Miracle-dz/UniFAR-FTUs huggingface dataset:
Discrete Systems in Thermal Engineering -- A Glance from Non-Equilibrium Thermodynamics

Non-equilibrium processes in Schottky systems generate by projection onto the equilibrium subspace and by contact temperature relaxation accompanying processes which serve as approximations of different accuracy establishing three thermodynamical stages. That of Thermal Engineering is characterized by the replacement of the contact temperature with the thermostatic one. The compatibility of the accompanying processes with the non-equilibrium ones is discussed according to the embedding theorem. An efficiency of generalized cyclic processes beyond the Carnot process is achieved.
Sub-sampling for Efficient Non-Parametric Bandit Exploration

In this paper we propose the first multi-armed bandit algorithm based on re-sampling that achieves asymptotically optimal regret simultaneously for different families of arms (namely Bernoulli, Gaussian and Poisson distributions). Unlike Thompson Sampling which requires to specify a different prior to be optimal in each case, our proposal RB-SDA does not need any distribution-dependent tuning. RB-SDA belongs to the family of Sub-sampling Duelling Algorithms (SDA) which combines the sub-sampling idea first used by the BESA [1] and SSMC [2] algorithms with different sub-sampling schemes. In particular, RB-SDA uses Random Block sampling. We perform an experimental study assessing the flexibility and robustness of this promising novel approach for exploration in bandit models.
Detector Algorithms of Bounding Box and Segmentation Mask of a Mask R-CNN Model

Detection performances on bounding box and segmentation mask outputs of Mask R-CNN models are evaluated. There are significant differences in detection performances of bounding boxes and segmentation masks, where the former is constantly superior to the latter. Harmonic values of precisions and recalls of linear cracks, joints, fillings, and shadows are significantly lower in segmentation masks than bounding boxes. Other classes showed similar harmonic values. Discussions are made on different performances of detection metrics of bounding boxes and segmentation masks focusing on detection algorithms of both detectors.
End-to-End Learning and Intervention in Games

In a social system, the self-interest of agents can be detrimental to the collective good, sometimes leading to social dilemmas. To resolve such a conflict, a central designer may intervene by either redesigning the system or incentivizing the agents to change their behaviors. To be effective, the designer must anticipate how the agents react to the intervention, which is dictated by their often unknown payoff functions. Therefore, learning about the agents is a prerequisite for intervention. In this paper, we provide a unified framework for learning and intervention in games. We cast the equilibria of games as individual layers and integrate them into an end-to-end optimization framework. To enable the backward propagation through the equilibria of games, we propose two approaches, respectively based on explicit and implicit differentiation. Specifically, we cast the equilibria as the solutions to variational inequalities (VIs). The explicit approach unrolls the projection method for solving VIs, while the implicit approach exploits the sensitivity of the solutions to VIs. At the core of both approaches is the differentiation through a projection operator. Moreover, we establish the correctness of both approaches and identify the conditions under which one approach is more desirable than the other. The analytical results are validated using several real-world problems.
Describing a Clinical Group Coding Method for Identifying Competencies in an Allied Health Single Session

Introduction Competencies that integrate research findings and practice expertise are necessary to maintain comprehensive evidence-based practice for allied health professions, such as social work. The context of modern multidisciplinary healthcare, especially in acute or emergency settings, means that an individual clinician may only have a single session with a patient. Maximizing the benefit of single sessions requires advanced competence that extends beyond diagnostics and biomedical treatments to the impact of social systems on health outcomes; multi-level advocacy for reduction of existing health disparities and equity in access to health and mental health services; and “working knowledge” of non-pharmacological treatments. Methods This study employed a practice-based research methodology whereby health social workers group coded 32 simulation videos, drawn from an advanced social work practice course, to develop a practice-based competency framework that incorporates these advanced skills. Constructivist grounded theory was employed through a cyclical coding process of viewing video data, identifying and discussing skills and competencies, and summarizing/synthesizing the discussions for critical reflection. Results The resulting Clinician Group Coding Method utilized systematic and collaborative group coding of practice simulation videos by three clinicians and two researchers to identify relevant competencies for a single session. Emphasis was placed on the progressive phases of single-session patient interactions (eg, joining, working, ending), a practice format that frequently occurs in social work and other allied health professions. These phases include themes of preparing, agenda setting and refining, addressing context, providing education, planning the next steps, and encouraging success. Discussion The group coding process allowed for immediate discussions and clarifications, supporting the clinicians to synthesize their experiences toward shared understandings of “best practices” in single-session healthcare contexts. This approach facilitated the understanding of critical actions that allied health clinicians could undertake to improve single-session interactions. This practice-based competency framework may have significant utility for multidisciplinary healthcare education and practice.
Acoustic Echo Cancellation with the Dual-Signal Transformation LSTM Network

This paper applies the dual-signal transformation LSTM network (DTLN) to the task of real-time acoustic echo cancellation (AEC). The DTLN combines a short-time Fourier transform and a learned feature representation in a stacked network approach, which enables robust information processing in the time-frequency and in the time domain, which also includes phase information. The model is only trained on 60 h of real and synthetic echo scenarios. The training setup includes multi-lingual speech, data augmentation, additional noise and reverberation to create a model that should generalize well to a large variety of real-world conditions. The DTLN approach produces state-of-the-art performance on clean and noisy echo conditions reducing acoustic echo and additional noise robustly. The method outperforms the AEC-Challenge baseline by 0.30 in terms of Mean Opinion Score (MOS).
One-class learning towards generalized voice spoofing detection

Human voices can be used to authenticate the identity of the speaker, but the automatic speaker verification (ASV) systems are vulnerable to voice spoofing attacks, such as impersonation, replay, text-to-speech, and voice conversion. Recently, researchers developed anti-spoofing techniques to improve the reliability of ASV systems against spoofing attacks. However, most methods encounter difficulties in detecting unknown attacks in practical use, which often have different statistical distributions from known attacks. In this work, we propose an anti-spoofing system to detect unknown logical access attacks (i.e., synthetic speech) using one-class learning. The key idea is to compact the genuine speech representation and inject an angular margin to separate the spoofing attacks in the embedding space. Our system achieves an equal error rate of 2.19% on the evaluation set of ASVspoof 2019 Challenge, outperforming all existing single systems.
Phase Aware Speech Enhancement using Realisation of Complex-valued LSTM

Most of the deep learning based speech enhancement (SE) methods rely on estimating the magnitude spectrum of the clean speech signal from the observed noisy speech signal, either by magnitude spectral masking or regression. These methods reuse the noisy phase while synthesizing the time-domain waveform from the estimated magnitude spectrum. However, there have been recent works highlighting the importance of phase in SE. There was an attempt to estimate the complex ratio mask taking phase into account using complex-valued feed-forward neural network (FFNN). But FFNNs cannot capture the sequential information essential for phase estimation. In this work, we propose a realisation of complex-valued long short-term memory (RCLSTM) network to estimate the complex ratio mask (CRM) using sequential information along time. The proposed RCLSTM is designed to process the complex-valued sequences using complex arithmetic, and hence it preserves the dependencies between the real and imaginary parts of CRM and thereby the phase. The proposed method is evaluated on the noisy speech mixtures formed from the Voice-Bank corpus and DEMAND database. When compared to real value based masking methods, the proposed RCLSTM improves over them in several objective measures including perceptual evaluation of speech quality (PESQ), in which it improves by over 4.3%
Knockdown of circ-TTBK2 Inhibits Glioma Progression by Regulating miR-1283 and CHD1

Background Dysregulated circular RNAs (circRNAs) are involved in the development of glioma. This paper aims to analyze the role and mechanism of circRNA tau tubulin kinase 2 (circ-TTBK2) in glioma progression. Methods The glioma samples and normal brain tissues were collected. The levels of circ-TTBK2, microRNA-1283 (miR-1283) and chromodomain helicase DNA-binding protein 1 (CHD1) were examined via quantitative reverse transcription polymerase chain reaction or Western blot. Cell proliferation, migration, invasion and glycolysis were determined via 3-(4, 5-dimethyl-2-thiazolyl)-2, 5-diphenyl-2-H-tetrazolium bromide, transwell assay, Western blot, glucose and lactate assay kits. The target relationship was analyzed via dual-luciferase reporter assay. The xenograft model was established using U251 cells. Results circ-TTBK2 expression was increased in glioma tissues and cells. circ-TTBK2 knockdown suppressed glioma cell proliferation, migration, invasion and glycolysis. circ-TTBK2 was a sponge for miR-1283, and knockdown of miR-1283 reversed the effect of circ-TTBK2 silence on glioma progression. CHD1 was targeted via miR-1283, and miR-1283 repressed glioma cell proliferation, migration, invasion and glycolysis via decreasing CHD1. Knockdown of circ-TTBK2-reduced CHD1 expression by mediating miR-1283. Silence of circ-TTBK2 reduced xenograft tumor growth. Conclusion Down-regulation of circ-TTBK2 suppressed glioma development by regulating miR-1283 and CHD1, providing a new mechanism for understanding glioma pathogenesis.
Overexpression of Cancer Upregulated Gene 2 (CUG2) Decreases Spry2 Through c-Cbl, Leading to Activation of EGFR and β-Catenin Signaling

Purpose The mechanism by which cancer upregulated gene 2 (CUG2) overexpression induces cancer stem cell-like phenotypes is not fully understood. Because the increased activity and expression of epidermal growth factor receptor (EGFR) kinase have been reported in A549 cancer cells overexpressing CUG2 (A549-CUG2) compared with control cells (A549-Vec), the Sprouty2 (Spry2) protein has gained attention as the downstream molecule of EGFR signaling. Therefore, we aim to identify the role of Spry2 in CUG2-overexpressing lung cancer cells. Materials and Methods Spry2 expression levels were examined in A549-CUG2 and A549-Vec cells by Western blotting and qRT-PCR. Cell migration, invasion, and sphere formation were examined after Spry2 suppression and overexpression. EGFR-Stat1 and Akt-ERK protein phosphorylation levels were detected via immunoblotting. NEK2 kinase and β-catenin reporter assay were performed for downstream of Spry2 signaling. Results Although A549-CUG2 cells showed lower levels of the Spry2 protein than A549-Vec cells, no difference in levels of Spry2 transcript was observed between both cells via qRT-PCR. Furthermore, MG132 treatment enhanced the protein levels and ubiquitination of Spry2, suggesting that Spry2 protein expression can be regulated via the ubiquitin-proteasome pathway. The enforced expression of c-Cbl, known as the binding partner of Spry2, decreased the Spry2 protein levels, whereas its knockdown oppositely increased them. Epithelial–mesenchymal transition (EMT) and sphere formation were increased in A549-Vec cells during Spry2 siRNA treatment, confirming the role of Spry2 in CUG2-induced oncogenesis. Furthermore, EMT and sphere formation were determined by the Spry2 protein levels through the regulation of EGFR-Stat1 and β-catenin-NEK2-Yap1 signaling pathways. Conclusion CUG2 reduces Spry2 protein levels, the negative signaling molecule of cell proliferation, via c-Cbl, possibly activating the EGFR and β-catenin signaling pathways and, in turn, contributing to the induction of cancer stem cell-like phenotypes.
Characterization of RNF43, a novel negative regulator of the Wnt signaling pathway

Colorectal cancer is one of the most commonly diagnosed cancers. The canonical Wnt signaling pathway plays a key role in carcinogenesis, and deregulation of the canonical Wnt signaling pathway is tightly associated with tumor development in the gut. In this study, the specific contribution of the putative tumor suppressor RNF43 to tumorbiological aspects, as well as its role as a negative regulator of the Wnt signaling pathway were characterized. This novel inhibitory function may pave the way for the development of new therapeutic approaches.
Knockdown of lncRNA PCAT1 Enhances Radiosensitivity of Cervical Cancer by Regulating miR-128/GOLM1 Axis

Purpose Cervical cancer (CC) is the fourth most common cancer with high death rate in females. The study aims to detect the mechanism of long non-coding RNA (LncRNA) PCAT1 on radiosensitivity of CC. Methods The expression of PCAT1, miR-128 and GOLM1 in CC tissues and cells was measured by qRT-PCR. Different doses of X-ray were used for radiation treatment of CC cells and 6 Gy was chosen to perform the following experiments. The proliferation, migration and invasion of CC cells were measured by MTT assay, wound healing assay and transwell assay, respectively. The target relationships among PCAT1, miR-128 and GOLM1 were predicted by StarBase and TargetScan and verified by luciferase reporter assay. The protein level of GOLM1 was determined by Western blot. The xenograft tumor model was constructed in nude mice to verify the effect of PCAT1 on radiosensitivity of CC in vivo. Results The PCAT1 expression was upregulated in CC tissues and cells. PCAT1 silencing enhances radiosensitivity of CC cells on proliferation, migration and invasion. MiR-128 was the target of PCAT1 and was negatively regulated by PCAT1. Upregulation of miR-128 enhances radiosensitivity of CC cells on proliferation, migration and invasion. GOLM1 was a target of miR-128 and was negatively regulated by miR-128. Upregulation of GOLM1 and downregulation of miR-128 both reversed the enhanced effect of PCAT1 knockdown on radiosensitivity of CC cells, which partly promoted the proliferation, migration and invasion of CC cells. Conclusion Silencing of PCAT1 enhanced radiosensitivity of CC via targeting miR-128/GOLM1, which provided a new idea for treating CC.
Identification and characterization of a novel component involved in water deficit stress and abscisic acid signaling in Arabidopsis thaliana

Plants respond to water deficit by primarily activating the signaling pathway of the hormone abscisic acid (ABA). In this work, the Arabidopsis gene AHR11 was identified as a novel component of ABA signaling. AHR11 is a negative regulator of ABA-activated transcription and a modulator of ABA distribution during water deficit. Considering the protein interacting partners of AHR11 found in a yeast two-hybrid system, a hypothesis on AHR11 action is presented.
Case Report: Allergic Reactivity to Mahaleb (Prunus mahaleb) Spice in a Subject With Almond and Other Tree Nut Allergies

Background Mahaleb is an aromatic spice prepared from the fruit stone of the St. Lucie Cherry that is used as a flavoring agent in traditional Turkish and Middle Eastern baking. Immunodiagnostic kits for almond, which are based on polyclonal almond-specific IgG antibodies, have been shown to demonstrate considerable cross-reactivity with mahaleb as was incidentally discovered during a cluster of allergen-related food recalls in 2015. Objective Though acute allergy to almond is somewhat common, allergies to mahaleb have not been previously documented. However, based on antigenic similarity observed with almond-specific IgG, it is predicted that mahaleb nut proteins would exhibit some level of cross-reactivity with almond-specific IgE and may therefore potentiate acute allergic symptoms in individuals with food allergy to almond. Case Presentation: Herein, we report on a 40-year old Caucasian female with longitudinal history of multiple tree nut allergies including allergy to almond, presenting with moderate pruritus and oropharyngeal swelling shortly following ingestion of mahaleb seed kernels. Methods and Results Skin-prick testing using extracts compounded from pistachio, almond, and mahaleb revealed positive wheals measuring 8, 3, and 7 mm respectfully. Indirect enzyme-linked immunosorbent assay (ELISA) using plate-bound antigens prepared from pistachio, almond, and mahaleb revealed IgG positive responses to all three targets. ELISA and Western blot analysis performed using goat anti-almond polyclonal IgG demonstrated significant cross-reactivity between almond and mahaleb, but not to pistachio. Conclusion This is the first documented case of acute allergy to mahaleb, co-occurring in the context of plural tree nut allergies, providing novel evidence that mahaleb may pose a risk to nut-allergic individuals and indicating a need for awareness of spice contamination with nut and mahaleb residues.
Are Multi-language Design Smells Prevalent? An Empirical Study

Nowadays, modern applications are developed using components written in different programming languages. These systems introduce several advantages. However, as the number of languages increases, so does the challenges related to the development and maintenance of these systems. In such situations, developers may introduce design smells (i.e., anti-patterns and code smells) which are symptoms of poor design and implementation choices. Design smells are defined as poor design and coding choices that can negatively impact the quality of a software program despite satisfying functional requirements. Studies on mono-language systems suggest that the presence of design smells affects code comprehension, thus making systems harder to maintain. However, these studies target only mono-language systems and do not consider the interaction between different programming languages. In this paper, we present an approach to detect multi-language design smells in the context of JNI systems. We then investigate the prevalence of those design smells. Specifically, we detect 15 design smells in 98 releases of nine open-source JNI projects. Our results show that the design smells are prevalent in the selected projects and persist throughout the releases of the systems. We observe that in the analyzed systems, 33.95% of the files involving communications between Java and C/C++ contains occurrences of multi-language design smells. Some kinds of smells are more prevalent than others, e.g., Unused Parameters, Too Much Scattering, Unused Method Declaration. Consequently, we formulate recommendations for researchers interested in improving the quality of multi-language systems.
Physics-Based Deep Learning for Fiber-Optic Communication Systems

We propose a new machine-learning approach for fiber-optic communication systems whose signal propagation is governed by the nonlinear Schrödinger equation (NLSE). Our main observation is that the popular split-step method (SSM) for numerically solving the NLSE has essentially the same functional form as a deep multi-layer neural network; in both cases, one alternates linear steps and pointwise nonlinearities. We exploit this connection by parameterizing the SSM and viewing the linear steps as general linear functions, similar to the weight matrices in a neural network. The resulting physics-based machine-learning model has several advantages over “black-box” function approximators. For example, it allows us to examine and interpret the learned solutions in order to understand why they perform well. As an application, low-complexity nonlinear equalization is considered, where the task is to efficiently invert the NLSE. This is commonly referred to as digital backpropagation (DBP). Rather than employing neural networks, the proposed algorithm, dubbed learned DBP (LDBP), uses the physics-based model with trainable filters in each step and its complexity is reduced by progressively pruning filter taps during gradient descent. Our main finding is that the filters can be pruned to remarkably short lengths—as few as 3 taps/step—without sacrificing performance. As a result, the complexity can be reduced by orders of magnitude in comparison to prior work. By inspecting the filter responses, an additional theoretical justification for the learned parameter configurations is provided. Our work illustrates that combining data-driven optimization with existing domain knowledge can generate new insights into old communications problems.
Studies on the Plasticity of Dormancy and on Aging in Switchgrass Seeds

ii DEDICATION . iii ACKNOWLEDGMENTS . iv LIST OF TABLES . vii LIST OF FIGURES . viii LIST OF APPENDIXES . ix CHAPTER ONE: INTRODUCTION AND LITERATURE REVIEW Justification . 1 Objectives . 1 Literature review . 2 Reference . 6 CHAPTER TWO: SWITCHGRASS SEED DORMANCY AND AGING AS INFLUENCED BY TEMPERATURE DURING STORAGE IN PAPER BAGS Abstract . 10 Introduction . 10 Materials and methods . 12 Results and discussion . 14 Summary and conclusions . 21 References . 22 Tables and figures . 23 CHAPTER THREE: AFTER-RIPENING AND AGING OF SWITCHGRASS SEEDS AS INFLUENCED BY SEED MOISTURE CONTENT AND STORAGE TEMPERATURE Abstract . 3