Miracle-dz/UniFAR-FTUs huggingface dataset:
The Anxiety Sensitivity Index-3: Factor structure and psychometric properties in Italian clinical and non-clinical samples

ᴥ ABSTRACT. Lo studio descrive le caratteristiche psicometriche dell’Anxiety Sensitivity Index 3 (ASI-3) che e stato tradotto in italiano con la procedura della back-translation e somministrato ad un campione misto di pazienti con disturbi d’ansia (n = 154) e soggetti non clinici (n = 629), assieme a misure di anxiety sensitivity, depressione e ansia. L’analisi fattoriale confermativa ha confermato la struttura con un fattore generale e tre sotto-fattori (fisico, mentale e sociale). L’ASI-3 ha dimostrato buone proprieta psicometriche. Tutti i gruppi di pazienti hanno ottenuto punteggi totali superiori rispetto al gruppo non clinico; pazienti con Disturbo di Panico e Disturbo d’Ansia Generalizzato hanno riportato punteggi totali piu elevati rispetto agli altri gruppi clinici (Disturbo Ossessivo Compulsivo e Fobia Sociale). Versione integrale in lingua italiana su www.giuntios. it/bpa
Maximum negentropy beamforming with superdirectivity

This paper presents new superdirective beamforming algorithms based on the maximum negentropy (MN) criterion for distant automatic speech recognition. The MN beamformer is configured in the generalized sidelobe canceler structure, and uses the weights derived from a delay-and-sum beamformer as the quiescent weight vector. While satisfying the distortionless constraint in the look direction, it adjusts the active weight vector to make the output maximally super-Gaussian. The current paper proposes to use the weights of a superdirective beamformer as the quiescent vector, which results in improved directivity and noise suppression at lower frequencies. We demonstrate the effectiveness of our approach through far-field speech recognition experiments on the Multi-Channel Wall Street Journal Audio Visual Corpus (MC-WSJ-AV). The technique proposed in the current paper reduces the word error rate (WER) by 56% relative to a single distant microphone baseline, which is a 14% reduction in WER over the original MN beamformer formulation.
Communication Flows : Analysis of an Email Network

Communications networks as diverse as wired telephone syst ms and online chat forums record information about every message that they carry, creating e normous, relentlessly growing streams of data. Hidden (often deeply hidden) in these streams is a rich , dynamic view of the people who use the network and their patterns of interaction, but tools for extracting, analyzing and visualizing that information are lacking. Email communications present par icularly challenging problems, because even basic information, such as the identities of senders and rec ipients of a message, must be pieced together by parsing log files on multiple servers across time. People m ay have multiple aliases that have to be translated to unique identifiers, and lists of group recip i nts may have to be exploded to individual people. Moreover, the ratio of spam, which is of little use fo r understanding patterns of communication, to useful messages is high even with aggressive spam filters. Thi report describes a statistical approach to meeting these challenges for an email history derived fro m 75 days of log files for several mail servers that handle email for hundreds of people in the research orga nization at Bell Labs. Some of the results about the structure of the email network are surprising and c o tradict common assumptions about social network behavior. The email exchanges for a subset of addres ses that exchange email with each other and with addresses outside the corporation are analyzed in d etail, providing a firm basis for building statistical descriptions of normal email activity that can be used to identify anomalous behavior.
Casual stereoscopic panorama stitching

This paper presents a method for stitching stereoscopic panoramas from stereo images casually taken using a stereo camera. This method addresses three challenges of stereoscopic image stitching: how to handle parallax, how to stitch the left- and right-view panorama consistently, and how to take care of disparity during stitching. This method addresses these challenges using a three-step approach. First, we employ a state-of-the-art stitching algorithm that handles parallax well to stitch the left views of input stereo images and create the left view of the final stereoscopic panorama. Second, we stitch the input disparity maps to obtain the target disparity map for the stereoscopic panorama by solving a Poisson's equation. This target disparity map is optimized to avoid vertical disparities and preserve the original perceived depth distribution. Finally, we warp the right views of the input stereo images and stitch them into the right-view panorama according to the target disparity map. The stitching of the right views is formulated as a labeling problem that is constrained by the stitching of the left views to make the left- and right-view panorama consistent to avoid “retinal rivalry”. Our experiments show that our method can effectively stitch casually taken stereo images and produce high-quality stereo panoramas that deliver a pleasant stereoscopic 3D viewing experience.
Volumetric Texture Segmentation by Discriminant Feature Selection and Multiresolution Classification

In this paper, a multiresolution volumetric texture segmentation (M-VTS) algorithm is presented. The method extracts textural measurements from the Fourier domain of the data via subband filtering using an orientation pyramid (Wilson and Spann, 1988). A novel Bhattacharyya space, based on the Bhattacharyya distance, is proposed for selecting the most discriminant measurements and producing a compact feature space. An oct tree is built of the multivariate features space and a chosen level at a lower spatial resolution is first classified. The classified voxel labels are then projected to lower levels of the tree where a boundary refinement procedure is performed with a three-dimensional (3-D) equivalent of butterfly filters. The algorithm was tested with 3-D artificial data and three magnetic resonance imaging sets of human knees with encouraging results. The regions segmented from the knees correspond to anatomical structures that can be used as a starting point for other measurements such as cartilage extraction
Job Strain, Overtime, Life Style, and Cardiovascular Risk in Managers and Physical Workers

The purpose of the study was to determine the relation between overtime, job strain and life style, and cardiovascular risk (CVR) in 97 managers and 98 physical workers. CVR was measured with the Framingham method. Information about job strain, overtime, life style and extra-occupational activities was obtained with a self-administered questionnaire. The results showed that both groups had a similar, medium-level job stress. Being a manager and having extra-occupational activities (self-education) were significantly related with CVR (p = .000, p = .035, respectively), whereas other factors that were analysed (i.e., physical work and overtime) were not. The managers were older than the physical workers; that may be why the factor of being a manager was significantly related to CVR. The extra-occupational activities connected with improving workersʼ skills may play an important role in the development of workersʼ overload and an increase in CVR.
Krypton: A Functional Approach to Knowledge Representation

A great deal of effort has focused on developing frame-based languages for knowledge representation. While the basic ideas of frame systems are straightforward, complications arise in their design and use. The authors have developed a design strategy for avoiding these types of problems and have implemented a representation system based on it. The system, called Krypton, clearly distinguishes between definitional and factual information. In particular, Krypton has two representation languages, one for forming descriptive terms and one for making statements about the world using these terms. Further, Krypton provides a functional view of a knowledge base, characterized in terms of what it can be asked or told, rather than in terms of the particular structures it uses to represent knowledge. 11 references.
Grouping Separated Frequency Components by Estimating Propagation Model Parameters in Frequency-Domain Blind Source Separation

This paper proposes a new formulation and optimization procedure for grouping frequency components in frequency-domain blind source separation (BSS). We adopt two separation techniques, independent component analysis (ICA) and time-frequency (T-F) masking, for the frequency-domain BSS. With ICA, grouping the frequency components corresponds to aligning the permutation ambiguity of the ICA solution in each frequency bin. With T-F masking, grouping the frequency components corresponds to classifying sensor observations in the time-frequency domain for individual sources. The grouping procedure is based on estimating anechoic propagation model parameters by analyzing ICA results or sensor observations. More specifically, the time delays of arrival and attenuations from a source to all sensors are estimated for each source. The focus of this paper includes the applicability of the proposed procedure for a situation with wide sensor spacing where spatial aliasing may occur. Experimental results show that the proposed procedure effectively separates two or three sources with several sensor configurations in a real room, as long as the room reverberation is moderately low.
Incorporating Symbolic Sequential Modeling for Speech Enhancement

In a noisy environment, a lossy speech signal can be automatically restored by a listener if he/she knows the language well. That is, with the built-in knowledge of a "language model", a listener may effectively suppress noise interference and retrieve the target speech signals. Accordingly, we argue that familiarity with the underlying linguistic content of spoken utterances benefits speech enhancement (SE) in noisy environments. In this study, in addition to the conventional modeling for learning the acoustic noisy-clean speech mapping, an abstract symbolic sequential modeling is incorporated into the SE framework. This symbolic sequential modeling can be regarded as a "linguistic constraint" in learning the acoustic noisy-clean speech mapping function. In this study, the symbolic sequences for acoustic signals are obtained as discrete representations with a Vector Quantized Variational Autoencoder algorithm. The obtained symbols are able to capture high-level phoneme-like content from speech signals. The experimental results demonstrate that the proposed framework can obtain notable performance improvement in terms of perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI) on the TIMIT dataset.
Context sensitive synonym discovery for web search queries

We propose a simple yet effective approach to context sensitive synonym discovery for Web search queries based on co-click analysis; i.e., analyzing queries leading to clicking same documents. In addition to deriving word based synonyms, we also derive concept based synonyms with the help of query segmentation. Evaluation results show that this approach dramatically outperforms the thesaurus based synonym replacement method in keeping search intent, from accuracy of 40% to above 80%.
Cooperative bin-picking with Time-of-Flight camera and impedance controlled DLR lightweight robot III

Because bin-picking effectively mirrors great challenges in robotics, it has been a relevant robotic showpiece application for several decades. In this paper we describe the computer vision algorithms in combination with the sophisticated control schemes of the robot and demonstrate a reliable and robust solution to the chosen problem. This paper approaches the bin-picking issue by applying the latest state-of-the-art hardware components, namely an impedance controlled lightweight robot and a Time-of-Flight camera. Lightweight robots have gained new capabilities in both sensing and actuation without suffering a decrease in speed and payload. Time-of- Flight cameras are superior to common proximity sensors in the sense that they provide depth and intensity images in video frame rate independent of textures. The bin-picking solution presented in this paper aims at extending the classical bin-picking problem by incorporating an environment model and allowing for the physical human-robot interaction during the entire process. Existing imprecisions in Time-of-Flight camera measurements and environment uncertainties are compensated by the compliant behavior of the robot. The overall process is implemented in a generic state machine that also monitors the entire bin-picking process.
Improving Early Navigation in Time-Lapse Video with Spread-Frame Loading

Time-lapse videos are often navigated by scrubbing with a slider. When networks are slow or images are large, however, even thumbnail versions load so slowly that scrubbing is limited to the start of the video. We developed a frame-loading technique called spread-loading that enables scrubbing regardless of delivery rate. Spread-loading orders frame delivery to maximize coverage of the entire sequence; this provides a temporal overview of the entire video that can be fully navigated at any time during delivery. The overview initially has a coarse temporal resolution, becoming finer-grained with each new frame. We compared spread-loading with traditional linear loading in a study where participants were asked to find specific episodes in a long time-lapse sequence, using three views with increasing levels of detail. Results show that participants found target episodes significantly and substantially faster with spread-loading, regardless of whether they could click to change the load point. Users rated spread-loading as requiring less effort, and strongly preferred the new technique.
Multi-Timescale Collaborative Tracking

We present the multi-timescale collaborative tracker for single object tracking. The tracker simultaneously utilizes different types of “forces”, namely attraction, repulsion and support, to take advantage of their complementary strengths. We model the three forces via three components that are learned from the sample sets with different timescales. The long-term descriptive component attracts the target sample, while the medium-term discriminative component repulses the target from the background. They are collaborated in the appearance model to benefit each other. The short-term regressive component combines the votes of the auxiliary samples to predict the target’s position, forming the context-aware motion model. The appearance model and the motion model collaboratively determine the target state, and the optimal state is estimated by a novel coarse-to-fine search strategy. We have conducted an extensive set of experiments on the standard 50 video benchmark. The results confirm the effectiveness of each component and their collaboration, outperforming current state-of-the-art methods.
The CMU Statistical Machine Translation System for IWSLT2005

In this paper we describe the CMU statistical machine translation system used in the IWSLT 2005 evaluation campaign. This system is based on phrase-to-phrase translations extracted from a bilingual corpus. We experimented with two different phrase extraction methods; PESA on-the-ﬂy phrase extraction and alignment free extraction method. The translation model, language model and other features were combined in a log-linear model during decoding. We present our experiments on model adaptation for new data in a different domain, as well as combining different translation hypotheses to obtain better translations. We participated in the supplied data track for manual transcriptions in the translation directions: Arabic-English, Chinese-English, Japanese-English and Korean-English. For Chinese-English direction we also worked on ASR output of the supplied data, and with additional data in unrestricted and C-STAR tracks.
MicroRNA-93-5p increases multidrug resistance in human colorectal carcinoma cells by downregulating cyclin dependent kinase inhibitor 1A gene expression

Multidrug resistance (MDR) impedes successful chemotherapy in colorectal carcinoma (CRC) and emerging evidence suggests that microRNAs (miRs) are involved in the development of MDR. In the present study, the role of miR-93-5p in the modulation of drug resistance in CRC was investigated using HCT-8 and MDR HCT-8/vincristine (VCR) cell lines. The results demonstrated upregulated expression of miR-93-5p and MDR protein 1 (MDR1) in HCT-8/VCR cells, compared with the parental HCT-8 cells. Furthermore, cyclin-dependent kinase inhibitor 1A (CDKN1A) was identified as a potential target of miR-93-5p using miR target analysis tools, including PicTar, TargetScan and miRanda. In addition, inhibition of miR-93-5p expression in HCT-8/VCR cells markedly downregulated MDR1 gene expression, upregulated CDKN1A gene expression and induced cell cycle arrest in G1. Conversely, the overexpression of miR-93-5p in HCT-8/VCR cells upregulated MDR1 gene expression, downregulated CDKN1A gene expression and promoted G1/S transition. Furthermore, the in vitro drug sensitivity assay performed suggested that downregulation of miR-93-5p enhanced the sensitivity of HCT-8/VCR cells to VCR, while the upregulation of miR-93-5p decreased the sensitivity of HCT-8 cells to VCR. In conclusion, the results of the present study suggest that miR-93-5p serves a role in the development of MDR through downregulating CDKN1A gene expression in CRC.
A Column Generation Method for the Parallel-Machine Scheduling Problem with availability constraint

In this paper, we study the problem of scheduling jobs on identical parallel machines with the objective of minimizing the total completion times of jobs where each machine is subject to one unavailability time interval. We provide a column generation based method to obtain a lower bound for the problem. Computational experiments show the interest and the versatility of the proposed method
PerspectivesX: A Proposed Tool to Scaffold Collaborative Learning Activities within MOOCs

In this work-in-progress paper, we introduce the PerspectivesX tool which aims to scaffold collaborative learning activities within MOOCs. The PerspectivesX tool has been designed to promote learner knowledge construction and curation for a range of multi-perspective elaboration techniques (e.g., SWOT analysis and Six Thinking Hats). The PerspectivesX tool is designed to store learner submissions in a searchable knowledge base which is able to be persisted across course re-runs and promotes the use of natural language processing techniques to allow course moderators to provide scalable feedback. In this paper we outline the design principles that structured collaborative learning tools need to adhere to, design a prototype tool (PerspectivesX) and evaluate whether MOOC platform extension frameworks are able to support the implementation of the tool.
Electrosurgical applications in Dentistry

: Electrosurgery which can also be called as radiosurgery has been used in dentistry for more than 50years. Electrosurgery is a controlled precise application of radiofrequency electrical current to the soft tissue site to be cut which is achieved by means of carefully designed electrodes. Electrosurgery is used in almost all branches of dentistry. Electrosurgery is a continuously evolving field with active research into various new applications.
Ultrasound guided percutaneous thrombin injection for the treatment of iatrogenic pseudoaneurysms

Iatrogenic aneurysms are usually postcatheterisation pseudoaneurysms of the femoral artery. Until recently, the treatment of choice was ultrasound guided compression repair. A case of pseudoaneurysm of the axillary artery, arising as a complication of pacemaker insertion in an 83 year old man is reported. Compression repair was not possible in this case, and so the aneurysm was occluded by percutaneous ultrasound guided thrombin injection directly into the aneurysm sac. Percutaneous ultrasound guided thrombin injection is a promising new minimally invasive technique for the treatment of iatrogenic pseudoaneurysms.
Infrastructure Planning for Electric Vehicles with Battery Swapping

The transportation sector is a major source of greenhouse gas (GHG) emissions. As a step toward a greener environment, solutions involving electric vehicles (EVs) have been proposed and discussed. When powered by electricity from efficient and environmentally-friendly generators, EVs have significantly lower per-mile running costs compared to gasoline cars, while generating lower emissions. Unfortunately, due to the limited capacity of batteries, typical EVs can only travel for about 100 miles on a single charge. Because recharging takes several hours, it is impossible to recharge an EV in the middle of a long (round) trip exceeding 100 miles. Better Place (BP), a start-up based in Palo Alto, CA, proposed a novel strategy that potentially overcomes the recharging problem. In the plan, in addition to charging adaptors at homes, work places and shopping malls, "swapping stations'', at which depleted batteries can be exchanged for recharged ones in the middle of long trips, will be located at strategic locations along freeways. With its battery swapping equipment, BP has demonstrated how to effectively refuel an EV in less than two minutes. The possible success of EV solutions based on the idea of battery swapping hinges on the ability of the charging service provider (BP or other similar firms) to deploy a cost-effective infrastructure network with comprehensive coverage. Unfortunately, since the adoption rate of electric vehicles, and thus demand for swapping service, is still highly uncertain, the service provider must make deployment plans with incomplete information on hand. In this paper, we develop models that aid the planning process for deploying battery swapping infrastructure, based on a robust optimization framework. We further show that our models can be tightly approximated by mixed-integer second-order cone programs (MISOCPs), which are readily solvable by commercial solvers. Using these models, we demonstrate the potential impacts of battery standardization and various technology advancements on the optimal infrastructure deployment strategy.
hpGEM---A software framework for discontinuous Galerkin finite element methods

hpGEM, a novel framework for the implementation of discontinuous Galerkin finite element methods (FEMs), is described. We present data structures and methods that are common for many (discontinuous) FEMs and show how we have implemented the components as an object-oriented framework. This framework facilitates and accelerates the implementation of finite element programs, the assessment of algorithms, and their application to real-world problems. The article documents the status of the framework, exemplifies aspects of its philosophy and design, and demonstrates the feasibility of the approach with several application examples.
Economies of Density and Productivity in Service Industries: An Analysis of Personal Service Industries Based on Establishment-Level Data

Abstract This study aims to empirically investigate the determinants of service industry productivity, such as economies of scale and economies of density. By using establishment-level data related to personal service industries in Japan, the study estimates the production functions for both value-added and physical output measures. In almost all the service industries examined, significant economies of scale and economies of density are observed, wherein productivity increases by 7% to 15% when the municipality population density doubles. These findings are confirmed by an estimation in which the measures of physical output are considered instead of value added.