Miracle-dz/UniFAR-FTUs huggingface dataset:
Cinta Senese Pig

Cinta Senese is an Italian autochthonous pig breed, one of the local pig breeds investigated in the project TREASURE. The present chapter aims to present history and status of Cinta Senese pig breed, its phenotypic characteristics, geographical location, production system and the quality of its main products. Reproductive performance was estimated by several data: sow age at first parturition, litters/sow/year, piglets alive/litter, weaning weight, stillborn/litter, death rate percentage at weaning, duration of lactation, length of farrowing and sow age at culling. Growth performance was estimated by means of average daily gain in lactation and from birth to slaughter, growing at early, middle, late and overall fattening stage and average daily feed intake in late and overall fattening stage. Carcass traits were evaluated by means of age and weight at slaughtering, hot carcass weight, carcass yield, loin eye area and back fat thickness at the first thoracic vertebra, last rib and above gluteus medius muscle. Meat quality traits of the longissimus muscle were evaluated by means of the following: pH at 45 minutes and 24 hours after slaughtering, instrumental measurements of colour (CIE L*, a*, b*) and intramuscular fat content. Fatty acid composition was evaluated in back fat tissue.
Subpath Queries on Compressed Graphs: a Survey

Text indexing is a classical algorithmic problem that has been studied for over four decades: given a text T, pre-process it off-line so that, later, we can quickly count and locate the occurrences of any string (the query pattern) in T in time proportional to the query’s length. The earliest optimal-time solution to the problem, the suffix tree, dates back to 1973 and requires up to two orders of magnitude more space than the plain text just to be stored. In the year 2000, two breakthrough works showed that efficient queries can be achieved without this space overhead: a fast index be stored in a space proportional to the text’s entropy. These contributions had an enormous impact in bioinformatics: today, virtually any DNA aligner employs compressed indexes. Recent trends considered more powerful compression schemes (dictionary compressors) and generalizations of the problem to labeled graphs: after all, texts can be viewed as labeled directed paths. In turn, since finite state automata can be considered as a particular case of labeled graphs, these findings created a bridge between the fields of compressed indexing and regular language theory, ultimately allowing to index regular languages and promising to shed new light on problems, such as regular expression matching. This survey is a gentle introduction to the main landmarks of the fascinating journey that took us from suffix trees to today’s compressed indexes for labeled graphs and regular languages.
Modeling Fashion Influence From Photos

The evolution of clothing styles and their migration across the world is intriguing, yet difficult to describe quantitatively. We propose to discover and quantify fashion influences from catalog and social media photos. We explore fashion influence along two channels: geolocation and fashion brands. We introduce an approach that detects which of these entities influence which other entities in terms of propagating their styles. We then leverage the discovered influence patterns to inform a novel forecasting model that predicts the future popularity of any given style within any given city or brand. To demonstrate our idea, we leverage public large-scale datasets of 7.7M Instagram photos from 44 major world cities (where styles are worn with variable frequency) as well as 41K Amazon product photos (where styles are purchased with variable frequency). Our model learns directly from the image data how styles move between locations and how certain brands affect each other's designs in a predictable way. The discovered influence relationships reveal how both cities and brands exert and receive fashion influence for an array of visual styles inferred from the images. Furthermore, the proposed forecasting model achieves state-of-the-art results for challenging style forecasting tasks. Our results indicate the advantage of grounding visual style evolution both spatially and temporally, and for the first time, they quantify the propagation of inter-brand and inter-city influences. Project page: https://www.cs. utexas.edu/~ziad/influence_from_photos. html
Advances in dynamic AFM: From nanoscale energy dissipation to material properties in the nanoscale

Since the inception of the atomic force microscope AFM, dynamic methods have been very fruitful by establishing methods to quantify dissipative and conservative forces in the nanoscale and by providing a means to apply gentle forces to the samples with high resolution. Here we review developments that cover over a decade of our work on energy dissipation, phase contrast and the extraction of relevant material properties from observables. We describe the attempts to recover material properties via one dimensional amplitude and phase curves from force models and explore the evolution of these methods in terms of force reconstruction, fits of experimental measurements, and the more recent advances in multifrequency AFM.
Fully dynamic approximation of LIS in polylogarithmic time

We revisit the problem of maintaining the longest increasing subsequence (LIS) of an array under (i) inserting an element, and (ii) deleting an element of an array. In a recent breakthrough, Mitzenmacher and Seddighin [STOC 2020] designed an algorithm that maintains an O((1/є)O(1/є))-approximation of LIS under both operations with worst-case update time Õ(nє), for any constant є>0 (Õ hides factors polynomial in logn, where n is the length of the input). We exponentially improve on their result by designing an algorithm that maintains an (1+є) approximation of LIS under both operations with worst-case update time Õ(є−5). Instead of working with the grid packing technique introduced by Mitzenmacher and Seddighin, we take a different approach building on a new tool that might be of independent interest: LIS sparsification. A particularly interesting consequence of our result is an improved solution for the so-called Erdős-Szekeres partitioning, in which we seek a partition of a given permutation of {1,2,…,n} into O(√n) monotone subsequences. This problem has been repeatedly stated as one of the natural examples in which we see a large gap between the decision-tree complexity and algorithmic complexity. The result of Mitzenmacher and Seddighin implies an O(n1+є) time solution for this problem, for any є>0. Our algorithm (in fact, its simpler decremental version) further improves this to Õ(n).
The Osteocyte Transcriptome Is Extensively Dysregulated in Mouse Models of Osteogenesis Imperfecta

Osteocytes are long‐lived, highly interconnected, terminally differentiated osteoblasts that reside within mineralized bone matrix. They constitute about 95% of adult bone cells and play important functions including in the regulation of bone remodeling, phosphate homeostasis, and mechanical stimuli sensing and response. However, the role of osteocytes in the pathogenesis of congenital diseases of abnormal bone matrix is poorly understood. This study characterized in vivo transcriptional changes in osteocytes from CrtapKO and oim/oim mouse models of osteogenesis imperfecta (OI) compared with wild‐type (WT) control mice. To do this, RNA was extracted from osteocyte‐enriched cortical femurs and tibias, sequenced and subsequently analyzed to identify differentially expressed transcripts. These models were chosen because they mimic two types of OI with different genetic mutations that result in distinct type I collagen defects. A large number of transcripts were dysregulated in either model of OI, but 281 of them were similarly up‐ or downregulated in both compared with WT controls. Conversely, very few transcripts were differentially expressed between the CrtapKO and oim/oim mice, indicating that distinct alterations in type I collagen can lead to shared pathogenic processes and similar phenotypic outcomes. Bioinformatics analyses identified several critical hubs of dysregulation that were enriched in annotation terms such as development and differentiation, ECM and collagen fibril organization, cell adhesion, signaling, regulatory processes, pattern binding, chemotaxis, and cell projections. The data further indicated alterations in important signaling pathways such as WNT and TGF‐β but also highlighted new candidate genes to pursue in future studies. Overall, our study suggested that the osteocyte transcriptome is broadly dysregulated in OI with potential long‐term consequences at the cellular level, which deserve further investigations. © 2019 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.
Inertial Proximal Block Coordinate Method for a Class of Nonsmooth and Nonconvex Sum-of-Ratios Optimization Problems

In this paper, we consider a class of nonsmooth and nonconvex sum-of-ratios fractional optimization problems with block structure. This model class encompasses a broad spectrum of nonsmooth optimization problems such as the energy efficiency maximization problem and the sparse generalized eigenvalue problem. We first show that these problems can be equivalently reformulated as non-fractional nonsmooth optimization problems and propose an inertial proximal block coordinate method for solving them by exploiting the block structure of the underlying model. The global convergence of our method is guaranteed under the Kurdyka--Łojasiewicz (KL) property and some mild assumptions. We then identify the explicit exponents of the KL property for three important structured fractional optimization problems. In particular, for the sparse generalized eigenvalue problem with either cardinality regularization or sparsity constraint, we show that the KL exponents are 1/2, and so, the proposed method exhibits linear convergence rate. Finally, we illustrate our theoretical results with both analytic and simulated numerical examples.
Improving Bayesian Network Structure Learning in the Presence of Measurement Error

Structure learning algorithms that learn the graph of a Bayesian network from observational data often do so by assuming the data correctly reflect the true distribution of the variables. However, this assumption does not hold in the presence of measurement error, which can lead to spurious edges. This is one of the reasons why the synthetic performance of these algorithms often overestimates real-world performance. This paper describes an algorithm that can be added as an additional learning phase at the end of any structure learning algorithm, and serves as a correction learning phase that removes potential false positive edges. The results show that the proposed correction algorithm successfully improves the graphical score of four well-established structure learning algorithms spanning different classes of learning in the presence of measurement error.
Isolation and Identification of Antagonistic Bacterium against a Pathogens of Bacterial Tuber Rot of Amorphophallus muelleri

Rhizosphere bacteria have the ability to protect the host plants from the infection of pathogenic microorganisms. This study aimed to identify rhizosphere bacteria that were capable of inhibiting the growth of bacterial isolates that cause tuber rot of Amorphophallus muelleri . Rhizosphere bacteria were isolated using Nutrient Agar medium by pour plate method. Isolates were subjected to antagonistic assay against several bacterial isolates from the rotten tuber of A. muelleri using dual culture method. The potential isolate was identified based on 16S rDNA sequence. Isolate R7 showed the strongest inhibition to the growth of bacterial isolates from rotten tuber with an inhibition zone diameter of 19.66 mm. The 16S rDNA sequence of isolate R7 R7 was 99.7% similar to Delftia tsuruhatensis PCL1755. The isolate was potential to be developed as phytopathogen control agent. Keywords: Amorphophallus , antagonistic bacteria, rhizosphere bacteria, rotten tuber, 16S rDNA .
MOFA: Modular Factorial Design for Hyperparameter Optimization

Automated hyperparameter optimization (HPO) has shown great power in many machine learning applications. While existing methods suffer from model selection, parallelism, or sample efficiency, this paper presents a new HPO method, MOdular FActorial Design (MOFA), to address these issues simultaneously. The major idea is to use techniques from Experimental Designs to improve sample efficiency of model-free methods. Particularly, MOFA runs with four modules in each iteration: (1) an Orthogonal Latin Hypercube (OLH)-based sampler preserving both univariate projection uniformity and orthogonality; (2) a highly parallelized evaluator; (3) a transformer to collapse the OLH performance table into a specified Fractional Factorial Design--Orthogonal Array (OA); (4) an analyzer including Factorial Performance Analysis and Factorial Importance Analysis to narrow down the search space. We theoretically and empirically show that MOFA has great advantages over existing model-based and model-free methods.
Visual Diver Face Recognition for Underwater Human-Robot Interaction

This paper presents a deep-learned facial recognition method for underwater robots to identify scuba divers. Specifically, the proposed method is able to recognize divers underwater with faces heavily obscured by scuba masks and breathing apparatus. Our contribution in this research is towards robust facial identification of individuals under significant occlusion of facial features and image degradation from underwater optical distortions. With the ability to correctly recognize divers, autonomous underwater vehicles (AUV) will be able to engage in collaborative tasks with the correct person in human-robot teams and ensure that instructions are accepted from only those authorized to command the robots. We demonstrate that our proposed framework is able to learn discriminative features from real-world diver faces through different data augmentation and generation techniques. Experimental evaluations show that this framework achieves a 3-fold increase in prediction accuracy compared to the state-of-the-art (SOTA) algorithms and is well-suited for embedded inference on robotic platforms.
Pulsating ULXs: large pulsed fraction excludes strong beaming

The recent discovery of pulsating ultraluminous X-ray sources (ULXs) shows that the apparent luminosity of accreting neutron stars can exceed the Eddington luminosity by a factor of 100s. The relation between the actual and apparent luminosity is a key ingredient in theoretical models of ULXs, but it is still under debate. A typical feature of the discovered pulsating ULXs is a large pulsed fraction (PF). Using Monte Carlo simulations, we consider a simple geometry of accretion flow and test the possibility of simultaneous presence of a large luminosity amplification due the geometrical beaming and a high PF. We argue that these factors largely exclude each other and only a negligible fraction of strongly beamed ULX pulsars can show PF above 10 per cent. Discrepancy between this conclusion and current observations indicates that pulsating ULXs are not strongly beamed and their apparent luminosity is close to the actual one.
State-of-charge Estimation of a Li-ion Battery using Deep Learning and Stochastic Optimization

This article presents a novel empirical study for the estimation of the State of Charge (SOC) of a lithium-ion (Li-ion) battery which uses a deep learning model with three hidden layers. We model a series of ten vehicle drive cycles that were applied to a Panasonic 18650PF Li-ion cell. Our results show that the choice of the optimization algorithm affects the model performance. The proposed model was able to achieve an error smaller than 1.0% in all drive cycles.
Unmixing methods based on nonnegativity and weakly mixed pixels for astronomical hyperspectral datasets

An increasing number of astronomical instruments (on Earth and space-based) provide hyperspectral images, that is three-dimensional data cubes with two spatial dimensions and one spectral dimension. The intrinsic limitation in spatial resolution of these instruments implies that the spectra associated with pixels of such images are most often mixtures of the spectra of the “pure” components that exist in the considered region. In order to estimate the spectra and spatial abundances of these pure components, we here propose an original blind signal separation (BSS), that is to say an unsupervised unmixing method. Our approach is based on extensions and combinations of linear BSS methods that belong to two major classes of methods, namely nonnegative matrix factorization (NMF) and sparse component analysis (SCA). The former performs the decomposition of hyperspectral images, as a set of pure spectra and abundance maps, by using nonnegativity constraints, but the estimated solution is not unique: It highly depends on the initialization of the algorithm. The considered SCA methods are based on the assumption of the existence of points or tiny spatial zones where only one source is active (i.e., one pure component is present). These points or zones are then used to estimate the mixture and perform the decomposition. In real conditions, the assumption of perfect single-source points or zones is not always realistic. In such conditions, SCA yields approximate versions of the unknown sources and mixing coefficients. We propose to use part of these preliminary estimates from the SCA to initialize several runs of the NMF in order to refine these estimates and further constrain the convergence of the NMF algorithm. The proposed methods also estimate the number of pure components involved in the data and they provide error bars associated with the obtained solution. Detailed tests with synthetic data show that the decomposition achieved with such hybrid methods is nearly unique and provides good performance, illustrating the potential of applications to real data.
The SALT survey of helium-rich hot subdwarfs: methods, classification, and coarse analysis

A medium- and high-resolution spectroscopic survey of helium-rich hot subdwarfs is being carried out using the Southern African Large Telescope (SALT). Objectives include the discovery of exotic hot subdwarfs and of sequences connecting chemically-peculiar subdwarfs of different types. The first phase consists of medium-resolution spectroscopy of over 100 stars selected from low-resolution surveys. This paper describes the selection criteria, and the observing, classification and analysis methods. It presents 107 spectral classifications on the MK-like Drilling system and 106 coarse analyses ($T_{\rm eff}, \log g, \log y$) based on a hybrid grid of zero-metal non-LTE and line-blanketed LTE model atmospheres. For 75 stars, atmospheric parameters have been derived for the first time. The sample may be divided into 6 distinct groups including the classical `helium-rich' sdO stars with spectral types (Sp) sdO6.5 - sdB1 (74) comprising carbon-rich (35) and carbon-weak (39) stars, very hot He-sdO's with Sp $\lesssim$ sdO6 (13), extreme helium stars with luminosity class $\lesssim 5$ (5), intermediate helium-rich subdwarfs with helium class 25 -- 35 (8), and intermediate helium-rich subdwarfs with helium class $10 - 25$ (6). The last covers a narrow spectral range (sdB0 -- sdB1) including two known and four candidate heavy-metal subdwarfs. Within other groups are several stars of individual interest, including an extremely metal-poor helium star, candidate double-helium subdwarf binaries, and a candidate low-gravity He-sdO star.
The Cube++ Illumination Estimation Dataset

Computational color constancy has the important task of reducing the influence of the scene illumination on the object colors. As such, it is an essential part of the image processing pipelines of most digital cameras. One of the important parts of the computational color constancy is illumination estimation, i.e. estimating the illumination color. When an illumination estimation method is proposed, its accuracy is usually reported by providing the values of error metrics obtained on the images of publicly available datasets. However, over time it has been shown that many of these datasets have problems such as too few images, inappropriate image quality, lack of scene diversity, absence of version tracking, violation of various assumptions, GDPR regulation violation, lack of additional shooting procedure info, etc. In this paper a new illumination estimation dataset is proposed that aims to alleviate many of the mentioned problems and to help the illumination estimation research. It consists of 4890 images with known illumination colors as well as with additional semantic data that can further make the learning process more accurate. Due to the usage of the SpyderCube color target, for every image there are two ground-truth illumination records covering different directions. Because of that, the dataset can be used for training and testing of methods that perform single or two-illuminant estimation. This makes it superior to many similar existing datasets. The datasets, it’s smaller version SimpleCube++, and the accompanying code are available at https://github.com/Visillect/CubePlusPlus/.
Cosmological horizons radiate

Gibbons and Hawking (Phys. Rev. D, 15 (1977) 2738) have shown that the horizon of de Sitter space emits radiation in the same way as the event horizon of the black hole. But actual cosmological horizons are not event horizons, except in de Sitter space. Nevertheless, this paper proves Gibbons' and Hawking's radiation formula as an exact result for any flat space expanding with strictly positive Hubble parameter. The paper gives visual and intuitive insight into why this is the case. The paper also indicates how cosmological horizons are related to the dynamical Casimir effect, which makes experimental tests with laboratory analogues possible.
Sensor Design Optimization of Innovative Low-Power, Large Area FD-MAPS for HEP and Applied Science

Fully depleted monolithic active pixel sensors (FD-MAPSs) represent a state-of-the-art detector technology and profit from a low material budget and cost for high-energy physics experiments and other fields of research like medical imaging and astro-particle physics. Compared to the MAPS currently in use, fully depleted pixel sensors have the advantage of charge collection by drift, which enables a fast and uniform response overall to the pixel matrix. The functionality of these devices has been shown in previous proof-of-concept productions. In this article, we describe the optimization of the test pixel designs that will be implemented in the first engineering run of the demonstrator chip of the ARCADIA project. These optimization procedures include radiation damage models that have been employed in Technology Computer Aided Design simulations to predict the sensors’ behavior in different working environments.