Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Unmanned Aerial Vehicle Remote Control Using Brain–Computer Interface

Numerous methods have been developed to gain reliable real-time remote control over pilotless flying aircraft and to perform teleoperation. Recently, state-of-the-art brain–computer interface (BCI) research has provided an avant-garde approach to reach this goal. Due to its broad range of application, BCI has been the center of attention as a promising candidate for deciphering brain signals into corresponding control commands for various systems. This paper surveys the application of BCI in designing control modules for unmanned aerial vehicles (UAVs). We first describe the basic configuration of UAVs, as well as identify the principle components of their control systems. We proceeded to describe different classes of BCI with emphasis on their applicability in controlling UAVs, and highlight the potential benefits and challenges in implementing each BCI paradigm. Details will be given on how essential strategies and key techniques regarding feature extraction, and the classification of data, as well as hybrid-modality, could be applied in this field to develop robust systems demonstrating optimal fidelity and performance. Moreover, by reviewing the primary trends in previous studies, we attempt to address the missing steps in current research and shed light on the road map for future innovation.
Hierarchical Multitask Learning for CTC-based Speech Recognition

Previous work has shown that neural encoder-decoder speech recognition can be improved with hierarchical multitask learning, where auxiliary tasks are added at intermediate layers of a deep encoder. We explore the effect of hierarchical multitask learning in the context of connectionist temporal classification (CTC)-based speech recognition, and investigate several aspects of this approach. Consistent with previous work, we observe performance improvements on telephone conversational speech recognition (specifically the Eval2000 test sets) when training a subword-level CTC model with an auxiliary phone loss at an intermediate layer. We analyze the effects of a number of experimental variables (like interpolation constant and position of the auxiliary loss function), performance in lower-resource settings, and the relationship between pretraining and multitask learning. We observe that the hierarchical multitask approach improves over standard multitask training in our higher-data experiments, while in the low-resource settings standard multitask training works well. The best results are obtained by combining hierarchical multitask learning and pretraining, which improves word error rates by 3.4% absolute on the Eval2000 test sets.
The boundary of holistic processing in the appraisal of facial attractiveness

Facial attractiveness is often studied on the basis of the internal facial features alone. This study investigated how this exclusion of the external features affects the perception of attractiveness. We studied the effects of two most commonly used methods of exclusion, where the shape of an occluding mask was defined by either the facial outline or an oval. Participants rated attractiveness of the same faces under these conditions. Results showed that faces were consistently rated more attractive when they were masked by an oval shape rather than by their outline (Experiment 1). Attractive faces were more strongly affected by this effect than were less attractive faces when participants were able to control the viewing time. However, unattractive faces benefited more from this effect when the same face stimuli were presented briefly for only 20 ms (Experiment 2). Further manipulation confirmed that the effect was mainly due to the occlusion of a larger area of the external features rather than the regular and symmetrical features of the oval shape (Experiment 3) or lacks contextual cues about the face boundary (Experiment 4). The effect was only relative to masked faces, with no advantage over unmasked faces (Experiment 5), and is likely a result of the interaction between the shape of a mask and the internal features of the face. This holistic effect in the appraisal of facial attractiveness is striking, because the oval shape of the mask is not a part of the face but is the edge of an occluding object.
Structural Hole Spanner in HumanNet Identifies Disease Gene and Drug targets

Traditional methods of analyzing genes mainly focus on a single gene. However, they are time consuming, costly, and neglect the interaction between genes. To overcome the shortcomings of traditional methods, many computational techniques have been proposed for gene analysis. Computational methods posit that the interaction between genes forms a network. By analyzing the topological characteristics of the network, a type of node, “hubs,” is identified. Only a small fraction of nodes is called “hubs,” but they interact with many partners in the network. Several methods are used to find “hubs” in a network. However, they are not comprehensive. Nevertheless, searching for more information about genes in a network remains challenging. In this paper, we integrate HumanNet and a framework to find a structural hole (SH) spanner. We classify genes into three classes (SH, normal, and non-SH). We also classify genes into three classes according to degree and betweenness. Enrichment analysis reveals that the SH genes are enriched for an essential one. Furthermore, enrichment analyses of disease mutations, viruses, and drugs consistently show that the SH genes are preferred targets and are important in cellular state transition. Functional enrichment shows that the biological function that the SH genes over represent is related to cell function. We demonstrate that by finding the structural hole spanner in HumanNet, we can find the SH genes that are essential and indispensable in cellular life. In addition, we identify the SH genes that are key players in cellular state transition.
Postoperative chemotherapy with single-agent fluoropyrimidines after resection of colorectal cancer liver metastases: a meta-analysis of randomised trials

Surgical resection is the only option of cure for patients with metastatic colorectal cancer. Risk of recurrence after metastasectomy is around 75%. Use of adjuvant chemotherapy after metastasectomy is controversial. Aim To address whether adjuvant systemic therapy after colorectal cancer metastasectomy offers any survival benefit compared with surgery alone. Methods Systematic review of literature and meta-analysis of all available randomised evidence. Relative hazards (RHs) were summarised across trials and heterogeneity was assessed with the Q and I2 statistics. Results Five trials were eligible. Three trials, all using single-agent fluoropyrimidine chemotherapy, presented data valuable for analyses. 482 patients were included in the meta-analysis: 238 randomly assigned to receive postoperative chemotherapy and 244 to metastasectomy only. We found no overall survival (OS) benefit with the use of postoperative single-agent fluoropyrimidines compared with surgery alone, even if a trend for benefit was observed (relative hazard (RH)=0.781, 95% CI 0.593 to 1.030, p=0.080). Significant disease-free survival benefit with the use of postoperative chemotherapy was observed (RH=0.645, 95% CI 0.509 to 0.818, p=0.001). No quality of life (QL) data were available. All trials showed accrual delay, two stopped and one recruiting after 10 years. Long follow-up needs were evidenced since OS curves split only after 3.5 years. Conclusions No OS benefit was documented from the use of postoperative monochemotherapy. Metastasectomy alone continues to be the standard of care. Combination chemotherapy regimens should be evaluated along with QL assessment in future trials appropriately designed for long-term accrual and follow-up.
Pruning based Distance Sketches with Provable Guarantees on Random Graphs

Measuring the distances between vertices on graphs is one of the most fundamental components in network analysis. Since finding shortest paths requires traversing the graph, it is challenging to obtain distance information on large graphs very quickly. In this work, we present a preprocessing algorithm that is able to create landmark based distance sketches efficiently, with strong theoretical guarantees. When evaluated on a diverse set of social and information networks, our algorithm significantly improves over existing approaches by reducing the number of landmarks stored, preprocessing time, or stretch of the estimated distances. On Erdos-Renyi graphs and random power law graphs with degree distribution exponent 2 < &bgr; < 3, our algorithm outputs an exact distance data structure with space between T(n5/4) and T(n3/2) depending on the value of &bgr;, where n is the number of vertices. We complement the algorithm with tight lower bounds for Erdos-Renyi graphs and the case when &bgr; is close to two.
Wnt/β-Catenin Signaling and Obesity

Obesity has become epidemic worldwide, which triggers several obesity-associated complications. Obesity is characterized by excess fat storage mainly in the visceral white adipose tissue (vWAT), subcutaneous WAT (sWAT), and other tissues. Myriad studies have demonstrated the crucial role of canonical Wnt/β-catenin cascade in the development of organs and physiological homeostasis, whereas recent studies show that genetic variations/mutations in the Wnt/β-catenin pathway are associated with human metabolic diseases. In this review, we highlight the regulation of updated Wnt/β-catenin signaling in obesity, especially the distinctly depot-specific roles between subcutaneous and visceral adipose tissue under high-fed diet stimulation and WAT browning process.
Dietary Bovine Lactoferrin Reduces Staphylococcus aureus in the Tissues and Modulates the Immune Response in Piglets Systemically Infected with S. aureus

Abstract Background Bovine lactoferrin (bLf) reduces Staphylococcus aureus infection in premature infants and promotes the growth of Bifidobacterium infantis, a predominant infant gut species. We hypothesized that bLf in combination with B. infantis would reduce the severity of systemic S. aureus infection. Objective The aim was to determine the effects of oral administration of bLf and B. infantis on the course of systemic S. aureus infection. Methods Colostrum-deprived piglets were fed formulas containing 4 g whey/L (CON group) or bLf (LF group). One-half of the piglets in each group were gavaged with B. infantis (109 colony-forming units/d), resulting in 2 additional groups (BI or COMB, respectively). On day 7, piglets were intravenously injected with S. aureus. Blood samples were collected preinfection and every 12 h postinfection for immune analyses. Tissue samples were collected on day 12 for analysis of bacterial abundance and gene expression. Results Preinfection, LF piglets had lower serum interleukin 10 (IL-10), a higher percentage of lymphocytes, and a lower percentage of neutrophils than BI or COMB piglets. After infection, dietary bLf increased piglet weight gain, reduced staphylococcal counts in the kidneys, and tended to lower staphylococcal counts in the lungs and heart. Dietary bLf also decreased kidney IL-10 and increased lung interferon γ (IFN-γ) mRNA. B. infantis increased splenic IFN-γ expression. Renal Toll-like receptor 2 was upregulated in BI piglets but not in COMB piglets. Postinfection, BI piglets had increased serum IL-10 and decreased memory T cell populations. LF and COMB piglets had fewer circulating monocytes and B cells than CON or BI piglets. Conclusions Dietary bLf and B. infantis produced independent and tissue-specific effects. Piglets fed bLf alone or in combination with B. infantis mounted a more effective immune response and exhibited lower bacterial abundance. This study provides biological underpinnings to the clinical benefits of bLf observed in preterm infants but does not support B. infantis administration during S. aureus infection.
A Lyra2 FPGA Core for Lyra2REv2-Based Cryptocurrencies

Lyra2REv2 is a hashing algorithm that consists of a chain of individual hashing algorithms and it is used as a proof-of-work function in several cryptocurrencies that aim to be ASIC-resistant. The most crucial hashing algorithm in the Lyra2REv2 chain is a specific instance of the general Lyra2 algorithm. In this work we present the first FPGA implementation of the aforementioned instance of Lyra2 and we explain how several properties of the algorithm can be exploited in order to optimize the design.
Joint jobs scheduling and lightpath provisioning in fog computing micro datacenter networks

With the growth in data volume and latencysensitive applications, fog computing micro datacenters (FC-mDC) have been proposed as a promising solution. The FC-mDC locally processes raw data requested by the computation task. A group of related parallel computation tasks is called a job, and the required raw data for a job request are mostly stored in multiple FC-mDCs. When establishing multiple job requests in the FC-mDC network, effectively managing job scheduling and data transmission is an open challenge. In this paper, a joint optimization algorithm of multiple jobs scheduling and lightpath provisioning (MJSLP) is investigated for minimizing average completion time in FC-mDC elastic optical networks. The MJSLP algorithm includes two key submethods that have a progressive relationship: first, for the minimization of a single job completion time (i.e., the slowest task completion time in a job), a mathematical model and a heuristic algorithm are developed. Then, on the basis of single job completion time, an integer linear programming formulation and a heuristic algorithm of routing, modulation level, and frequency slots are proposed. Simulation results show that the MJSLPalgorithmoutperforms the scheduling-only and routing-only algorithms in terms of average completion time and frequency slots.
Anatomical Variants of “Short Head of Biceps Femoris Muscle” Associated with Common Peroneal Neuropathy in Korean Populations : An MRI Based Study

Objective In Asians, kneeling and squatting are the postures that are most often induce common peroneal neuropathy. However, we could not identify a compatible compression site of the common peroneal nerve (CPN) during hyper-flexion of knees. To evaluate the course of the CPN at the popliteal area related with compressive neuropathy using magnetic resonance imaging (MRI) scans of healthy Koreans. Methods 1.5-Tesla knee MRI scans were obtained from enrolled patients and were retrospectively reviewed. The normal populations were divided into two groups according to the anatomical course of the CPN. Type I included subjects with the CPN situated superficial to the lateral gastocnemius muscle (LGCM). Type II included subjects with the CPN between the short head of biceps femoris muscle (SHBFM) and the LGCM. We calculated the thickness of the SHBFM and posterior elongation of this muscle, and the LGCM at the level of femoral condyles. In type II, the length of popliteal tunnel where the CPN passes was measured. Results The 93 normal subjects were included in this study. The CPN passed through the “popliteal tunnel” formed between the SHBFM and the LGCM in 36 subjects (38.7% type II). The thicknesses of SHBFM and posterior portions of this muscle were statistically significantly increased in type II subjects. The LGCM thickness was comparable in both groups. In 78.8% of the “popliteal tunnel”, a length of 21 mm to <40 mm was measured. Conclusion In Korean population, the course of the CPN through the “popliteal tunnel” was about 40%, which is higher than the Western results. This anatomical characteristic may be helpful for understanding the mechanism of the CPNe by posture.
Methods of Nonlinear Dynamics and the Construction of Cryptocurrency Crisis Phenomena Precursors

This article demonstrates the possibility of constructing indicators of critical and crisis phenomena in the volatile market of cryptocurrency. For this purpose, the methods of the theory of complex systems such as recurrent analysis of dynamic systems and the calculation of permutation entropy are used. It is shown that it is possible to construct dynamic measures of complexity, both recurrent and entropy, which behave in a proper way during actual pre-crisis periods. This fact is used to build predictors of crisis phenomena on the example of the main five crises recorded in the time series of the key cryptocurrency bitcoin, the effectiveness of the proposed indicators-precursors of crises has been identified.
Survey on Deep Learning Techniques for Person Re-Identification Task

Intelligent video-surveillance is currently an active research field in computer vision and machine learning techniques. It provides useful tools for surveillance operators and forensic video investigators. Person re-identification (PReID) is one among these tools. It consists of recognizing whether an individual has already been observed over a camera in a network or not. This tool can also be employed in various possible applications such as off-line retrieval of all the video-sequences showing an individual of interest whose image is given a query, and online pedestrian tracking over multiple camera views. To this aim, many techniques have been proposed to increase the performance of PReID. Among the systems, many researchers utilized deep neural networks (DNNs) because of their better performance and fast execution at test time. Our objective is to provide for future researchers the work being done on PReID to date. Therefore, we summarized state-of-the-art DNN models being used for this task. A brief description of each model along with their evaluation on a set of benchmark datasets is given. Finally, a detailed comparison is provided among these models followed by some limitations that can work as guidelines for future research.
Dual-path Convolutional Image-Text Embeddings with Instance Loss

Matching images and sentences demands a fine understanding of both modalities. In this article, we propose a new system to discriminatively embed the image and text to a shared visual-textual space. In this field, most existing works apply the ranking loss to pull the positive image/text pairs close and push the negative pairs apart from each other. However, directly deploying the ranking loss on heterogeneous features (i.e., text and image features) is less effective, because it is hard to find appropriate triplets at the beginning. So the naive way of using the ranking loss may compromise the network from learning inter-modal relationship. To address this problem, we propose the instance loss, which explicitly considers the intra-modal data distribution. It is based on an unsupervised assumption that each image/text group can be viewed as a class. So the network can learn the fine granularity from every image/text group. The experiment shows that the instance loss offers better weight initialization for the ranking loss, so that more discriminative embeddings can be learned. Besides, existing works usually apply the off-the-shelf features, i.e., word2vec and fixed visual feature. So in a minor contribution, this article constructs an end-to-end dual-path convolutional network to learn the image and text representations. End-to-end learning allows the system to directly learn from the data and fully utilize the supervision. On two generic retrieval datasets (Flickr30k and MSCOCO), experiments demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language-based person retrieval, we improve the state of the art by a large margin. The code has been made publicly available.
Ipsilateral proprioceptive neuromuscular facilitation patterns improve overflow and reduce foot drop in patients with demyelinating polyneuropathy

The purpose of this study was to evaluate the response of the tibialis anterior muscle (TAm) using surface electromyography in patients with Charcot-Marie-Tooth disease (CMT-IA), after ipsilateral proprioceptive neuromuscular patterns (PNF). Thirteen CMT-IA patients (both sexes) were treated twice a week, for 5 weeks, with bilateral PNF pattern, four times per treatment. During the execution of the patterns, we recorded the bilateral activation of the TAm in root mean square (RMS). We used the Student paired t-test for the first and last treatments, P-value set at <0.05. Clinical significance (CS) was obtained by subtracting the values of the first treatment from the last. Chopping pattern to the right side increased RMS for the right (t=−3.52, CS=52%), but not the left TAm (t= −3.35). Flexion-abduction with external rotation pattern to the right (t= −2.46, CS=55%) and left (t=−2.07, CS=53%) significantly increased RMS for TAm on both sides. Extension-adduction with internal rotation pattern to the right (t=−0.25) and left (t=−1.84) did not produce any changes in TAm. Ipsilateral PNF patterns selectively produce TAm overflow in peripheral polyneuropathy patients and can be successfully used as supportive therapy for foot drop.
Quorum-Quenching Bacteria Isolated From Red Sea Sediments Reduce Biofilm Formation by Pseudomonas aeruginosa

Quorum sensing (QS) is the process by which bacteria communicate with each other through small signaling molecules such as N-acylhomoserine lactones (AHLs). Certain bacteria can degrade AHL molecules by a process called quorum quenching (QQ); therefore, QQ can be used to control bacterial infections and biofilm formation. In this study, we aimed to identify new species of bacteria with QQ activity. Red Sea sediments were collected either from the close vicinity of seagrass or from areas with no vegetation. We isolated 72 bacterial strains, which were tested for their ability to degrade/inactivate AHL molecules. Chromobacterium violaceum CV026-based bioassay was used for the initial screening of isolates with QQ activity. QQ activity was further quantified using high-performance liquid chromatography-tandem mass spectrometry. We found that these isolates could degrade AHL molecules of different acyl chain lengths as well as modifications. 16S-rRNA sequencing of positive QQ isolates showed that they belonged to three different genera. Specifically, two isolates belonged to the genus Erythrobacter; four, Labrenzia; and one, Bacterioplanes. The genome of one representative isolate from each genus was sequenced, and potential QQ enzymes, namely, lactonases and acylases, were identified. The ability of these isolates to degrade the 3OXOC12-AHLs produced by Pseudomonas aeruginosa PAO1 and hence inhibit biofilm formation was investigated. Our results showed that the isolate VG12 (genus Labrenzia) is better than other isolates at controlling biofilm formation by PAO1 and degradation of different AHL molecules. Time-course experiments to study AHL degradation showed that VG1 (genus Erythrobacter) could degrade AHLs faster than other isolates. Thus, QQ bacteria or enzymes can be used in combination with an antibacterial to overcome antibiotic resistance.