Miracle-dz/UniFAR-FTUs huggingface dataset:
The design of social hyper-connectivity: Video-ethnography of the uses of Mobile Social Network Sites

This article focuses on digital sociability developed using smartphones and Mobile Social Network Services (mSNS) in daily commuting. A video ethnography was made involving 35 French intensive users of the Facebook app. This method is based on the combination of context-oriented recordings made with user-worn camera glasses with mobile screen capture data. These data reveal how smartphone usage patterns tend to be organized according to mSNS notification devices and how these mediatized forms of social stress lead users to develop " hyper-connected " digital sociability. By examining the distribution of users' attention between urban settings and screen-based activities, this video-ethnography highlights how these notifications affect the organization of visual forms of social coordination in urban settings. We analyze how social forms of visual coordination are not only structured by internalized social norms but also according to rhythms of smartphone apps, how they display relational stress and perform hyper-connected relational practices.
Pituitary-adrenocortical response to stress in the neonatal rat.

Exposure of 2-day-old rats to ether resulted in a small but reliable increment in the corticosterone content of their plasma taken 30 min after the exposure. This increment was greater than the increment due to a handling or heat-stress procedure, and was completely prevented if the animal was pretreated with dexamethasone. Eight-day-old rats also showed an ether-induced, dexamethasone-preventable increment in plasma corticosterone. {Endocri- nology 89: 1126, 1971)
Classifying turn-level uncertainty using word-level prosody

Spoken dialogue researchers often use supervised machine learning to classify turn-level user affect from a set of tur n-level features. The utility of sub-turn features has been less exp lored, due to the complications introduced by associating a variable number of sub-turn units with a single turn-level classifica tion. We present and evaluate several voting methods for using word- level pitch and energy features to classify turn-level user un- certainty in spoken dialogue data. Our results show that when linguistic knowledge regarding prosody and word position is in- troduced into a word-level voting model, classification acc uracy is significantly improved compared to the use of both turn-le vel and uninformed word-level models. Index Terms: emotion recognition, speech dialogue systems.
eHealth Search Patterns: A Comparison of Private and Public Health Care Markets Using Online Panel Data

Background Patient and consumer access to eHealth information is of crucial importance because of its role in patient-centered medicine and to improve knowledge about general aspects of health and medical topics. Objectives The objectives were to analyze and compare eHealth search patterns in a private (United States) and a public (United Kingdom) health care market. Methods A new taxonomy of eHealth websites is proposed to organize the largest eHealth websites. An online measurement framework is developed that provides a precise and detailed measurement system. Online panel data are used to accurately track and analyze detailed search behavior across 100 of the largest eHealth websites in the US and UK health care markets. Results The health, medical, and lifestyle categories account for approximately 90% of online activity, and e-pharmacies, social media, and professional categories account for the remaining 10% of online activity. Overall search penetration of eHealth websites is significantly higher in the private (United States) than the public market (United Kingdom). Almost twice the number of eHealth users in the private market have adopted online search in the health and lifestyle categories and also spend more time per website than those in the public market. The use of medical websites for specific conditions is almost identical in both markets. The allocation of search effort across categories is similar in both the markets. For all categories, the vast majority of eHealth users only access one website within each category. Those that conduct a search of two or more websites display very narrow search patterns. All users spend relatively little time on eHealth, that is, 3-7 minutes per website. Conclusions The proposed online measurement framework exploits online panel data to provide a powerful and objective method of analyzing and exploring eHealth behavior. The private health care system does appear to have an influence on eHealth search behavior in terms of search penetration and time spent per website in the health and lifestyle categories. Two explanations are offered: (1) the personal incentive of medical costs in the private market incentivizes users to conduct online search; and (2) health care information is more easily accessible through health care professionals in the United Kingdom compared with the United States. However, the use of medical websites is almost identical, suggesting that patients interested in a specific condition have a motivation to search and evaluate health information, irrespective of the health care market. The relatively low level of search in terms of the number of websites accessed and the average time per website raise important questions about the actual level of patient informedness in both the markets. Areas for future research are outlined.
The minimax distortion redundancy in noisy source coding

The authors consider a source emitting the sequence of pairs {(X/sub k/,Z/sub k/)}/sub k/spl ges/l/, which are independent drawings of dependent random variables (X, Z), taking values in X/spl times/Z and distributed according to P/sub x,z/. The problem of noisy coding is that of representing the sequence {X/sub k/}/sub k/spl ges/l/ by the sequence {Y/sub k/}/sub k/spl ges/l/ (of alphabet Y) with lowest possible distortion (measured by a given distortion measure /spl rho/), based on observing {Z/sub k/}/sub k/spl ges/l/, while operating at a given information rate. The authors' goal in this work is to answer the immediate questions that arise for the case where P/sub x,z/, rather than being completely specified, is only known to belong to a given set /spl Lambda/.
FCP_DIS_ViT: Efficient Vision Transformer with Neural Network Pruning

Benefiting from the self-attention module, the transformer architecture exhibits extraordinary performance in many computer vision tasks. Compared with mainstream convolutional neural networks, visual transformer usually has a complex structure for extracting powerful feature representations. Although the network performance is improved, it usually requires more computational resources and is more difficult to develop on mobile devices. This study explored the feasibility of implementing the model compression algorithm on the visual transformer and proposed a visual transformer pruning method, which can identify the influence of dimensions in each layer of the transformer and then perform the corresponding pruning. This paper automatically represents important dimensions by promoting the sparsity of the transformer. To achieve higher pruning rates, we can crop many dimensions which have smaller importance scores, without significantly reducing the accuracy. The procedure for pruning visual transformer involves three main steps: (1) incorporating sparse regularization during training; (2) reducing the dimensions of certain features in accordance with a predetermined pruning ratio; and (3) conducting fine-tuning. The parameters of the suggested algorithm, as well as the number of floating-point operations per second, have been thoroughly assessed and analyzed using the ImageNet datasets. This experiment have confirmed the efficacy of the proposed technique.
Random Projection Filter Bank for Time Series Data

We propose Random Projection Filter Bank (RPFB) as a generic and simple approach to extract features from time series data. RPFB is a set of randomly generated stable autoregressive filters that are convolved with the input time series to generate the features. These features can be used by any conventional machine learning algorithm for solving tasks such as time series prediction, classification with time series data, etc. Different filters in RPFB extract different aspects of the time series, and together they provide a reasonably good summary of the time series. RPFB is easy to implement, fast to compute, and parallelizable. We provide an error upper bound indicating that RPFB provides a reasonable approximation to a class of dynamical systems. The empirical results in a series of synthetic and real-world problems show that RPFB is an effective method to extract features from time series.
Tensor Ring Decomposition Based Collaborative Filtering Recommendation with Differential Privacy

Recommendation systems offer a solution to problems such as information overload and lack of relevant information, thereby enhancing sales and improving users satisfaction. Collaborative filtering (CF) uses the preferences of a known user group to predict the preferences of unknown users. At present, many related models have been proposed, with matrix and CP factorizations being particularly popular. But in practical recommendation applications, matrix decomposition is difficult to deal with high-dimensional data, and CP decomposition is limited by its few parameters. To optimize the efficacy of recommendations while still retaining a low level of complexity, we propose employing a tensor ring (TR) decomposition framework for CF recommendation. In this paper, TR decomposition the original tensor to obtain core tensors and a rank vector. To minimize the rating error, a loss function is constructed, and stochastic gradient descent is used to repeatedly update each parameter. In addition, to protect user sensitive information, we employ Laplace random noise to disturbe ratings. By controlling the value of the privacy budget, a balance is achieved between the preservation of data privacy and the utility of data. Extensive experiments show that our approach not only improves recommendations but also protects users privacy by using this perturbation technique.
A Self-Controlled, Naturalistic Study of Selective Serotonin Reuptake Inhibitors versus Tricyclic Antidepressants

Objective: To compare the effectiveness of serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCAs) for anxiety and mood disorders in a naturalistic setting. Methods: 114 of 2,000 outpatients drawn from a private facility with a diagnosis of mood or anxiety disorder had two separate episodes during which they were treated once with a SSRI and once with a TCA. The drugs had to be in monotherapy and appropriate according to the recent guidelines. Key outcome measures included several rating scales, the results of which were combined into three measures of outcome: full response (no symptom), partial response (residual symptoms), poor response. Results: TCAs produced a better response in 63 cases and SSRIs in 18 cases (p < 0.00001). When the outcome was dichotomized, TCAs were still superior (stricter criterion of full response: p = 0.0002; lower threshold: p < 0.0001). Considering depressive and anxiety disorders separately, TCAs remained superior in terms of efficacy (for depression: p < 0.0001; for anxiety: p = 0.026). Moreover, the second episode of illness showed a better outcome than the first (p = 0.0008). Conclusions: In those cases where two different antidepressants were prescribed over two different episodes of illness, TCAs were significantly more effective than SSRIs, regardless of the type of disorder and order of prescription.
Gaussian Process Kernels for Pattern Discovery and Extrapolation

Gaussian processes are rich distributions over functions, which provide a Bayesian nonparametric approach to smoothing and interpolation. We introduce simple closed form kernels that can be used with Gaussian processes to discover patterns and enable extrapolation. These kernels are derived by modelling a spectral density - the Fourier transform of a kernel - with a Gaussian mixture. The proposed kernels support a broad class of stationary covariances, but Gaussian process inference remains simple and analytic. We demonstrate the proposed kernels by discovering patterns and performing long range extrapolation on synthetic examples, as well as atmospheric CO2 trends and airline passenger data. We also show that it is possible to reconstruct several popular standard covariances within our framework.
Improved phonetic and lexical speaker recognition through MAP adaptation

High level features such as phone and word n-grams have been shown to be effective for speaker recognition, particularly when used along side traditional acoustic speaker recognition techniques. The applicability of these high-level recognition systems is impeded by the large training data requirements needed to build robust and stable speaker models. This paper describes an extension to an existing phone n-gram based speaker recognition technique, whereby MAP adaptation is used in the speaker model training process. Results obtained for the NIST 2003 Speaker Recognition Extended Data Task indicate that a significant improvement in performance can be gained through the use of this model estimation technique. In our tests, we were able to improve performance over the baseline system, and at the same time, halved the training data requirement. Further experimentation using MAP adaptation on word n-gram models also showed improvement over baseline results, suggesting that the technique could be applied to other multinomial distribution feature sets.
Evaluating the Use of Large Language Model to Identify Top Research Priorities in Hematology

The field of hematology is highly progressive and dynamic requiring researchers to commit significant amounts of time and effort towards staying abreast of the most crucial research areas. As such, in this work we assess the potential of ChatGPT for identifying research priorities within five key topics in hematology: acute lymphocytic leukemia, immunotherapy, targeted therapy, hematopoietic stem cell transplantation, and acute myeloid leukemia. After ChatGPT was employed to generate specific research questions in these areas, a panel of seven experienced hematologists independently reviewed and rated resultant research questions based on four parameters: relevance, originality, clarity, and specificity on a scale of 1 to 5, with 5 denoting the highest score. Excitingly, the mean and median grades of the four parameters were all above 4, indicating that the hematologists strongly agreed that the problems generated by ChatGPT were generally highly specific, clear, relevant, and original. As such, although further work will clearly be required, we suggest our current study indicates that Large Language Models (LLMs), such as ChatGPT, may very well represent valuable new tools for more efficiently identifying and prioritizing impactful research questions in the field of hematology.
Learning Indoors Free-Space Segmentation for a Mobile Robot from Positive Instances

Accurate indoor free-space segmentation is a demanding task due to the intricate and dynamic nature of indoor environments. We propose an indoors free-space segmentation method that associates large depth values with navigable regions. Our method employs an unsupervised masking technique that, using positive instances, generates segmentation labels based on textural homogeneity and depth uniformity. Using the estimated free-space masks and a Dense Prediction Transformer (DPT) feature representation, a SegFormer model is fine-tuned on our custom-collected indoor dataset. Our experiments demonstrate sufficient performance in complex scenes where the identification of free space is challenging.
A Comprehensive Analysis of Lung Sound Signals

The analysis of lung sound signals is an important approach for diagnosing lung diseases, as these signals cover vital physiological and pathological information. However, conventional methods face challenges in capturing weak physiological sound signals, which can affect diagnosis outcomes and heavily rely on the subjective experience and expertise of the auscultating physician. This paper presents a comprehensive analysis of lung sound signals, covering various aspects such as data acquisition, preprocessing, feature analysis, and classification. It then describes the preprocessing and noise reduction processes applied to lung sound signals, highlighting the classical methods used for feature engineering and real-time lung sound classification. Furthermore, the paper explores more advanced machine learning methods in this context and discusses and compares the relevant literature. So far, this paper has contributed to the advancement of lung sound signal analysis and its potential impact on healthcare outcomes.
URES : an Unsupervised Web Relation Extraction System

Most information extraction systems either use hand written extraction patterns or use a machine learning algorithm that is trained on a manually annotated corpus. Both of these approaches require massive human effort and hence prevent information extraction from becoming more widely applicable. In this paper we present URES (Unsupervised Relation Extraction System), which extracts relations from the Web in a totally unsupervised way. It takes as input the descriptions of the target relations, which include the names of the predicates, the types of their attributes, and several seed instances of the relations. Then the system downloads from the Web a large collection of pages that are likely to contain instances of the target relations. From those pages, utilizing the known seed instances, the system learns the relation patterns, which are then used for extraction. We present several experiments in which we learn patterns and extract instances of a set of several common IE relations, comparing several pattern learning and filtering setups. We demonstrate that using simple noun phrase tagger is sufficient as a base for accurate patterns. However, having a named entity recognizer, which is able to recognize the types of the relation attributes significantly, enhances the extraction performance. We also compare our approach with KnowItAll's fixed generic patterns.
Comparison of open information extraCtion for english and spanish

Open Information Extraction (IE) is the task of extracting relational tuples representing facts from text, with no prior specification of relation, no pre-specified vocabulary, or a manually tagged training corpus. Part-of-speech based systems are shown to be competitive with parsing-based systems on this task and work faster for large-scale corpora. Nevertheless, implementation of such a system requires language-specific information. So far, all work has been done for English. We present a relation extraction al-gorithm for Open IE in Spanish, based on POS-tagged input and semantic constraints. We provide a description of its implementation in an Open IE system for Spanish ExtrHech. We compare its performance with Open IE systems for English, including a comparison on a parallel English-Spanish dataset, and show that the performance is comparable with the state-of-the-art systems, while the system is more robust to noisy input. We give a comparative analysis of errors in extractions for both languages.
Performance of BICM-OFDM Systems in Non-Gaussian Noise and Interference

In this paper, we present a general mathematical framework for performance analysis of systems employing bit-interleaved coded modulation (BICM) and orthogonal frequency division multiplexing (OFDM). The proposed analysis is general enough to include impairment by general types of noise and interference with finite moments such as additive white Gaussian noise (AWGN), Gaussian mixture noise, narrowband interference, and ultra-wideband interference. We present an asymptotically tight upper bound for the bit error rate (BER) and a closed-form expression for the asymptotic BER at high signal-to-noise ratios. Our analysis shows that, if the popular Euclidean distance metric is used for Viterbi decoding, BICM-OFDM systems optimized for AWGN are also optimum for any other type of noise and interference with finite moments.
Effect of PGC-1α on Proliferation, Migration, and Transdifferentiation of Rat Vascular Smooth Muscle Cells Induced by High Glucose

We assessed the role of PGC-1α (PPARγ coactivator-1 alpha) in glucose-induced proliferation, migration, and inflammatory gene expression of vascular smooth muscle cells (VSMCs). We carried out phagocytosis studies to assess the role of PGC-1α in transdifferentiation of VSMCs by flow cytometry. We found that high glucose stimulated proliferation, migration and inflammatory gene expression of VSMCs, but overexpression of PGC-1α attenuated the effects of glucose. In addition, overexpression of PGC-1α decreased mRNA and protein level of VSMCs-related genes, and induced macrophage-related gene expression, as well as phagocytosis of VSMCs. Therefore, PGC-1α inhibited glucose-induced proliferation, migration and inflammatory gene expression of VSMCs, which are key features in the pathology of atherosclerosis. More importantly, PGC-1α transdifferentiated VSMCs to a macrophage-like state. Such transdifferentiation possibly increased the portion of VSMCs-derived foam cells in the plaque and favored plaque stability.
SS18 Together with Animal-Specific Factors Defines Human BAF-Type SWI/SNF Complexes

Background Nucleosome translocation along DNA is catalyzed by eukaryotic SNF2-type ATPases. One class of SNF2-ATPases is distinguished by the presence of a C-terminal bromodomain and is conserved from yeast to man and plants. This class of SNF2 enzymes forms rather large protein complexes that are collectively called SWI/SNF complexes. They are involved in transcription and DNA repair. Two broad types of SWI/SNF complexes have been reported in the literature; PBAF and BAF. These are distinguished by the inclusion or not of polybromo and several ARID subunits. Here we investigated human SS18, a protein that is conserved in plants and animals. SS18 is a putative SWI/SNF subunit which has been implicated in the etiology of synovial sarcomas by virtue of being a target for oncogenic chromosomal translocations that underlie synovial sarcomas. Methodology/Principal Findings We pursued a proteomic approach whereby the SS18 open reading frame was fused to a tandem affinity purification tag and expressed in amenable human cells. The fusion permitted efficient and exclusive purification of so-called BAF-type SWI/SNF complexes which bear ARID1A/BAF250a or ARID1B/BAF250b subunits. This demonstrates that SS18 is a BAF subtype-specific SWI/SNF complex subunit. The same result was obtained when using the SS18-SSX1 oncogenic translocation product. Furthermore, SS18L1, DPF1, DPF2, DPF3, BRD9, BCL7A, BCL7B and BCL7C were identified. ‘Complex walking’ showed that they all co-purify with each other, defining human BAF-type complexes. By contrast,we demonstrate that human PHF10 is part of the PBAF complex, which harbors both ARID2/BAF200 and polybromo/BAF180 subunits, but not SS18 and nor the above BAF-specific subunits. Conclusions/Significance SWI/SNF complexes are found in most eukaryotes and in the course of evolution new SWI/SNF subunits appeared. SS18 is found in plants as well as animals. Our results suggest that in both protostome and deuterostome animals, a class of BAF-type SWI/SNF complexes will be found that harbor SS18 or its paralogs, along with ARID1, DPF and BCL7 paralogs. Those BAF complexes are proteomically distinct from the eukaryote-wide PBAF-type SWI/SNF complexes. Finally, our results suggests that the human bromodomain factors BRD7 and BRD9 associate with PBAF and BAF, respectively.