Miracle-dz/UniFAR-FTUs huggingface dataset:
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language

Optimizing an expensive-to-query function is a common task in science and engineering, where it is beneficial to keep the number of queries to a minimum. A popular strategy is Bayesian optimization (BO), which leverages probabilistic models for this task. Most BO today uses Gaussian processes (GPs), or a few other surrogate models. However, there is a broad set of Bayesian modeling techniques that could be used to capture complex systems and reduce the number of queries in BO. Probabilistic programming languages (PPLs) are modern tools that allow for flexible model definition, prior specification, model composition, and automatic inference. In this paper, we develop ProBO, a BO procedure that uses only standard operations common to most PPLs. This allows a user to drop in a model built with an arbitrary PPL and use it directly in BO. We describe acquisition functions for ProBO, and strategies for efficiently optimizing these functions given complex models or costly inference procedures. Using existing PPLs, we implement new models to aid in a few challenging optimization settings, and demonstrate these on model hyperparameter and architecture search tasks.
Fluctuation-induced potential for an impurity in a semi-infinite one-dimensional Bose gas

We consider an impurity in a semi-infinite one-dimensional system of weakly-interacting bosons. We calculate the interaction potential for the impurity due to the end of the system, i.e., the wall. For local repulsive (attractive) interaction between the impurity and the Bose gas, the interaction potential is attractive (repulsive). At short distances from the wall it decays exponentially crossing over into a universal $1/r^2$ behavior at separations $r$ above the healing length. Our results can also be interpreted as a Casimir-like interaction between two impurities, where one of them is infinitely strongly coupled to the Bose gas. We discuss various scenarios for the induced interaction between the impurities using the scattering approach. We finally address the phenomenon of localization of the impurity near the wall. In the paper we mainly study the case of a static impurity, however the universal $1/r^2$ interaction also holds for a slowly moving impurity.
Fractional polarization of extragalactic sources in the 500 deg2 SPTpol survey

We study the polarization properties of extragalactic sources at 95 and 150 GHz in the SPTpol 500 deg2 survey. We estimate the polarized power by stacking maps at known source positions, and correct for noise bias by subtracting the mean polarized power at random positions in the maps. We show that the method is unbiased using a set of simulated maps with similar noise properties to the real SPTpol maps. We find a flux-weighted mean-squared polarization fraction 〈p2〉 = [8.9 ± 1.1] × 10−4 at 95 GHz and [6.9 ± 1.1] × 10−4 at 150 GHz for the full sample. This is consistent with the values obtained for a subsample of active galactic nuclei. For dusty sources, we find 95 per cent upper limits of 〈p2〉95 < 16.9 × 10−3 and 〈p2〉150 < 2.6 × 10−3. We find no evidence that the polarization fraction depends on the source flux or observing frequency. The 1σ upper limit on measured mean-squared polarization fraction at 150 GHz implies that extragalactic foregrounds will be subdominant to the CMB E and B mode polarization power spectra out to at least ℓ ≲ 5700 (ℓ ≲ 4700) and ℓ ≲ 5300 (ℓ ≲ 3600), respectively, at 95 (150) GHz.
Phase retrieval by random binary questions: Which complementary subspace is closer?

Phase retrieval in real or complex Hilbert spaces is the task of recovering a vector, up to an overall unimodular multiplicative constant, from magnitudes of linear quantities. In this paper, we assume that the vector is normalized, but retain only qualitative, binary information about the measured quantities by comparing them with a threshold. In more specific, geometric terms, we choose a sequence of random subspaces in a real or complex Hilbert space and only record whether a given vector is closer to the subspace than to the complementary subspace. The subspaces have half the dimension of the Hilbert space and are independent, uniformly distributed with respect to the action of the orthogonal or unitary groups. The main goal of this paper is to find a feasible algorithm for approximate recovery based on the information gained about the vector from these binary questions and to establish error bounds for its approximate recovery. We provide a pointwise bound for fixed input vectors and a uniform bound that controls the worst-case scenario among all inputs. Both bounds hold with high probability with respect to the choice of the subspaces. For real or complex vectors of dimension $n$, the pointwise bound requires $m \ge C \delta^{-2} n \log(n)$ and the uniform bound $m \ge C \delta^{-2} n^2 \log(\delta^{-1} n)$ binary questions in order to achieve an accuracy of $\delta$. The accuracy $\delta$ is measured by the operator norm of the difference between the rank-one orthogonal projections corresponding to the normalized input vector and its approximate recovery.
Linear Size Sparsifier and the Geometry of the Operator Norm Ball

The Matrix Spencer Conjecture asks whether given $n$ symmetric matrices in $\mathbb{R}^{n \times n}$ with eigenvalues in $[-1,1]$ one can always find signs so that their signed sum has singular values bounded by $O(\sqrt{n})$. The standard approach in discrepancy requires proving that the convex body of all good fractional signings is large enough. However, this question has remained wide open due to the lack of tools to certify measure lower bounds for rather small non-polyhedral convex sets. A seminal result by Batson, Spielman and Srivastava from 2008 shows that any undirected graph admits a linear size spectral sparsifier. Again, one can define a convex body of all good fractional signings. We can indeed prove that this body is close to most of the Gaussian measure. This implies that a discrepancy algorithm by the second author can be used to sample a linear size sparsifer. In contrast to previous methods, we require only a logarithmic number of sampling phases.
On Coding for Orthogonal Frequency Division Multiplexing Systems

The main contribution of this thesis is the statistical analysis of orthogonal frequency division multiplexing (OFDM) systems operating over wireless channels that are both frequency selective and Rayleigh fading. We first describe the instantaneous capacity of such systems using a central limit theorem, as well as the asymptotic capacity of a power limited OFDM system as the number of subcarriers approaches infinity. We then analyse the performance of uncoded OFDM systems by first developing bounds on the block error rate. Next we show that the distribution of the number of symbol errors within each block may be tightly approximated, and derive the distribution of an upper bound on the total variation distance. Finally, the central result of this thesis proposes the use of lattices for encodingOFDM systems. For this, we detail a particular method of using lattices to encode OFDM systems, and derive the optimalmaximum likelihood decodingmetric. Generalised Minimum Distance (GMD) decoding is then introduced as a lower complexity method of decoding lattice encoded OFDM. We derive the optimal reliability metric for GMD decoding of OFDM systems operating over frequency selective channels, and develop analytical upper bounds on the error rate of lattice encoded OFDM systems employing GMD decoding.
Linear Regression from Strategic Data Sources

Linear regression is a fundamental building block of statistical data analysis. It amounts to estimating the parameters of a linear model that maps input features to corresponding outputs. In the classical setting where the precision of each data point is fixed, the famous Aitken/Gauss-Markov theorem in statistics states that generalized least squares (GLS) is a so-called “Best Linear Unbiased Estimator” (BLUE). In modern data science, however, one often faces strategic data sources; namely, individuals who incur a cost for providing high-precision data. For instance, this is the case for personal data, whose revelation may affect an individual’s privacy—which can be modeled as a cost—or in applications such as recommender systems, where producing an accurate estimate entails effort. In this article, we study a setting in which features are public but individuals choose the precision of the outputs they reveal to an analyst. We assume that the analyst performs linear regression on this dataset, and individuals benefit from the outcome of this estimation. We model this scenario as a game where individuals minimize a cost composed of two components: (a) an (agent-specific) disclosure cost for providing high-precision data; and (b) a (global) estimation cost representing the inaccuracy in the linear model estimate. In this game, the linear model estimate is a public good that benefits all individuals. We establish that this game has a unique non-trivial Nash equilibrium. We study the efficiency of this equilibrium and we prove tight bounds on the price of stability for a large class of disclosure and estimation costs. Finally, we study the estimator accuracy achieved at equilibrium. We show that, in general, Aitken’s theorem does not hold under strategic data sources, though it does hold if individuals have identical disclosure costs (up to a multiplicative factor). When individuals have non-identical costs, we derive a bound on the improvement of the equilibrium estimation cost that can be achieved by deviating from GLS, under mild assumptions on the disclosure cost functions.
L1 Use, Lexical Richness, Accuracy and Syntactic Complexity in the Oral Production of CLIL and NON-CLIL Learners of English

As more focus on communication is promoted in Content and Language Integrated Learning (CLIL) classrooms and general proficiency is usually enhanced in these settings, this study aims to explore (i) whether a lower rate of L1 use, as regards interactional strategies and transfer lapses, is found in CLIL learners when compared to NON-CLIL learners; (ii) whether differences exist between both groups in lexical richness (type/token ratio and D), accuracy (word order and correct production of the definite and indefinite article) and syntactic complexity (production of simple and complex sentences and variety of tenses used); and (iii) whether L1 use correlates with lexical richness, accuracy and syntactic complexity. The analysis of an oral production task of two groups of fourteen year-old Basque/Spanish bilingual students learning L3 English indicates that the CLIL group made lower use of L1. The CLIL group also obtained better scores in lexical richness and general proficiency as well as in the correct production of definite and indefinite articles and in the use of complex clauses. No correlation was observed between L1 use and accuracy or syntactic complexity, suggesting that a decrease in L1 use is not always related to better performance in specific aspects of language or the use of more complex language.
Algorithms for generalized numerical semigroups

We provide algorithms for performing computations in generalized numerical semigroups, that is, submonoids of [Formula: see text] with finite complement in [Formula: see text]. These semigroups are affine semigroups, which in particular implies that they are finitely generated. For a given finite set of elements in [Formula: see text] we show how to deduce if the monoid spanned by this set is a generalized numerical semigroup and, if so, we calculate its set of gaps. Also, given a finite set of elements in [Formula: see text] we can determine if it is the set of gaps of a generalized numerical semigroup and, if so, compute the minimal generators of this monoid. We provide a new algorithm to compute the set of all generalized numerical semigroups with a prescribed genus (the cardinality of their sets of gaps). Its implementation allowed us to compute (for various dimensions) the number of numerical semigroups of higher genus than has previously been computed.
Location-aware Beam Alignment for mmWave Communications

Beam alignment is required in millimeter wave communication to ensure high data rate transmission. However, with narrow beamwidth in massive MIMO, beam alignment could be computationally intensive due to the large number of beam pairs to be measured. In this paper, we propose an efficient beam alignment framework by exploiting the location information of the user equipment (UE) and potential reflecting points. The proposed scheme allows the UE and the base station to perform a coordinated beam search from a small set of beams within the error boundary of the location information, the selected beams are then used to guide the search of future beams. To further reduce the number of beams to be searched, we propose an intelligent search scheme within a small window of beams to determine the direction of the actual beam. The proposed beam alignment algorithm is verified on simulation with some location uncertainty.
Non-structured DNN Weight Pruning Considered Harmful

Large deep neural network (DNN) models pose the key challenge to energy efficiency due to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or SRAM operations. It motivates the intensive research on model compression with two main approaches. Weight pruning leverages the redundancy in the number of weights and can be performed in a non-structured, which has higher flexibility and pruning rate but incurs index accesses due to irregular weights, or structured manner, which preserves the full matrix structure with lower pruning rate. Weight quantization leverages the redundancy in the number of bits in weights. Compared to pruning, quantization is much more hardware-friendly, and has become a "must-do" step for FPGA and ASIC implementations. This paper provides a definitive answer to the question for the first time. First, we build ADMM-NN-S by extending and enhancing ADMM-NN, a recently proposed joint weight pruning and quantization framework. Second, we develop a methodology for fair and fundamental comparison of non-structured and structured pruning in terms of both storage and computation efficiency. Our results show that ADMM-NN-S consistently outperforms the prior art: (i) it achieves 348x, 36x, and 8x overall weight pruning on LeNet-5, AlexNet, and ResNet-50, respectively, with (almost) zero accuracy loss; (ii) we demonstrate the first fully binarized (for all layers) DNNs can be lossless in accuracy in many cases. These results provide a strong baseline and credibility of our study. Based on the proposed comparison framework, with the same accuracy and quantization, the results show that non-structrued pruning is not competitive in terms of both storage and computation efficiency. Thus, we conclude that non-structured pruning is considered harmful. We urge the community not to continue the DNN inference acceleration for non-structured sparsity.
Associated production of 
χc
 pairs with a gluon in the collinear-factorization approach

We calculate cross section for production of $\chi_c$ pairs in proton-proton collisions. The cross section for the $g g \to \chi_{c J_1} \chi_{c J_2}$ is considerably smaller (especially for $\chi_{c 1} \chi_{c 1}$ final state) than that obtained recently in the $k_T$-factorization approach. We calculate therefore next-to-leading order contributions with $\chi_c$ pair and one extra associated (mini-)jet. We find these contributions to be much larger than those for the $2 \to 2$ contribution. Especially the emission of a leading gluon (carrying a large momentum fraction of one of the incoming gluons) are important. These emissions in the $k_T$-factorization approach are absorbed into the initial state unintegrated gluon distributions. A smaller contribution to the cross section comes from the production of central gluons emitted with rapidities between the $\chi_c$-mesons. They do lead, however, to an enhancement of the $\chi_c$-pair production at large rapidity distance between the mesons. Our present study explains the size of the cross section for the $\chi_c$ pair production obtained previously in the $k_T$-factorization approach. Several differential distributions are presented.
The Application of Multi-Layer Artificial Neural Networks in Speckle Reduction (Methodology)

Optical Coherence Tomography (OCT) uses the spatial and temporal coherence properties of optical waves backscattered from a tissue sample to form an image. An inherent characteristic of coherent imaging is the presence of speckle noise. In this study we use a new ensemble framework which is a combination of several Multi-Layer Perceptron (MLP) neural networks to denoise OCT images. The noise is modeled using Rayleigh distribution with the noise parameter, sigma, estimated by the ensemble framework. The input to the framework is a set of intensity and wavelet statistical features computed from the input image, and the output is the estimated sigma value for the noise model. In this article the methodology of this technique is explained.
DO PERIODS OF CRISIS AFFECT HUMAN RESOURCE MANAGEMENT APPLICATIONS ? THE CASE OF THE TURKISH TEXTILE INDUSTRY

The principal purpose of this study is to determine and evaluate the human resources practices of corporations that faced problems in the crisis, which was developed in recent years as a result of the financial crisis. This study concentrates on “human resources practices” because of the common opinion that “human resources” play a helping role in facilitating major organisational change programmes and various crisis period practices. The study was limited to the Turkish textile sector in order to attain the determined objective. In conclusion, when looking at the human resources practices of organisations in recent years we see that they cannot be called unsuccessful. It is observed that within the organisations in the sample group, in crisis periods, we encounter results that can be regarded as positive as well as some that can be regarded as negative from the employees’ stand-point. Although reducing the number of employees can be qualified as a negative effect of the crisis period from the employees’ stand-point, increasing practices aiming at integrating the employees with the organisation with the objective of restructuring the psychological state of the employees can be regarded as a positive effect of this period.
BUSCA VISUAL EM UM ESPAÇO SEMÂNTICO: UMA ESCOLHA ENTRE IDENTIDADE E POPULARIDADE

Online social networks, such as Facebook and Instagram, are becoming major sources of clothing inspiration, with users sharing their latest outfits and looking for others with similar apparel style. This typical behavior made possible the emergence and popularization of fashion bloggers, considered, today, the great dictators of fashion trends, with wide audiences searching for looks that are in conformity with their fashion sense. However, a substantial time is generally spent searching for specific looks in fashion blogs or social networks. The main problem we investigate in this thesis is how to facilitate and improve the retrieval of relevant looks posted in fashion blogs. We tackle this problem by using a content-based retrieval (CBIR) approach − given a query image, we find images with similar meanings in a large database of images posted in online social networks. In our solution, we approximate the meaning of an outfit through the pieces of clothes that compose it, using a Convolution Neural Network (CNN) for representation learning and classification. In few words, the CNN model takes as input the pixels of an image and transforms them into a multi-dimensional feature vector, where each dimension corresponds to the probability associated with the corresponding clothing item. Since the model has learned a representation for the images, we are able to compare them in the resulting semantic space. That is, given an arbitrary query image, the CNN model is able to predict the pieces of clothes of the look in that image, and the ranking model is able to retrieve a sorted group of images, from the most to the least similar images, considering the distance between their feature vectors in the semantic space. When searching for looks, a user is, implicitly, searching for something that matches her or his identity. Besides, a user wants, most of times, to be inspired by looks with high levels of popularity in terms of fashion, i.e. fashionability. Considering the fact that identity and fashionability are, most of times, in non-conformity, this thesis also analyses the trade-off between these two concepts, in order to improve the results of the search, according to the user’s needs. We produce a second ranking
An Efficient Ranking Algorithm for Scientific Research Papers

Large content of scientific research papers of variable quality became available online, that is generally hard for researchers and students to locate their desired information by using the traditional search engines that return large number of documents. This made it necessary to develop an efficient and effective ranking algorithm to solve this problem. One of the most famous ranking algorithms is the PageRank (PR), which is originally used to rank web pages. This algorithm needs to be modified in order to efficiently rank scientific research papers, since they are different than WebPages. This paper proposes adding some factors that can enhance PageRank results when applied on research papers, making it less biased toward old papers, and taking author ranks into account. The results showed that the proposed method improved the results of the PageRank with respect to the recall and precision measures.
OBJECTS TRACKING IN A VIDEO SEQUENCE

OBJECTS TRACKING IN A VIDEO SEQUENCE. This paper presents the result of implementing a tracking system for identifying objects in a video sequence. The main objective of this research is to keep track of objects movement and their activities which are then analyzed whether the activities related to suspicious activities or not. At this stage the research is concentrated on the keep track of the objects once the objects enter the scene. The objects tracking are done by identify objects’ movement from video sequence using frame by frame analysis. In order to avoid tracking unnecessary objects a method is implemented to eliminate such objects. In this research a method to eliminate such objects is to use spatial objects information. Based on the described method the research shows that objects tracking in a video sequence can be implemented. Moreover, the research is also trying to isolate objects so that the object size and its activities can be analyzed. Finally, this research has been implemented both offline and real-time environment and the results show no significant different.
P o S ( N I C X I I I ) 0 2 2 Primordial Nucleosynthesis

Primordial nucleosynthesis, or big bang nucleosynthesis (BBN), is one of the three evidences for the big bang model, together with the expansion of the Universe and the Cosmic Microwave Background. There is a good global agreement over a range of nine orders of magnitude between abundances of 4He, D, 3He and 7Li deduced from observations, and calculated in primordial nucleosynthesis. However, there remain, a yet–unexplained, discrepancy of a factor ≈3, between the calculated and observed lithium primordial abundances, that has not been reduced, neither by recent nuclear physics experiments, nor by new observations. The precision in Deuterium observations in cosmological clouds has recently improved dramatically, so that nuclear cross sections involved in Deuterium BBN need to be known with similar precision. We will shortly discuss nuclear aspects related to BBN of Li and D, BBN with non-standard neutron sources, and finally, improved sensitivity studies using Monte Carlo that can be used in other site of nucleosynthesis.