Miracle-dz/UniFAR-FTUs huggingface dataset:
LOSGSR: Learned Opinion Score Guided Shoeprint Retrieval

As a kind of forensic evidence, a shoeprint conveys many important human characteristics, and it plays a vital role in forensic investigations. Millions of shoeprints are acquired from crime scenes, and it is a challenging task to retrieve the most similar shoeprints for a query shoeprint. Most shoeprint retrieval methods sort shoeprint images by feature similarities with respect to the query shoeprint; however, the results are not always what the investigator expects because the retrieval algorithm cannot determine what the investigator prefers based on only the content of the query shoeprint. This paper proposes a method to guide the shoeprint retrieval process to approximate what the user wants by applying learned opinion scores. Additionally, this paper improves shoeprint retrieval effectiveness by implementing the following four perspectives: 1) using the opinion scores of multiple examples to guide the results to meet the forensic experts’ expectations; 2) proposing a learning-based method to refine opinion scores, which corrects the labeled opinion scores of multiple examples and their neighbors; 3) using a manifold ranking method to propagate the opinion scores to other dataset shoeprints; and 4) introducing a coefficient matrix to prevent the tendency of the ranking scores to become low values. The experiments show that the cumulative match scores of the proposed method are more than 96.6% in the top 2% of the dataset composed of 10,096 crime scene shoeprints.
Directional asymmetry of the zebrafish epithalamus guides dorsoventral innervation of the midbrain target

The zebrafish epithalamus, consisting of the pineal complex and flanking dorsal habenular nuclei, provides a valuable model for exploring how left-right differences could arise in the vertebrate brain. The parapineal lies to the left of the pineal and the left habenula is larger, has expanded dense neuropil, and distinct patterns of gene expression from the right habenula. Under the influence of Nodal signaling, positioning of the parapineal sets the direction of habenular asymmetry and thereby determines the left-right origin of habenular projections onto the midbrain target, the interpeduncular nucleus (IPN). In zebrafish with parapineal reversal, neurons from the left habenula project to a more limited ventral IPN region where right habenular axons would normally project. Conversely, efferents from the right habenula adopt a more extensive dorsoventral IPN projection pattern typical of left habenular neurons. Three members of the leftover-related KCTD (potassium channel tetramerization domain containing) gene family are expressed differently by the left and right habenula, in patterns that define asymmetric subnuclei. Molecular asymmetry extends to protein levels in habenular efferents, providing additional evidence that left and right axons terminate within different dorsoventral regions of the midbrain target. Laser-mediated ablation of the parapineal disrupts habenular asymmetry and consequently alters the dorsoventral distribution of innervating axons. The results demonstrate that laterality of the dorsal forebrain influences the formation of midbrain connections and their molecular properties.
Associative Transcriptomics Study Dissects the Genetic Architecture of Seed Glucosinolate Content in Brassica napus

Breeding new varieties with low seed glucosinolate (GS) concentrations has long been a prime target in Brassica napus. In this study, a novel association mapping methodology termed ‘associative transcriptomics’ (AT) was applied to a panel of 101 B. napus lines to define genetic regions and also candidate genes controlling total seed GS contents. Over 100,000 informative single-nucleotide polymorphisms (SNPs) and gene expression markers (GEMs) were developed for AT analysis, which led to the identification of 10 SNP and 7 GEM association peaks. Within these peaks, 26 genes were inferred to be involved in GS biosynthesis. A weighted gene co-expression network analysis provided additional 40 candidate genes. The transcript abundance in leaves of two candidate genes, BnaA.GTR2a located on chromosome A2 and BnaC.HAG3b on C9, was correlated with seed GS content, explaining 18.8 and 16.8% of phenotypic variation, respectively. Resequencing of genomic regions revealed six new SNPs in BnaA.GTR2a and four insertions or deletions in BnaC.HAG3b. These deletion polymorphisms were then successfully converted into polymerase chain reaction–based diagnostic markers that can, due to high linkage disequilibrium observed in these regions of the genome, be used for marker-assisted breeding for low seed GS lines.
KAI1 COOH-Terminal Interacting Tetraspanin (KITENIN), a Member of the Tetraspanin Family, Interacts with KAI1, a Tumor Metastasis Suppressor, and Enhances Metastasis of Cancer

We cloned recently an alternatively spliced variant of KAI1 mRNA that lacked exon 7 at the COOH-terminal region and showed differences in metastasis suppression when compared with the wild-type KAI1. These findings indicated that the COOH-terminal region of KAI1 is critical for its metastasis suppressor function. In this study, we isolated a cDNA clone of VANGL1, a member of the tetraspanin protein family, which interacted specifically with the COOH-terminal cytoplasmic domain of KAI1 in the yeast two-hybrid system. We renamed it KAI1 COOH-terminal interacting tetraspanin (KITENIN). We found that KITENIN-overexpressing CT-26 mouse colon cancer cells showed increased tumorigenicity and early hepatic metastasis in vivo, as well as increased invasiveness and adhesion to fibronectin in vitro compared with parental cells. Moreover, increased levels of KITENIN were observed in a human gastric tumor and its metastatic tissues, compared with the normal adjacent mucosa. Our results indicate that KITENIN promotes adhesion and invasion of cancer cells in vitro and in vivo, and suggest that KITENIN participates in the regulation of the tumor formation and metastasis by interacting with KAI1, a metastasis suppressor and antisense KITENIN strategy that can be used to inhibit metastasis in various cancers.
Myocardial Expression Analysis of Osteopontin and Its Splice Variants in Patients Affected by End-Stage Idiopathic or Ischemic Dilated Cardiomyopathy

Osteopontin (OPN) is a phosphoglycoprotein of cardiac extracellular matrix and it is still poorly defined whether its expression changes in failing heart of different origin. The full-length OPN-a and its isoforms (OPN-b, OPN-c) transcriptomic profile were evaluated in myocardium of patients with dilated or ischemic cardiomyopathy (DCM n = 8; LVEF% = 17.5±3; ICM n = 8; LVEF% = 19.5±5.2) and in auricle of valvular patients (VLP n = 5; LVEF%≥50), by Real-time PCR analysis. OPN-a and thrombin mRNA levels resulted significantly higher in DCM compared to ICM patients (DCM:31.3±7.4, ICM:2.7±1.1, p = 0.0002; DCM:19.1±4.9, ICM:5.4±2.2, p = 0.007, respectively). Although both genes’ mRNA levels increased in patients with LVEF<50% (DCM+ICM) with respect to VLP with LVEF>50%, a significant increase in OPN (p = 0.0004) and thrombin (p = 0.001) expression was observed only in DCM. In addition, a correlation between OPN-a and thrombin was found in patients with LVEF<50% (r = 0.6; p = 0.003). The mRNA pattern was confirmed by OPN-a cardiac protein concentration (VLP:1.127±0.26; DCM:1.29±0.22; ICM:1.00±0.077 ng/ml). The OPN splice variants expression were detectable only in ICM (OPN-b: 0.357±0.273; OPN-c: 0.091±0.033) and not in DCM patients. A significant correlation was observed between collagen type I, evaluated by immunohistochemistry analysis, and both OPN-a mRNA expression (r = 0.87, p = 0.002) and OPN protein concentrations (r = 0.77, p = 0.016). Concluding, OPN-a and thrombin mRNA resulted dependent on the origin of heart failure while OPN-b and OPN-c highlighted a different expression for DCM and ICM patients, suggesting their correlation with different clinical-pathophysiological setting.
Bigger Not Better: Unpacking Future Expenses Inflates Spending Predictions

People often underestimate their future personal spending. Across four studies we examined an “unpacking” intervention to reduce this bias. Participants predicted spending for an upcoming week (Study 1), a weekend (Study 2a), a vacation (Study 2b), and for weeks versus self-nominated events (Study 3), and subsequently reported actual spending. In each case, unpacking the details of expected expenses increased spending predictions. In contexts where predictions tended to be too low (Study 1, 3), unpacking eliminated underestimation bias. However, in contexts where predictions were already unbiased, unpacking introduced an overestimation bias (Study 2, 3). Unpacking appears to make predictions bigger, not necessarily better.
MSRE-PCR for analysis of gene-specific DNA methylation

Abnormal DNA methylation is observed in certain promoters of neoplastic cells, although the likelihood of methylation for each individual promoter varies. Simultaneous analysis of many promoters in the same sample can allow use of statistical methods for identification of neoplasia. Here we describe an assay for such analysis, based on digestion of genomic DNA with methylation-sensitive restriction enzyme and multiplexed PCR with gene-specific primers (MSRE-PCR). MSRE-PCR includes extensive digestion of genomic DNA (uncut fragments cannot be identified by PCR), can be applied to dilute samples (<1 pg/μl), requires limited amount of starting material (42 pg or genomic equivalent of seven cells) and can identify methylation in a heterogeneous mix containing <2% of cells with methylated fragments. When applied to 53 promoters of breast cancer cell lines MCF-7, MDA-MB-231 and T47D, MSRE-PCR correctly identified the methylation status of genes analyzed by other techniques. For selected genes results of MSRE-PCR were confirmed by methylation-specific PCR and bisulfite sequencing. The assay can be configured for any number of desired targets in any user-defined set of genes.
An FPGA-based model suitable for evolution and development of spiking neural networks

We propose a digital neuron model suitable for evolving and growing heterogeneous spiking neural networks on FPGAs using a piecewise linear approximation of the Quadratic Integrate and Fire (QIF) model. A network of 161 neurons and 1610 synapses with 4210 times real- time neuron simulation speed was simulated and synthesized for a Virtex-5 chip.
Serial Concatenated Codes for Embedded Bit-Streams Wireless Transmisson with Iterative Decoding

With the rapid growth of wireless communication systems, new architectures of transmitters and receivers have evolved to ensure robust transmission of multimedia data. The need for efficient error-protection schemes is being increasingly challenging for the transmission of embedded bit-streams. In this paper, we present a robust concatenated coding/decoding scheme for progressive JPEG 2000 compressed images for transmission over noisy channels. The image is encoded in different quality layers, that are sorted in a decreasing importance order and applied, according to their importance to the input of the corresponding convolutional encoder. The proposed scheme involves iterative decoding of concatenated codes and a flexible strategy for Unequal Error Protection (UEP) is also guaranteed. Results show a Peak Signal to Noise Ratio (PSNR) gain of 18 dB over a conventional Multiple Turbo Codes (MTC) scheme at low values of Signal-to-Noise Ratio (SNR).
MSnet: Multi-Head Self-Attention Network for Distantly Supervised Relation Extraction

Distant supervision for relation extraction is a task of recognizing semantic relations between entities in a large amount of plain text weakly supervised by external knowledge bases, which can benefit many NLP applications, such as knowledge graph completion and question answering. While it significantly alleviates the expensive cost for data labeling, it severely suffers from noisy labels. In this paper, we propose a Multi-head Self-attention Network (MSNet)-based label denoising method for relation extraction. More specifically, we encode the words, entities and their positions information into contextual embeddings via a multi-head self-attention mechanism, then extract the discriminative sentence features with max pooling operation. MSNet can capture the inherent structure of a sentence and model the relatedness between two words without regard to their distance. Moreover, we adopt a novel label confidence learning method to correct the noisy labels. A latent label is predicted step by step during training as the ground-truth according to a curriculum function of label confidence. This label denoising mechanism gradually incorporates the obtained latent label of easy relation patterns into later latent label prediction of hard patterns, which makes latent label consistent learning more reliable. To verify the effectiveness of our proposed method, in addition to the widely used PCNN-based architecture, we also perform the experiment on BiLSTM model as a comparison. The results demonstrate that our approach can outperform the state-of-the-art systems on the popular evaluation dataset.
Be active and become happy: an ecological momentary assessment of physical activity and mood.

The positive effects of physical activity on mood are well documented in cross-sectional studies. To date there have been only a few studies analyzing within-subject covariance between physical activity and mood in everyday life. This study aims to close this gap using an ambulatory assessment of mood and physical activity. Thirteen participants completed a standardized diary over a 10-week period, resulting in 1,860 measurement points. Valence, energetic arousal, and calmness are the three subscales of mood that were assessed. Participants rated their mood promptly after self-selected activities. A multilevel analysis indicates that the three dimensions of mood were positively affected by episodes of physical activity, such as walking or gardening-valence: t(12) = 5.6, p < . 001; energetic arousal: t(12) = 2.4, p = . 033; calmness: t(12) = 2.8, p = . 015. Moreover, the association is affected by the individual baseline mood level, with the greatest effect seen when mood is depressed.
3L Switching Sequences using Parallel Connected 2L VSIs

This paper analyzes two parallel connected two-level (2L) three-phase voltage source inverters (VSIs) as a single three-level (3L) VSI. Conventionally the parallel connected 2L VSIs are modulated using the continuous and discontinuous 2L pulse-width modulation (PWM) techniques. However, any conventional 3L VSI PWM technique could also be implemented using parallel connected 2L VSIs. Moreover, for implementing a 3L PWM technique or 3L switching sequence using the parallel connected 2L VSIs, there could be many 2L switching sequences. Among the different 2L switching sequences, depending on the system constraints such as individual VSIs line current ripple, circulating current, and switching loss appropriate 2L switching sequence is found out for implementing a 3L switching sequence. Analytical, simulation, and experimental results are also provided.
The Road Not Taken: Neural Correlates of Decision Making in Orbitofrontal Cortex

Empirical research links human orbitofrontal cortex (OFC) to the evaluation of outcomes during decision making and the representation of alternative (better) outcomes after failures. When faced with a difficult decision, rats sometimes pause and turn back-and-forth toward goals, until finally orienting toward the chosen direction. Neural representations of reward in rodent OFC increased immediately following each reorientation, implying a transient representation of the expected outcome following self-initiated decisions. Upon reaching reward locations and finding no reward (having made an error), OFC representations of reward decreased locally indicating a disappointment signal that then switched to represent the unrewarded, non-local, would-have-been rewarded site. These results illustrate that following a decision to act, neural ensembles in OFC represent reward, and upon the realization of an error, represent the reward that could have been.
Classification of left and right hand movement from EEG signals by HOS features

Brain Computer Interface (BCI) shown an importance tool to help paralysed patients to treatmenting their disabilities of communication and mobility by sending commands to the outside using brain signals. In this paper we propose a non linear features able to analyse the EEG signals of hand movements. These features are related to Higher Order Spectra (HOS), especially the normalized version of the third order spectrum namely the bicoherence. The Support Vector Machine (SVM) is used to classify the proposed features into their pertinent classes (left or right hand movement). The SVM classification of our features achieved an average accuracy of 79,14%. We concluded that HOS study could be an accurate tool in the recognition of hand movement.
Deep Batch-Normalized LSTM networks with Auxiliary classifier for Skeleton based Action Recognition

Recently, Recurrent Neural Network (RNN) based approach presents good performance in skeleton based action recognition. Utilizing deep layers of RNN or Long-Short Term Memory (LSTM) has its on weakness when handling long-sequence data because of vanishing or exploding gradients through time. Batch Normalized LSTM (BN-LSTM) is able to give a solution for the problem, with the merit of converging faster in training. In contrast, when deeply layered, BN-LSTM structure shows slow convergence and worse accuracy. In this work, we analyze deep-layered BN-LSTM shows slower convergence in early training phase in training scheme and, finally we propose a deep BN-LSTM structure with auxiliary classifier that is able to converge faster and gives better results at skeleton based action recognition problem. Some experiment are conducted with Penn Action dataset and our own Computer Assembling Video dataset, we verified our proposal shows better results in skeleton based action recognition.
A Reduced Switch Count Hybrid Fifteen-level Inverter for an Open-End Winding Induction Motor (OEWIM) Drive

In this paper, a reduced switch count hybrid fifteen-level inverter topology for an OEWIM (Open-End Winding Induction Motor) drive is proposed. In this topology, one terminal of OEWIM is supported by a FC based hybrid 5-level inverter and other terminal is provided to a symmetrical 3-level inverter. The hybrid 5-level inverter is realized by cascading a 3-level flying capacitor inverter with capacitor fed H-bridge. The symmetrical 3-level inverter is obtained by cascade bond of two 2-level inverters with the same DC-link voltages. The combined effect of five-level hybrid and three-level symmetrical inverters generate fifteen distinct levels in the motor phase-voltage. In this proposed topology, a total of 74,088 space phasor combinations are accessible; hence using switching redundancies, the capacitor voltages can be retained at desired levels. The proposed topology requires only 36 switching devices, as compared to conventional topologies that require 84 switches and fully eliminates the requirement of clamping diodes, which are requisite in NPC inverter. It, also, requires only 6 capacitors unlike to 273 capacitors required in fifteen-level FC inverter and 3 DC power supply sources as compared to fifteen-level CHB inverter that requires 21 DC power supply sources. Simulation exercise is implemented to assess the performance of the considered scheme and outcomes are conferred.
High-performance optical modulators based on stepped quantum wells

High-speed and high-performance optical phase and amplitude modulators are critical components of many photonic systems. Semiconductor-based modulators are very attractive, since they can be monolithically integrated with other semiconductor devices. Unfortunately, the commonly used modulators based on square quantum wells have inherent properties that limit their modulation performance. We present a new class of quantum wells called "stepped quantum wells" (SQW) with extra degrees of freedom that can be used to design high performance optical modulators. We demonstrated SQW phase modulators with nearly one order of magnitude higher efficiency than their counterparts. Also, linearized modulators based on SQW with more than two orders of magnitude higher linearity than the existing semiconductor modulators are presented. Finally, high-performance surface-normal modulators based on SQWs with nearly two times better efficiency and 7 dB higher extinction ratio compared with the conventional devices with rectangular and coupled-quantum well active layers are demonstrated.
Molecular profiling of ETS and non‐ETS aberrations in prostate cancer patients from northern India

Molecular stratification of prostate cancer (PCa) based on genetic aberrations including ETS or RAF gene‐rearrangements, PTEN deletion, and SPINK1 over‐expression show clear prognostic and diagnostic utility. Gene rearrangements involving ETS transcription factors are frequent pathogenetic somatic events observed in PCa. Incidence of ETS rearrangements in Caucasian PCa patients has been reported, however, occurrence in Indian population is largely unknown. The aim of this study was to determine the prevalence of the ETS and RAF kinase gene rearrangements, SPINK1 over‐expression, and PTEN deletion in this cohort.
Diagnosis of DES With Petri Net Models

The diagnosis of discrete event systems is strongly related to events estimation. This paper focuses on faulty behaviors modeled with ordinary Petri nets with some "fault" transitions. Partial but unbiased measurement of the places marking variation is used in order to estimate the firing sequences. The main contribution is to decide which sets of places must be observed for the exact estimation of some given firing sequences. Minimal diagnosers are defined that detect and isolate the firing of fault transitions immediately. Causality relationships and directed paths are also investigated to characterize the influence and dependence areas of the fault transitions. Delayed diagnosers are obtained as a consequence. Note to Practitioners-Structural tools are provided for the analysis of models used in the context of fault detection and isolation for discrete event systems. The systems that are concerned are either manufacturing processes, batch processes, digital devices, or communication protocols with single or multiple failures. Methods are proposed to decide, in a systematic way, if the considered failures can be detected and isolated according to the existing sensors. The obtained results can also be used by designers for sensor selection