Miracle-dz/UniFAR-FTUs huggingface dataset:
Another look at the size and book-to-market effects on stock returns

In this study, I test the robustness of size and book-to-market effects on average stock returns reported by Fama and French (FF, 1992, 1993) using a sample that is less subject to survivorship bias and a longer sampling period. Specifically, I exclude NASDAQ stocks in the sample to reduce survivorship bias that has largely been induced by Compustat during an expansion project in 1978. Survivorship bias exists when the sustaining and successful firms are included in the data and those firms that fail or merge with other firms are omitted. Since NASDAQ stocks tend to have relatively smaller stocks than the NYSE and AMEX stocks, they were not included to mitigate such bias. I also test whether size and BE/ME factors are proxied by growth in earnings as studies like FF (1995) and Harris and Marston (1994) reported strong correlation between the two factors and growth. There are two major findings from this study. First, I find that Fama and French results are period and sample specific. Size and book-to-market equity are only significant in the earlier subperiod from 1973 to 1984 but fail to capture variations in average stock returns over the most recent years. Furthermore, the exclusion of the NASDAQ stocks also contributes to the insignificance of size and value premiums as survivorship bias is reduced. Overall, the FF findings are largely driven by the returns of NASDAQ stocks and the earlier sampling period. Second, although growth in earnings is significant in both subperiods, its opposite signs in each subperiod cancel each other’s significance in the overall period. I also did not find that size and BE/ME are proxied by growth in earnings as it fails to absorb the significance of these factors. Finally, Beta is found to be insignificant in the presence of both size and value factors.
Deep learning of resting state networks from independent component analysis

Independent component analysis (ICA) is a powerful technique for analyzing functional networks of the brain. It decomposes resting-state functional magnetic resonance imaging data into distinct networks that are temporally correlated but maximally independent in the spatial domain. Manual classification of ICA components is labor intensive and requires expertise; hence, a fully automatic algorithm that can reliably detect various types of functional brain networks is desirable. In this paper, we introduce a deep Convolutional Neural Network (CNN) method, which provides an automatic solution for identifying resting-state networks extracted using ICA. Our results demonstrate that the proposed CNN method achieves over 98% classification accuracy and out-performs template matching methods.
Divergence of Flowering‐Related Genes in Three Legume Species

We used a set of approximately 200 Arabidopsis thaliana (L.) Heynh. genes that are involved in the control of flowering time as a reference to identify orthologous (or homologous) counterparts of these genes in three legume species, that is, Lotus corniculatus L. var. japonicus Regel [syn. Lotus japonicus (Regel) K. Larsen], Medicago truncatula Gaertn. (barrel medic), and soybean [Glycine max (L.) Merr. ]. A total of 96, 98, and 304 homologs of flowering genes were identified in L. corniculatus var. japonicus, M. truncatula, and G. max, respectively. Most of these genes were categorized into seven different flowering pathways, including photoperiod, vernalization, gibberellins, autonomous pathways, floral pathway integrators (FPIs), and floral meristem identity. Many key genes, including the FPI genes FT, SOC1, and LFY, are conserved in the legumes while CO, FRI, FLC, and FD were not. Eighteen genes were conserved as single copy genes in all three legume species, including GI, VRN2, COP1, and TSF. The chromosomal distribution of paralog‐rich genes revealed differences in the major evolutionary processes affecting flowering genes in legumes, including whole genome duplication in soybean, tandem duplication in M. truncatula, and ectopic duplication in L. corniculatus var. japonicus. High divergence was observed among the members of large gene families, most containing transcription factors, indicating the accumulation of gene copies and gene divergence during evolutionary adaptations to environmental changes.
SVC-based caching and transmission strategy in wireless device-to-device networks

To address the explosively growing demand for mobile traffic, wireless device-to-device (D2D) networks have been introduced, where caching at user devices can be exploited to alleviate the burden on base stations. In this paper, we consider joint caching and transmission of scalable video coding (SVC) streaming over wireless D2D networks. We formulate a joint caching and transmission problem using integer linear programming to minimize the average download time for user, and prove that finding the optimal solution is NP-hard. A heuristic solution is proposed based on a two-phase sub-optimization problem focusing on caching and transmission decisions, which is solved by using relaxed linear programming. Simulation results show that the proposed scheme can significantly reduce the average download time in comparison with existing caching strategies.
Benchmarks and models for entity-oriented polarity detection

We address the problem of determining entity-oriented polarity in business news. This can be viewed as classifying the polarity of the sentiment expressed toward a given mention of a company in a news article. We present a complete, end-to-end approach to the problem. We introduce a new dataset of over 17,000 manually labeled documents, which is substantially larger than any currently available resources. We propose a benchmark solution based on convolutional neural networks for classifying entity-oriented polarity. Although our dataset is much larger than those currently available, it is small on the scale of datasets commonly used for training robust neural network models. To compensate for this, we use transfer learning—pre-train the model on a much larger dataset, annotated for a related but different classification task, in order to learn a good representation for business text, and then fine-tune it on the smaller polarity dataset.
PowerSensor 2: A Fast Power Measurement Tool

PowerSensor 2 is a tool that measures the instantaneous power consumption of PCIe cards and SoC development boards like GPUs, Xeon Phis, FPGAs, DSPs, and network cards, at sub-millisecond time scale. It consists of a commodity microcontroller, commodity current sensors, and (for PCIe devices) a PCIe riser card. The microcontroller reports measurements to the host via USB. A small host library assists an application to determine its own energy efficiency. The high time resolution (up to 8.62 kHz) provides much better insight into energy usage than low-resolution built-in power meters (if available at all), as PowerSensor 2 enables analysis of individual compute kernels.
An Extract of Artemisia dracunculus L. Promotes Psychological Resilience in a Mouse Model of Depression

Stress-induced peripheral inflammation contributes to depression-like behaviors in both human and experimental models. PMI 5011, a botanical extract of Artemisia dracunculus L. , was previously shown to have multiple bioactivities, including anti-inflammatory activity. In this work, using a repeated social defeat stress (RSDS) model of depression, we demonstrate that oral administration of the botanical extract PMI 5011 promotes resilience to RSDS-mediated depression-like phenotypes. We also show that the behavioral improvements are associated with attenuation of stress-mediated induction of inflammatory cytokines in the periphery and alteration of synaptic plasticity in the nucleus accumbens (NAc). Our studies provide experimental evidence that botanical extracts such as PMI 5011, which target pathological mechanisms (i.e., peripheral inflammation) not addressed by currently available antidepressants, could be further developed as novel therapeutics for the treatment of stress disorders and anxiety in humans.
Effect of Aromatherapy with Peppermint Oil on the Severity of Nausea and Vomiting in Pregnancy: A Single-blind, Randomized, Placebo-controlled trial

Background: Nausea and vomiting are common complaints in the first half of pregnancy. These symptoms can significantly affect a person's personal and professional life. Aromatherapy is one of the types of complementary medicine that is used in the treatment of nausea and vomiting. The objective of this study was to determine the effect of aromatherapy with peppermint oil on the severity of nausea and vomiting of pregnancy (NVP). Methods: This was a single-blind clinical trial that was conducted on 56 pregnant women with mild to moderate severity of NVP and 6 to 20 weeks of gestational age. After the determination of gestational age and base severity of NVP in each woman, they were randomly assigned to one of the two groups: peppermint oil (n=28) or placebo (n=28). Inhalation aromatherapy was done for four days and at the end of each day, they responded to the Pregnancy Unique Quantification of Emesis/Nausea questionnaire (PUQE). The data obtained were analyzed with Mann-Whitney test and ANOVA with repeated measures using SPSS software version 22. Also, the level of significance was p<0.05. Results: Although the severity of NVP in each intervention group significantly decreased (p<0.001), the comparison of the severity of NVP during the study period and at the end of it was not statistically significant between the placebo and intervention groups. Conclusion: According to the possibility of neurological mechanisms causing NVP, the effect of aromatherapy with peppermint oil and placebo were the same in this study. This similarity can be due to psychological impacts of intervention on pregnant women.
Ghent University - IBCN Participation in the TAC KBP 2015 Cold Start Slot Filling task

This paper describes the system of team UGENT IBCN for the TAC KBP 2015 Cold Start (slot filling variant) task. The slot filling system uses Distant Supervision to generate training data for feature-based relation classifiers, combined with feature labeling and pattern based extractions. An overall performance 23.3% in micro-mean F1was obtained, which is an increase of 10% compared to the team’s 2014 participation.
Visual Referring Expression Recognition: What Do Systems Actually Learn?

We present an empirical analysis of state-of-the-art systems for referring expression recognition – the task of identifying the object in an image referred to by a natural language expression – with the goal of gaining insight into how these systems reason about language and vision. Surprisingly, we find strong evidence that even sophisticated and linguistically-motivated models for this task may ignore linguistic structure, instead relying on shallow correlations introduced by unintended biases in the data selection and annotation process. For example, we show that a system trained and tested on the input image without the input referring expression can achieve a precision of 71.2% in top-2 predictions. Furthermore, a system that predicts only the object category given the input can achieve a precision of 84.2% in top-2 predictions. These surprisingly positive results for what should be deficient prediction scenarios suggest that careful analysis of what our models are learning – and further, how our data is constructed – is critical as we seek to make substantive progress on grounded language tasks.
Deep neural networks for data association in particle tracking

An essential first step towards understanding intracellular dynamic processes using live-cell time-lapse microscopy imaging is to extract accurate trajectories of all relevant particles in the images. One of the key aspects of this task is to make accurate associations between particle detections across time frames. State-of-the-art methods for this purpose often have many user parameters, sometimes even without a clear biophysical meaning, and/or they use explicit particle motion models that may not reflect reality, making them unfavorable for non-expert users or specific applications. Here we present a novel approach to data association for particle tracking applications based on deep neural networks. Specifically, we propose a recurrent neural network that learns particle behavior from the data, and based on this it determines how to best extend trajectories from one frame to the next. The results of preliminary experiments indicate that our method performs comparable to state-of-the-art data association methods for particle tracking, but with the advantage that it does not require users to tune any parameters.
Network Analysis of MERS Coronavirus within Households, Communities, and Hospitals to Identify Most Centralized and Super-Spreading in the Arabian Peninsula, 2012 to 2016

Contact history is crucial during an infectious disease outbreak and vital when seeking to understand and predict the spread of infectious diseases in human populations. The transmission connectivity networks of people infected with highly contagious Middle East respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia were assessed to identify super-spreading events among the infected patients between 2012 and 2016. Of the 1379 MERS cases recorded during the study period, 321 (23.3%) cases were linked to hospital infection, out of which 203 (14.7%) cases occurred among healthcare workers. There were 1113 isolated cases while the number of recorded contacts per MERS patient is between 1 (n=210) and 17 (n=1), with a mean of 0.27 (SD = 0.76). Five super-important nodes were identified based on their high number of connected contacts worthy of prioritization (at least degree of 5). The number of secondary cases in each SSE varies (range, 5–17). The eigenvector centrality was significantly (p < 0.05) associated with place of exposure, with hospitals having on average significantly higher eigenvector centrality than other places of exposure. Results suggested that being a healthcare worker has a higher eigenvector centrality score on average than being nonhealthcare workers. Pathogenic droplets are easily transmitted within a confined area of hospitals; therefore, control measures should be put in place to curtail the number of hospital visitors and movements of nonessential staff within the healthcare facility with MERS cases.
Minimax open shortest path first routing algorithms in networks supporting the SMDS service

Two quasi-static minimax open shortest path first (OSPF) routing algorithms in networks supporting the Switched Multi-megabit Data Service (SMDS) are presented and compared. In OSPF routing, the network is modeled as a graph and each link is associated with a nonnegative arc weight. A shortest path spanning tree is calculated for each origin to carry both the individually addressed and the group addressed (multicast) traffic. The OSPF routing protocol is adopted as a major part of the default inter-switching system interface (ISSI) routing algorithm for SMDS networks where arc weights are inversely proportional to the aggregate link set capacities. The problem of choosing a set of link set metrics is considered so that the maximum link utilization factor is minimized in an SMDS network. The problem is formulated as a nonlinear mixed integer programming problem. <<ETX>>
Technique for reduction of MRI 3D affine motion artifacts

Patient motion plays a major role in the degradation of image quality provided with magnetic resonance imaging (MRI). This effect is called motion artifact. In this work, we address the problem of MRI artifact arising from a 3-D affine motion. We show that the effect of such a motion is imposing phase error, amplitude distortion, and non uniform sampling to the Fourier domain (k-space) of the acquired MR data. Based on the previous works, a reconstruction algorithm is proposed to reduce the corresponding MRI artifact. In order to investigate the problem and proposed solution, the Poisson and Gaussian probability density functions (PDF) are used to randomly generate the location, amplitude, and number of a subject displacements during data acquisition. Then, the effect of 3-D affine motion parameters on creating MRI artifact and also the performance of the employed technique to suppress the resultant artifact are reported in the paper. © (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Fatigability during volitional walking in incomplete spinal cord injury: cardiorespiratory and motor performance considerations

Fatigability describes the decline in force production (i.e., performance fatigability) and/or changes in sensations regulating performance (i.e., perceived fatigability) during whole-body activity and poses a major challenge to those living with spinal cord injuries (SCI). After SCI, the inability to overcome disruptions to metabolic homeostasis due to cardiorespiratory limitations and physical deconditioning may contribute to increased fatigability severity. The increased susceptibility to fatigability may have implications for motor control strategies and motor learning. Locomotor training approaches designed to reduce fatigability and enhance aerobic capacity in combination with motor learning may be advantageous for promoting functional recovery after SCI. Future research is required to advance the understanding of the relationship between fatigability, cardiorespiratory function and motor performance following SCI.
Review highlights: opinion mining on reviews: a hybrid model for rule selection in aspect extraction

This paper proposes a methodology to extract key insights from user generated reviews. This work is based on Aspect Based Sentiment Analysis (ABSA) which predicts the sentiment of aspects mentioned in the text documents. The extracted aspects are fine-grained for the presentation form known as Review Highlights. The syntactic approach for extraction process suffers from the overlapping chunking rules which result in noise extraction. We introduce a hybrid technique which combines machine learning and rule based model. A multi-label classifier identifies the effective rules which efficiently parse aspects and opinions from texts. This selection of rules reduce the amount of noise in extraction tasks. This is a novel attempt to learn syntactic rule fitness from a corpus using machine learning for accurate aspect extraction. As the model learns the syntactic rule prediction from the corpus, it makes the extraction method domain independent. It also allows studying the quality of syntactic rules in a different corpus.
Estimating Shell-Index in a Graph with Local Information

For network scientists, it has always been an interesting problem to identify the influential nodes in a given network. The k-shell decomposition method is a widely used method which assigns a shell-index value to each node based on its influential power. The k-shell method requires the global information of the network to compute the shell-index of a node that is infeasible for large-scale real-world dynamic networks. In this work, we propose a method to estimate the shell-index of a node using its local information. We also propose hill-climbing based approach to hit the top-ranked nodes in a small number of steps. We further discuss a method to estimate the rank of a node based on the proposed estimator.
Long read assemblies of geographically dispersed Plasmodium falciparum isolates reveal highly structured subtelomeres

Background: Although thousands of clinical isolates of Plasmodium falciparum are being sequenced and analysed by short read technology, the data do not resolve the highly variable subtelomeric regions of the genomes that contain polymorphic gene families involved in immune evasion and pathogenesis. There is also no current standard definition of the boundaries of these variable subtelomeric regions. Methods: Using long-read sequence data (Pacific Biosciences SMRT technology), we assembled and annotated the genomes of 15 P. falciparum isolates, ten of which are newly cultured clinical isolates. We performed comparative analysis of the entire genome with particular emphasis on the subtelomeric regions and the internal var genes clusters. Results: The nearly complete sequence of these 15 isolates has enabled us to define a highly conserved core genome, to delineate the boundaries of the subtelomeric regions, and to compare these across isolates. We found highly structured variable regions in the genome. Some exported gene families purportedly involved in release of merozoites show copy number variation. As an example of ongoing genome evolution, we found a novel CLAG gene in six isolates. We also found a novel gene that was relatively enriched in the South East Asian isolates compared to those from Africa. Conclusions: These 15 manually curated new reference genome sequences with their nearly complete subtelomeric regions and fully assembled genes are an important new resource for the malaria research community. We report the overall conserved structure and pattern of important gene families and the more clearly defined subtelomeric regions.
Adaptive Principal Component Analysis

We develop a new signal modeling method, entropy-constrained adaptive PCA, that has the flexibility to accurately model the cluster structure of non-stationary data. Using a latent data framework, we derive a statistical model for a broad category of real world signals that includes images and measurements from natural processes. Data of this type consists of a collection of low-dimensional patterns embedded in a high-dimensional observation or measurement space. We use this statistical model to develop our adaptive PCA algorithm. Our algorithm adjusts the model parameters to minimize the dimension reduction error between the model and sample data subject to a constraint on the entropy. We evaluate the quality of models porduced by adaptive PCA using image texture data and salinity and temperature measurements from the Columbia river. Compated to entropy-constrained vector quantization, local PCA and full-covariance models, adaptive PCA proved to be a more effective tool for analyzing the salinity and temperature data. In addition, our results show that our model segments texture images as well as entropy-constrained vector quantizers, yet uses substantially fewer model components. Adaptive PCA models conform to the data structure better than full covariance models when training data is sparse.