Miracle-dz/UniFAR-FTUs huggingface dataset:
Alignment of Similar Shapes Based on their Convex Hulls for 3D Object Classification

In order to facilitate true autonomous robot manipulation, object classification becomes inevitable part of advanced robotic vision. Since objects belonging to the same class usually have similar shape, we propose a new method for object alignment based on similarity of their convex hulls. In this paper, the purpose of the developed alignment method is to generate hypotheses for object classification, and return pose of the classified object. The proposed approach consists of generating alignment proposals and evaluation of these proposals by a method commonly used in shape instance detection algorithms. The proposed approach is verified on an object classification benchmark dataset, where the proposed method achieves competitive results in comparison to four state-of-the-art object classification methods.
A New Continuous-Time Stability Perspective of Time-Delay Control: Introducing a State-Dependent Upper Bound Structure

In the literature of any time-delay control (TDC)-based methods, the boundedness of the error due to time-delay estimation (TDE) is crucial to prove the stability. However, the TDE error has been studied by discretizing the closed-loop system while neglecting the effect of discretization error; consequently, the TDE error is considered to be upper bounded by a constant. This letter proves that such constant upper bound is restrictive in nature due to the explicit involvement of system states in the TDE error. Thereby, without discretizing the closed-loop system, a new structure of the upper bound of TDE error is directly formulated in the continuous-time domain which has an explicit dependency on the system states. Via this formulation, this letter solves the long-standing problem for TDC of having consistent stability analysis and control design in continuous time. Based on the newly proposed structure of TDE error, an enhanced robust control law is formulated. The effectiveness of the proposed method is experimentally substantiated as compared to the conventional TDC using a multiple-degrees-of-freedom robot.
Online Multi-Object Tracking Based on Feature Representation and Bayesian Filtering Within a Deep Learning Architecture

In detection-based multi-object tracking (MOT), one challenging problem is to design a robust affinity model for data association. Moreover, since these approaches entirely rely on detection responses to locate targets, a strategy should be taken to deal with a detector’s defect. In this paper, we propose a robust online MOT tracking method that can handle these two issues effectively. We first present a novel affinity model by jointly learning more powerful feature representation and distance metric within a deep architecture. Specifically, we design a convolutional neural network to extract appearance cue tailored toward person Re-ID and a long short-term memory network to extract motion cue to encode dynamics of targets. Both the cues are then combined with a triplet loss function, which performs end-to-end deep metric learning to encode dependences across both cues automatically and thus generates fused features in embedding space to distinguish targets. To overcome the detector’s limitation, a trajectory estimation strategy is presented. We design a recurrent neural network-based Bayesian filtering module, which takes a hidden state of the above-mentioned LSTM network as an input and performs recursive prediction and update for explicitly estimating targets state. In this way, we can reconstruct trajectories by filling the gaps where no detections are present or adjusting the exact locations of trajectory where detections are imprecise. The experiments on the challenging MOT 2015 and 2016 datasets show very competitive results when comparing our method with the recent state-of-the-art batch and online tracking approaches. We achieve top one in terms of multiple objects tracking accuracy and multiple objects tracking precision among online methods on the MOT2016 dataset.
An Efficient Framework for Sentence Similarity Modeling

Sentence similarity modeling lies at the core of many natural language processing applications, and thus has received much attention. Owing to the success of word embeddings, recently, popular neural network methods achieved sentence embedding. Most of them focused on learning semantic information and modeling it as a continuous vector, yet the syntactic information of sentences has not been fully exploited. On the other hand, prior works have shown the benefits of structured trees that include syntactic information, while few methods in this branch utilized the advantages of word embeddings and another powerful technique—attention weight mechanism. This paper suggests to absorb their advantages by merging these techniques in a unified structure, dubbed as attention constituency vector tree (ACVT). Meanwhile, this paper develops a new tree kernel, known as ACVT kernel, which is tailored for sentence similarity measure based on the proposed structure. The experimental results, based on 19 widely used semantic textual similarity datasets, demonstrate that our model is effective and competitive, when compared against state-of-the-art models. Additionally, the experimental results validate that many attention weight mechanisms and word embedding techniques can be seamlessly integrated into our model, demonstrating the robustness and universality of our model.
Is specific antibody determination diagnostic for asthma attributable to low-molecular-weight agents?

It is important to understand a medical testis performance characteristics, so that it can be used appropriately. Performance characteristics of tests for antibodies specific to low-molecular-weight (LMW) agents in predicting asthma caused by these agents differ in the study population. In general, currently published data supporting the use of tests to detect specific IgE and IgG to LMW agents in the diagnosis of occupational asthma is limited and inconclusive. However, a few general statements can be made. The most promising results have been achieved for agents such as acid anhydrides and platinum salts, where specific IgE responses appear to play a significant pathogenic role in causing occupational asthma. Results have been less promising for agents such as isocyanates and plicatic acid, for which antibody responses do not appear to underlie the development of asthma in most individuals. In the case of isocyanates, determination of antigen-specific IgG might have some utility as a biomarker of exposure.
Hallazgo de una Punta “Cola de Pescado” en Magallanes, Chile. Finding of a “Fishtail” Point in Magallanes, Chile.

Se describe el hallazgo de una nueva punta de proyectil “Cola de Pescado”, registrada en una coleccion litica procedente de Magallanes, Chile. Se discute la relevancia de este tipo de registros y su variabilidad morfologica. Finalmente se destaca la necesidad de contar con una base de datos comparativa para las puntas de proyectil paleoindias de Sudamerica.
Face To Face with Next Flu Pandemic with a Wiener-Series-Based Machine Learning: Fast Decisions to Tackle Rapid Spread

It’s well known the potential arrival of the AH1N1 flu disease can be realized in any large city, as well as its unknown impact and consequences on the people. Depending upon the strength of the propagation of virus, clearly it might fade away any scheme of preparedness has been designed. The experience of the 2009 worldwide flu pandemic have served to improve and test newest methodologies that target to toughen the resilience of the public health systems. In this paper we focus on the usage of a Machine Learning algorithm as an advantageous computational system aimed to support fast and effective decisions in epochs where a flu virus has initialized its spreading in a large or middle- size city. For this end the algorithm uses the formalism of the Wiener series that allows us to estimate predictions and thus manage decisions through these computational methodologies. In order to test the efficiency of the algorithm we used the 2009 Peruvian data where the flu A(H1N1) was spreading in Lima city with a velocity of 40 cases per week. We present simulations by which the usage of Machine Learning algorithms might be of importance to minimize undesired errors and optimize resources of public health services on those epochs where the velocity of spreading and number of contagious reaches their top values.
Portable Stress Level Detector based on Galvanic Skin Response, Heart Rate, and Body Temperature

A portable device was designed and developed that measures stress level based from physiological signals gathered from a person. The device uses three physiological signals, the galvanic skin response (GSR), heart rate (HR), and body temperature (BT). The system uses a microcomputer to fetch and process the data, and to compute the stress level using the applied machine learning algorithm. From the results, it was verified that the system provides accurate measurements of the physiological signals with an average percent difference of 0.68338% and 0.19327% for HR and BT respectively.
Day Programs in Italy for Persons with Severe Mental Illness

Translation © 2002 M.E. Sharpe, Inc., from the original Italian. Translated by Stephen Naron. The authors are affiliated with the Coordinamento Nazionale Centri Diurni in Psichiatria. Dr. Maone's address is: Department of Mental Health, ASL Roma/ A, via Sabrata, 12, 00198 Rome, Italy. E-mail: maone@tin.it. This study was encouraged and made possible by the invaluable contribution of members of the Executive Committee for the National Coordination of Psychiatric Day Centers, in the persons of President Giorgio De Isabella, Past President Angelo Cocchi, Vice President Rolando Paterniti, and Secretary Bruno Gentile. Other indispensable contributions were made by the Regional Consultants Patrizia De Rosa and Anselmo Palma (Piemonte), Emanuela Cafiso (Lombardia), Daniela Pisani (Canton Ticino), Daniela Pietropaoli (Liguria), Franco Giubilini (Emilia Romagna), Mario Pettinelli (Marche), Nubia Gomez and Giovanna Le Divelec (Toscana), Paola Carozza and Gianni Carusi (Abruzzo), Rosa Convertini (Puglia), Giuseppe D'Aquino (Campania), Antonio Virzi (Sicilia), Sandro Tagliamonte (Calabria), and the staff at the Borromeo Day Center in Rome. Our particular thanks go to Giovanni de Girolamo for his support and invaluable suggestions.
Original article Effects of exercise testing on natriuretic peptide secretion in patients with atrial fibrillation

Background: Assessment of endocrine profile in patients with cardiovascular diseases has become increasingly important during the last decade. Plasma brain natriuretic peptide (BNP) levels have been used as a marker of left ventricular dysfunction. However, the role of BNP in patients with atrial fibrillation (AF) and normal left ventricular function has not yet been determined. Aim: To examine changes in the secretion of natriuretic peptides (atrial natriuretic peptide – ANP and BNP) during exercise in patients with persistent or permanent AF. Methods: The study group consisted of 42 patients with permanent AF and 77 patients with persistent AF. There were no significant differences in baseline clinical (except AF duration), echocardiographic and haemodynamic data between the groups. The control group comprised 20 patients. All had normal sinus rhytm without a history of AF and were compatible in age, gender and concomitant diseases with the examined groups. The ANP and BNP samples were obtained at rest and at the peak of the exercise testing. Duration of exercise testing was 10 min. Results: The multiple regression analysis showed an association between ANP levels and left atrial volume (p = 0.0001), maximal heart rate (p = 0.0036) and NYHA class (p Conclusions: Neurohormonal response to exercise differs between patients with AF and those in sinus rhythm. Exercise testing may be used to assess the ability of cardiac myocytes to increase peptide secretion.
Pengaruh Kompensasi dan Kepuasan Kerja terhadap Komitmen Organisasional Perawat dan Bidan Kontrak di RS Bhayangkara Hasta Brata Batu Malang

This study aims to determine the effect of compensation and job satisfaction of nurses and midwives to organizational commitment in Hasta Brata Bhayangkara Hospital Batu Malang. This study uses a quantitative approach using descriptive analytic survey with cross sectional study, with 37 respondents contract nurses and midwives in the Bhayangkara Hospital Hasta Brata Batu. The research instrument uses PSQ (Pay Satisfaction Questionnaire) on variable compensation, MSQ (Minnesota Satisfaction Questionare) on job satisfaction variables, and OCS (Organizational Commitment Scale) on the variable organizational commitment. Data analysis is using path analysis. The results show that compensation has siqnifikan effet either directly or indirectly through job satisfaction on organizational commitment of nurses and midwives in hospitals Bhayangkara Hasta Brata Batu Malang.
Inductance of Low-Frequency Small-Scale High-Temperature Superconducting Coils

Inductance is a very commonly used electrical parameter in transient progress analysis, as well as in energy storage evaluations. It was normally considered as constant when the geometrical configuration of a coil is determined. However, experiments on Bi-2223 (Bi<sub>2</sub>Sr<sub>2</sub>Ca<sub>2</sub>Cu<sub>3</sub>O<sub>10+x</sub>) and YBCO (YBa<sub>2</sub>Cu<sub>4</sub>O<sub>7-x</sub>) coils, both show 10% rise inductance while increasing the applied current. The inductance grows approximately a linear relationship from its minimum to maximum. We used both theoretical and experimental analysis to deeply understand the mechanism of the variation in terms of current distribution in high-temperature superconducting (HTS) coils. Taking this inductance change into account would benefit the accuracy in energy storage applications and in transient analysis occasions. Besides, inductance controllable devices could be achieved by manipulating the bias current applied on HTS coils.
Compensation of Phase Noise in Uplink Massive MIMO OFDM Systems

Due to the imperfection of practical oscillators, phase noise (PHN) inevitably occurs in the phase of desired signals, and may incur significant degradation in the performance of wireless communications. This paper addresses the PHN compensation in uplink massive multiple-input multiple-output orthogonal frequency division multiplexing systems, where multiple single-antenna users simultaneously communicate with a base station equipped with a large antenna array. The proposed compensation scheme considers a two-stage transmission protocol. In the first stage, training symbols are transmitted, and the channels of multiple users are estimated along with the PHN sequences involved. In the second stage, information-conveying data symbols are transmitted, and the channel estimates previously obtained are used to estimate the data symbols in the presence of unknown PHN. For both stages, the estimation methods are elaborately derived based on the variational expectation maximization methodology. Moreover, some simplifications are conducted to significantly reduce the computational complexity of the proposed scheme. The effectiveness of the proposed PHN compensation scheme is corroborated through extensive simulations.
Asymptotics of the Random Coding Union Bound

Saddlepoint approximations and expansions of the random coding union bound are derived for the i.i. d. random coding ensemble. Using the inverse Laplace transform of lattice and strongly non-lattice distributions, our results recover the random coding error exponent and refine the pre-exponential coefficient of the error probability. Explicit characterization of the terms are given for the binary symmetric channel and for the binary input AWGN channel.
When Deep Meets Shallow: Subspace-Based Multi-View Fusion for Instance-Level Image Retrieval

Instance-level image retrieval, which aims to find all the images correctly matching the query instance, has attracted extensive attention in the community of computer vision and multimedia. The success of recent retrieval systems largely depends on an image signature with sufficient discriminative power for visual representation. For the past few years, massive feature learning algorithms have sprung up, and they have significantly advanced the development of instance retrieval. In particular, CNN features derived from a deep neural network allows encoding high-level image contents and demonstrates unrivaled superiority to the hand-crafted shallow image signatures. However, the shallow features still play a beneficial role in visual matching particularly when dramatic variances in viewpoint and scale are present, since they inherit certain invariance from the local robust descriptor, e.g. SIFT. Thus, it is important to leverage the complementarity between these two heterogeneous signatures for multi-view fusion in instance retrieval. Since it is still an open problem, in this paper, we propose a subspace-based multi-view fusion strategy where a shared subspace is uncovered from the original high-dimensional features yielding a compact latent representation. Since our method works in an unsupervised fashion without any training process involved, it lends itself to various real-time robotic vision tasks, e.g. place recognition and scene description. Experiments on public benchmark datasets reveal the proposed method works better than other classic fusion approaches and achieves the state-of-the-art performance.
Breaking the Meta: Origins and Influence of Fourth Wall Breaking Games

INTRODUCTION When discussing games, thoughts that often come to mind are how does it utilize and engage methods to make players feel immersed in a virtual and imaginary world. For example, the advanced technologies used for perfect rendering, realistic character movements or vocals, curiosity arising from the storyline in the game and the mastery of feeling in control. Players are usually focused entirely on the game in front of them when becoming immersed in the game. Game designers need to consider is how to utilize challenges to put players in a state of flow, immersing players with a mix of gameplay and narrative. However not many games take advantage of the idea of a human player and the direct interaction that happens between the human player and the digital game.
Perfil respiratório de 457 trabalhadores expostos à poeira de sílica livre no Estado do Rio de Janeiro Respiratory profile of 457 workers exposure to free silica dust in the State of Rio de Janeiro

Introduction: pneumoconioses are a group of diseases caused by fibrogenic dust accumulation in the lungs, producing pulmonary tecidual reaction. The high concentration of particles in suspension, prolonged exposure and individual susceptibility are the major factors that influence the appearance of the disease. The aim of this study is to show the relationship between radiological and functional alterations, the influence of exposure duration in lung function and to present respiratory profile of workers exposed to activities containing silica. Methods: for 01 year (from 2001 to 2002) we have performed a transversal study in which we examined 457 workers, performing several risky activities in the state of Rio de Janeiro, that were referred from clinics of people exposed to free silica. These workers were evaluated through a standardized questionnaire, physical and functional examinations and the Accumulated Exposure Index was calculated. Results: the symptoms most commonly found were expectoration, cough, and wheezing. We found that 22,7% of the x-rays displayed radiological alterations compatible with silicosis and 14,9% had a reduction of the VEF1/CVF ratio. The duration of workers exposure presented statistically significant association with functional alterations. Conclusion: exposure to silica and other mineral dusts, in several occupational activities, contributes to the development of respiratory illness and lung functional alteration.
Gabapentin for the Treatment of Postherpetic Neuralgia

Background: The goal of this study was to evaluate the effects of gabapentin on postherpetic neuralgia. Gabapentin is a known anti-seizure medication, whose cellular mechanism of action is not well understood. Unlike other anticonvulsant, gabapentin has the advantage of a low toxicity and favorable side effect profile. If has been recently recommended for use in treatment of neuropathic pain. Methods: Twelve patients with a diagnosis of postherpetic neuralgia were prescribed gabapentin after failure of routine therapeutic regimens. The dose of gabapentin ranged 300~1800 mg per day, in three divided doses. If initial dose was ineffective and no side effects were noted, the dosages was increased by 300 mg a day in divided doses, to the maximum level for 2 weeks. Patients were evaluated for analgesia using visual analogue scale (VAS) pain score (0; no pain, 10; worst possible pain) and possible side effects. Results: A significant decrease in pain scores with gabapentin were noted. There were several mild side effects such as dizziness, somnolence, dry mouth, constipation and facial edema, without need of special treatment. Conclusions: Gabapentin may be a useful adjunct for treating intractable postherpetic neuralgia with a minimal side effects.
Enhanced Pinning Properties of Sm-Doped GdBCO CCs by the RCE-DR Process

We report the enhanced pinning properties of Sm-doped GdBa 2Cu 3O 7-δ (GdBCO) coated conductors (CCs) compared with those of GdBCO CCs by the reactive co-evaporation deposition and reaction process. For this paper, Sm-doped GdBCO CCs could be fabricated successfully using the same processing parameters with GdBCO CCs. While the GdBCO CCs exhibited higher critical temperature (Tc) and larger self-field critical current (Ic) at 77 K, Sm-doped GdBCO CCs possessed higher infield minimum critical current density (Jc) values in the field region of 1–5 T at 77 K most probably due to a refinement of (Gd,Sm) 2O 3 nanoparticles trapped in the (Gd,Sm)BCO superconducting matrix.
Differential Mechanisms of Potato Yield Loss Induced by High Day and Night Temperatures During Tuber Initiation and Bulking: Photosynthesis and Tuber Growth

The tuber yield of potatoes is vulnerable to high temperature and is challenged by the asymmetric increase in day and night temperatures. This study aimed to evaluate photosynthesis, biomass growth, tuber mass distribution, and dry tuber yield in early harvested potatoes that were field-grown under high day and night temperature conditions during different growth stages. Potatoes were exposed to ambient (control), high night temperature (HNT; 19:00–7:00), high day temperature (HDT; 7:00–19:00), and high day/night temperature (HDNT; all day) for 14 days during tuber initiation (TI) or tuber bulking (TB) using portable, temperature-controlled plastic houses that were controlled to increase the temperature by 4.0°C. During TI, HNT delayed tuber development, thus altering tuber mass distribution by reducing the yield proportion of large tubers of >100 g (-53.7%) and lowering early harvest index (-16.1%), causing a significant yield loss (-17.2%) without interfering with photosynthesis. In contrast, HDT decreased early tuber yield (-18.1%) by reducing photosynthetic sources, which was probably attributed to decreased photosynthetic efficiency through a feedback inhibition. However, HDT altered neither tuber mass distribution nor early harvest index. HDNT during TI exhibited all the aforementioned effects of HNT and HDT (i.e., cumulative effects): reduced yield proportion of large tubers (-46.7%), decreased early harvest index (-23.7%), and reduced photosynthetic rate; thus, HDNT caused the highest yield loss (-30.3%). During TB, when the tubers were fully developed, the thermal effects decreased because most of the effects were either directly or indirectly linked to tuber development. These results provide comprehensive insight to the differential mechanisms of potato yield loss under high day and night temperatures and show that further field experiments should be conducted to cope with the threat of global warming on potato production.