the need to represent, inspect, modify, display and recognize graphs. In this paper we describe a systematic approach to graph modification, using graph rewrite rules. Graph rewrite rules replace one subgraph by another subgraph. In other words, a graph rewrite rule specifies how, and under what conditions, to replace one piece of a graph by another piece. This is an intuitive and useful generalization of string rewriting. We illustrate these ideas using a sampling of applications: precedence-network creation, visual language editing, and recognition of mathematics notation. Graph rewriting examples are written in PROGRES, a programming language with both visual and textual elements.