Miracle-dz/UniFAR-FTUs huggingface dataset:
The Commingled Division of Visual Attention

Many critical activities require visual attention to be distributed simultaneously among distinct tasks where the attended foci are not spatially separated. In our two experiments, participants performed a large number of trials where both a primary task (enumeration of spots) and a secondary task (reporting the presence/absence or identity of a distinctive shape) required the division of visual attention. The spots and the shape were commingled spatially and the shape appeared unpredictably on a relatively small fraction of the trials. The secondary task stimulus (the shape) was reported in inverse proportion to the attentional load imposed by the primary task (enumeration of spots). When the shape did appear, performance on the primary task (enumeration) suffered relative to when the shape was absent; both speed and accuracy were compromised. When the secondary task required identification in addition to detection, reaction times increased by about 200 percent. These results are broadly compatible with biased competition models of perceptual processing. An important area of application, where the commingled division of visual attention is required, is the augmented reality head-up display (AR-HUD). This innovation has the potential to make operating vehicles safer but our data suggest that there are significant concerns regarding driver distraction.
Toward Estimating Autonomous Neural Network-Based Electric Load Forecasters

Anticipation of load's future behavior is very important for decision making in power system operation and planning. During the last 40 years, many different load models have been proposed for short-term forecasting. After 1991, the literature on this subject has been dominated by neural network (NN) based proposals. This is mainly due to the NNs' capacity for capturing the nonlinear relationship between load and exogenous variables. However, one major risk in using neural models is the possibility of excessive training data approximation, i.e., overfitting, which usually increases the out-of-sample forecasting errors. The extent of nonlinearity provided by NN-based load forecasters, which depends on the input space representation, has been adjusted using heuristic procedures. Training early stopping based on cross validation, network pruning methods, and architecture selection based on trial and error are popular. The empirical nature of these procedures makes their application cumbersome and time consuming. This paper develops two nonparametric procedures for solving, in a coupled way, the problems of NN structure and input selection for short-term load forecasting.
A Selective Chromogenic Plate, YECA, for the Detection of Pathogenic Yersinia enterocolitica: Specificity, Sensitivity, and Capacity to Detect Pathogenic Y. enterocolitica from Pig Tonsils

A new selective chromogenic plate, YECA, was tested for its specificity, sensitivity, and accuracy to detect pathogenic Y. enterocolitica from pig tonsils. We tested a panel of 26 bacterial strains on YECA and compared it to PCA, CIN, and YeCM media. Detection of pathogenic Y. enterocolitica was carried out on 50 pig tonsils collected in one slaughter house. Enrichment was done in PSB and ITC broths. Streaking on YECA and CIN was done in direct, after 24H incubation of ITC, after 48H incubation of PSB and ITC. All the plates were incubated at 30°C during 24 hours. Presence of typical colonies on CIN and YECA was checked, and isolates were biotyped. Pathogenic Y. enterocolitica strains showed an important growth on YECA with small and red fuchsia colonies while biotype 1A exhibited very few violet colonies. Enrichment in ITC during 48H gave the best performance for detecting positive samples in pathogenic Y. enterocolitica, and YECA could detect directly pathogenic Y. enterocolitica strains (2, 3, and 4). Use of YECA in combination with ITC generates a time-saver by giving a positive test in 72H.
Effects of Temperature on Survival, Development, Longevity, and Fecundity of Ophraella communa (Coleoptera: Chrysomelidae), a Potential Biological Control Agent Against Ambrosia artemisiifolia (Asterales: Asteraceae)

ABSTRACT Ophraella communa (Coleoptera: Chrysomelidae) is a leaf beetle that is unintentionally introduced in China. It is a potential biological control agent against common ragweed, Ambrosia artemisiifolia (Asterales: Asteraceae). The effects of temperature on the development and fecundity of O. communa were studied at eight constant temperature regimens (15, 20, 22, 25, 28, 30, 32, 36°C) in the laboratory. The results showed that the developmental periods for egg, larva, pupa, and entire immature stages decreased in response to the increasing temperature, with the exception of 30°C. The survival rates at different developmental stages were higher at 25 and 28°C than at other temperatures. Ovipositional period and longevity of female shortened with the increasing temperature. The highest fecundity of female was observed to be 2,712.3 eggs/female at 28°C. Life table of O. communa was constructed based on the data at 20–32°C. The innate capacity for increase (r m), the net reproductive rate (R 0), and the finite rate of increase (&lgr;) reached the maximum at 28°C, with values of 0.247, 1,773.0, and 1.280, respectively. The shortest period of a generation (T) was 24.6 d at 32°C, whereas the longest T value was recorded as 79.3 d at 20°C. These results offer valuable insight on the establishment potential of O. communa in new environments with diverse temperature regimens and on its massrearing techniques in laboratory.
Learning Compressed Sensing

Compressed sensing [7], [6] is a recent set of mathematical results showing that sparse signals can be exactly reconstructed from a small number of linear measurements. Interestingly, for ideal sparse signals with no measurement noise, random measurements allow perfect reconstruction while measurements based on principal component analysis (PCA) or independent component analysis (ICA) do not. At the same time, for other signal and noise distributions, PCA and ICA can significantly outperform random projections in terms of enabling reconstruction from a small number of measurements. In this paper we ask: given a training set typical of the signals we wish to measure, what are the optimal set of linear projections for compressed sensing ? We show that the optimal projections are in general not the principal components nor the independent components of the data, but rather a seemingly novel set of projections that capture what is still uncertain about the signal, given the training set. We also show that the projections onto the learned uncertain components may far outperform random projections. This is particularly true in the case of natural images, where random projections have vanishingly small signal to noise ratio as the number of pixels becomes large.
Metabolic costs and heart rate responses to treadmill walking in water at different depths and temperatures

Treadmill walking/jogging in water is a potentially useful therapeutic modality. Since energy costs of this activity are unknown, we compared oxygen consumption (VO2) of treadmill walking/jogging in water to a dry treadmill at speeds of 40.23 to 160.9 meters/min (m/m) in 13.4 m/m increments in 11 subjects. At speeds ≥53.6 m/m, ankle depth, below knee, midthigh, and waist depth walking/jogging in water significantly elevated VO2 and heart rate (HR) above dry treadmill walking (P < 0.05). At speeds ≥134.1 m/m, VO 2 of waist depth jogging was not significantly greater than dry jogging. These findings showed no gender specificity. Treadmill walk ing/jogging in waist depth water at temperatures of 30.5°C and 36.1 °C was compared to dry treadmill walking in five subjects. The rate of increase of HR compared to VO2 was significantly greater at 30.5°C than dry walking, and greater at 36.1° C than 30.5°C (P < 0.05). Treadmill walking in water can double the oxygen cost of movement depending on the depth and speed, and the response to increasing speed is nonlinear. Water temperature affects the relationship of HR to VO2 at waist depth, suggesting that water temperature can add a significant thermal load to the cardiovascular system. Metabolic and cardiovascular demands of treadmill walking/jogging in water must be considered when using this modality since greater external work results at much lower speeds than on land.
Minimally Invasive Resection of an Extradural Far Lateral Lumbar Schwannoma with Zygapophyseal Joint Sparing: Surgical Nuances and Literature Review

Introduction. Spinal schwannomas are benign nerve sheath tumors. Completely extradural schwannomas of the lumbar spine are extremely rare lesions, accounting for only 0,7–4,2% of all spinal NSTs. Standard open approaches have been used to treat these tumors, requiring extensive muscle dissection, laminectomy, radical foraminotomy, and facetectomy. In this paper the authors present the case of a minimally invasive resection of a completely extradural schwannoma. Operative technique literature review is presented. Material & Methods. A 50-year-old woman presented with progressive complains of chronic right leg pain and paresthesia. The magnetic resonance imaging revealed a giant well-encapsulated dumbbell-shaped extradural lesion at the L3-L4 level. The patient underwent a minimally invasive gross total resection of the tumor using a tubular expandable retractor system. Results. The patient had complete resolution of radiculopathy in the immediate postoperative period and she was discharged home, neurologically intact, on the second postoperative day. Postoperative MRI demonstrated no evidence of residual tumor. At latest follow-up (18 months) the patient remains asymptomatic. Conclusion. Although challenging, this minimally invasive procedure is safe and effective, being an appropriate alternative, with many potential advantages, to the open approach.
The Intestinal Microbiota in Metabolic Disease

Gut bacteria exert beneficial and harmful effects in metabolic diseases as deduced from the comparison of germfree and conventional mice and from fecal transplantation studies. Compositional microbial changes in diseased subjects have been linked to adiposity, type 2 diabetes and dyslipidemia. Promotion of an increased expression of intestinal nutrient transporters or a modified lipid and bile acid metabolism by the intestinal microbiota could result in an increased nutrient absorption by the host. The degradation of dietary fiber and the subsequent fermentation of monosaccharides to short-chain fatty acids (SCFA) is one of the most controversially discussed mechanisms of how gut bacteria impact host physiology. Fibers reduce the energy density of the diet, and the resulting SCFA promote intestinal gluconeogenesis, incretin formation and subsequently satiety. However, SCFA also deliver energy to the host and support liponeogenesis. Thus far, there is little knowledge on bacterial species that promote or prevent metabolic disease. Clostridium ramosum and Enterococcus cloacae were demonstrated to promote obesity in gnotobiotic mouse models, whereas bifidobacteria and Akkermansia muciniphila were associated with favorable phenotypes in conventional mice, especially when oligofructose was fed. How diet modulates the gut microbiota towards a beneficial or harmful composition needs further research. Gnotobiotic animals are a valuable tool to elucidate mechanisms underlying diet–host–microbe interactions.
UvA-DARE (Digital Academic Repository) Self-Supervised Video Representation Learning With Odd-One-Out Networks

We propose a new self-supervised CNN pre-training technique based on a novel auxiliary task called odd-one-out learning . In this task, the machine is asked to identify the unrelated or odd element from a set of otherwise related elements. We apply this technique to self-supervised video representation learning where we sample subsequences from videos and ask the network to learn to predict the odd video subsequence. The odd video subsequence is sampled such that it has wrong temporal order of frames while the even ones have the correct temporal order. Therefore, to generate a odd-one-out question no manual annotation is required. Our learning machine is implemented as multi-stream convolutional neural network, which is learned end-to-end. Using odd-one-out networks, we learn temporal representations for videos that generalizes to other related tasks such as action recognition. On action classiﬁcation, our method obtains 60.3% on the UCF101 dataset using only UCF101 data for training which is approximately 10% better than current state-of-the-art self-supervised learning methods. Similarly, on HMDB51 dataset we outperform self-supervised state-of-the art methods by 12.7% on action classiﬁcation task.
Three-dimensional graphene monolith-based composite: superiority in properties and applications

With the development of graphene research, the three-dimensional graphene monolith (3DGM), which can be prepared by the chemical vapour deposition method (CVD-3DGM) and the self-assembly of reduced graphene oxide nanosheets (RGO-3DGM), has drawn increasing attention because of its properties and application prospects that are better than those of two-dimensional graphene nanosheets. By utilising its excellent electrical property, large BET area and favourable mechanical strength, some research findings on the 3DGM-assisted thermal interface materials, conductive polymers and films, dye-sensitised solar cells and supercapacitors have been reported. In this perspective, we review recent progress in the synthesis and application of 3DGM-based composites and devices. In particular, the advantages of the CVD-3DGM are highlighted after comparing the reports, and the specific reasons are analysed and discussed. Finally, the probable directions of development, research focus and corresponding challenges for the preparation and application of the CVD-3DGM in the future are proposed.
Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation

Monte Carlo tree search (MCTS) is extremely popular in computer Go which determines each action by enormous simulations in a broad and deep search tree. However, human experts select most actions by pattern analysis and careful evaluation rather than brute search of millions of future interactions. In this paper, we propose a computer Go system that follows experts’ way of thinking and playing. Our system consists of two parts. The first part is a novel deep alternative neural network (DANN) used to generate candidates of next move. Compared with existing deep convolutional neural network (DCNN), DANN inserts recurrent layer after each convolutional layer and stacks them in an alternative manner. We show such setting can preserve more contexts of local features and its evolutions which are beneficial for move prediction. The second part is a long-term evaluation (LTE) module used to provide a reliable evaluation of candidates rather than a single probability from move predictor. This is consistent with human experts’ nature of playing since they can foresee tens of steps to give an accurate estimation of candidates. In our system, for each candidate, LTE calculates a cumulative reward after several future interactions when local variations are settled. Combining criteria from the two parts, our system determines the optimal choice of next move. For more comprehensive experiments, we introduce a new professional Go dataset (PGD), consisting of $253,233$ professional records. Experiments on GoGoD and PGD datasets show the DANN can substantially improve performance of move prediction over pure DCNN. When combining LTE, our system outperforms most relevant approaches and open engines based on MCTS.
Probing Meiotic Recombination and Aneuploidy of Single Sperm Cells by Whole-Genome Sequencing

Single-Cell Sequencing With the rapid progress in sequencing technologies, single-cell sequencing is now possible, promising insight into how cell-to-cell heterogeneity affects biological behavior. Achieving adequate genome coverage remains a challenge because single-cell sequencing relies on genome amplification that is prone to sequence bias. Zong et al. (p. 1622) report a new amplification method: multiple annealing and looping-based amplification cycles that allowed 93% genome coverage for a human cell. This coverage facilitated accurate detection of point mutations and copy number variations. Lu et al. (p. 1627) used the method to sequence 99 sperm cells from a single individual. Mapping the meiotic crossovers revealed a nonrandom distribution with a reduced recombination rate near transcription start sites. A whole-genome amplification method with reduced bias yields a personal meiotic recombination map. Meiotic recombination creates genetic diversity and ensures segregation of homologous chromosomes. Previous population analyses yielded results averaged among individuals and affected by evolutionary pressures. We sequenced 99 sperm from an Asian male by using the newly developed amplification method—multiple annealing and looping-based amplification cycles—to phase the personal genome and map recombination events at high resolution, which are nonuniformly distributed across the genome in the absence of selection pressure. The paucity of recombination near transcription start sites observed in individual sperm indicates that such a phenomenon is intrinsic to the molecular mechanism of meiosis. Interestingly, a decreased crossover frequency combined with an increase of autosomal aneuploidy is observable on a global per-sperm basis.
An empirical analysis of word error rate and keyword error rate

This paper studies the relationship between word error rate (WER) and keyword error rate (KER) in speech transcripts and their effect on the performance of speech analytics applications. Automatic speech recognition (ASR) systems are increasingly used as input for speech analytics, which raises the question of whether WER or KER is the more suitable performance metric for calibrating the ASR system. ASR systems are typically evaluated in terms ofWER.Many speech analytics applications, however, rely on identifying keywords in the transcripts—thus their performance can be expected to be more sensitive to keyword errors than regular word errors. To study this question, we conduct a case study using an experimental data set comprising 100 calls to a contact center. We first automatically extract domain-specific words from the manual transcription and use this set of words to calculate keyword error rates in the following experiments. We then generate call transcripts with the IBM Attila speech recognition system, using different training for each repetition to generate transcripts with a range of word error rates. The transcripts are then processed with two speech analytics applications, call section segmentation and topic categorization. The results show similar WER and KER in high-accuracy transcripts, but KER increases more rapidly than WER as the accuracy of the transcription deteriorates. Neither speech analytics application showed significant sensitivity to the increase in KER for low-accuracy transcripts. Thus this case study did not identify a significant difference between using WER and KER.
Serum Lipid Profiles Poorly Correlate With Chlamydia pneumoniae, Helicobacter pylori, and Cytomegalovirus Seropositivity in Prospectively Followed-Up Healthy Children

Objective—Chronic Chlamydia pneumoniae (Cpn), Helicobacter pylori (Hp), and herpes virus infections have been associated with atherogenic serum lipid profile and an excess of cardiovascular events in adults. Because mechanisms leading to atherosclerosis are active since early childhood, we examined whether Cpn, Hp, or cytomegalovirus (CMV) seropositivity relates to serum lipid, lipoprotein, or apolipoprotein concentrations in children. We also looked for factors increasing probability of Cpn seropositivity in children. Methods and Results—Cpn-specific IgG and IgA, as well as Hp-specific and CMV-specific IgG antibodies were assessed by enzyme immunoassay in 199 apparently healthy children, followed-up from 7 to 11 years of age. Serum lipid profiles were studied at the ages of 7, 9, and 11 years using standard methods. Neither seroconversion to Cpn IgG or IgA antibody positivity nor persistent seropositivity for Cpn, Hp, or CMV was associated with proatherogenic serum lipid values. Children with siblings were more likely to possess Cpn antibodies than children without siblings (IgG: OR, 5.24; 95% CI, 1.63 to 16.82; IgA: OR, 3.32; 95% CI, 1.15 to 9.57). Conclusions—These data suggest that contrary to the observations in adults, Cpn, Hp, and CMV seropositivity in otherwise healthy children is not associated with disturbances in serum lipid profile.
Discharge patterns and recruitment order of identified motoneurons and internuclear neurons in the monkey abducens nucleus.

1. Single neurons in the abducens nucleus were recorded extracellularly in alert rhesus macaques trained to make a variety of eye movements. An abducens neurons was identified as a motoneuron (MN) if its action potentials triggered an averaged EMG potential in the lateral rectus muscle. Abducens internuclear neurons (INNs) that project to the oculomotor nucleus were identified by collision block of spontaneous with antidromic action potentials evoked with a stimulating electrode placed in the medial rectus subdivision of the contralateral oculomotor nucleus. 2. All abducens MNs and INNs had qualitatively similar discharge patterns consisting of a burst of spikes for lateral saccades and a steady firing whose rate increased with lateral eye position in excess of a certain threshold. 3. For both MNs and INNs the firing rates associated with different, constant eye positions could be described accurately by a straight line with slope, K (the eye position sensitivity in spikes.s-1.deg-1), and intercept, T (the eye position threshold for steady firing). For different MNs, K increased as T varied from more medial to more lateral values. In contrast, the majority of INNs already were active for values of T more medial than 20 degrees and showed little evidence of recruitment according to K. 4. During horizontal sinusoidal smooth-pursuit eye movements, both MNs and INNs exhibited a sinusoidal modulation in firing rate whose peak preceded eye position. From these firing rate patterns, the component of firing rate related to eye velocity, R (the eye velocity sensitivity in spikes.s-1.deg-1.s-1), was determined. The R for INNs was, on average, 78% larger than that for MNs. Furthermore, R increased with T for MNs, whereas INNs showed no evidence of recruitment according to R. If, as in the cat, the INNs of monkeys provide the major input to medial rectus MNs and if simian medial rectus MNs behave like our abducens MNs, then recruitment order, which is absent in INNs, must be established at the MN pool itself. 5. Unexpectedly, the R of MNs decreased with the frequency of the smooth-pursuit movement. Furthermore, the eye position sensitivity, K, obtained during steady fixations was usually less than that determined during smooth pursuit. Therefore, conclusions about the roles of MNs and premotor neurons based on how their R and K values differ must be viewed with caution if the data have been obtained under different tracking conditions. (ABSTRACT TRUNCATED AT 400 WORDS)