Miracle-dz/UniFAR-FTUs huggingface dataset:
Mining of gene modules and identification of key genes in head and neck squamous cell carcinoma based on gene co-expression network analysis

Supplemental Digital Content is available in the text Abstract To explore the gene modules and key genes of head and neck squamous cell carcinoma (HNSCC), a bioinformatics algorithm based on the gene co-expression network analysis was proposed in this study. Firstly, differentially expressed genes (DEGs) were identified and a gene co-expression network (i-GCN) was constructed with Pearson correlation analysis. Then, the gene modules were identified with 5 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Gene Ontology (GO) analysis was used to annotate the biological pathways of the gene modules. Then, the key genes were identified with 2 methods, gene significance (GS) and PageRank algorithm. Moreover, we used the Disgenet database to search the related diseases of the key genes. Lastly, the online software onclnc was used to perform the survival analysis on the key genes and draw survival curves. There were 2600 up-regulated and 1547 down-regulated genes identified in HNSCC. An i-GCN was constructed with Pearson correlation analysis. Then, the i-GCN was divided into 9 gene modules. The result of association analysis showed that, sex was mainly related to mitosis and meiosis processes, event was mainly related to responding to interferons, viruses and T cell differentiation processes, T stage was mainly related to muscle development and contraction, regulation of protein transport activity processes, N stage was mainly related to mitosis and meiosis processes, while M stage was mainly related to responding to interferons and immune response processes. Lastly, 34 key genes were identified, such as CDKN2A, HOXA1, CDC7, PPL, EVPL, PXN, PDGFRB, CALD1, and NUSAP1. Among them, HOXA1, PXN, and NUSAP1 were negatively correlated with the survival prognosis. HOXA1, PXN, and NUSAP1 might play important roles in the progression of HNSCC and severed as potential biomarkers for future diagnosis.
Document Graph for Neural Machine Translation

Previous works have shown that contextual information can improve the performance of neural machine translation (NMT). However, most existing document-level NMT methods failed to leverage contexts beyond a few set of previous sentences. How to make use of the whole document as global contexts is still a challenge. To address this issue, we hypothesize that a document can be represented as a graph that connects relevant contexts regardless of their distances. We employ several types of relations, including adjacency, syntactic dependency, lexical consistency, and coreference, to construct the document graph. Then, we incorporate both source and target graphs into the conventional Transformer architecture with graph convolutional networks. Experiments on various NMT benchmarks, including IWSLT English–French, Chinese-English, WMT English–German and Opensubtitle English–Russian, demonstrate that using document graphs can significantly improve the translation quality. Extensive analysis verifies that the document graph is beneficial for capturing discourse phenomena.
Neural Online Graph Exploration

Can we learn how to explore unknown spaces efficiently? To answer this question, we study the problem of Online Graph Exploration, the online version of the Traveling Salesperson Problem. We reformulate graph exploration as a reinforcement learning problem and apply Direct Future Prediction (Dosovitskiy and Koltun, 2016) to solve it. As the graph is discovered online, the corresponding Markov Decision Process entails a dynamic state space, namely the observable graph and a dynamic action space, namely the nodes forming the graph's frontier. To the best of our knowledge, this is the first attempt to solve online graph exploration in a data-driven way. We conduct experiments on six data sets of procedurally generated graphs and three real city road networks. We demonstrate that our agent can learn strategies superior to many well known graph traversal algorithms, confirming that exploration can be learned.
Sobolev Wasserstein GAN

Wasserstein GANs (WGANs), built upon the Kantorovich-Rubinstein (KR) duality of Wasserstein distance, is one of the most theoretically sound GAN models. However, in practice it does not always outperform other variants of GANs. This is mostly due to the imperfect implementation of the Lipschitz condition required by the KR duality. Extensive work has been done in the community with different implementations of the Lipschitz constraint, which, however, is still hard to satisfy the restriction perfectly in practice. In this paper, we argue that the strong Lipschitz constraint might be unnecessary for optimization. Instead, we take a step back and try to relax the Lipschitz constraint. Theoretically, we first demonstrate a more general dual form of the Wasserstein distance called the Sobolev duality, which relaxes the Lipschitz constraint but still maintains the favorable gradient property of the Wasserstein distance. Moreover, we show that the KR duality is actually a special case of the Sobolev duality. Based on the relaxed duality, we further propose a generalized WGAN training scheme named Sobolev Wasserstein GAN (SWGAN), and empirically demonstrate the improvement of SWGAN over existing methods with extensive experiments.
Mannose-Modified Liposome Co-Delivery of Human Papillomavirus Type 16 E7 Peptide and CpG Oligodeoxynucleotide Adjuvant Enhances Antitumor Activity Against Established Large TC-1 Grafted Tumors in Mice

Background Previously, we demonstrated the therapeutic efficacy of a human papillomavirus (HPV) vaccine, including HPV16 E7 peptide and CpG oligodeoxynucleotides (CpG ODN), against small TC-1 grafted tumors. Here, we developed an HPV16 E7 peptide and CpG ODN vaccine delivered using liposomes modified with DC-targeting mannose, Lip E7/CpG, and determined its anti-tumor effects and influence on systemic immune responses and the tumor microenvironment (TME) in a mouse large TC-1 grafted tumor model. Methods L-alpha-phosphatidyl choline (SPC), cholesterol (CHOL), 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy (polyethylene glycol-2000)] (DSPE-PEG-2000), 1,2-dioleoyl-3-trimethylammonium-propane chloride salt (DOTAP) and Mannose-PEG-DSPE, loaded with HPV16 E7 peptide and CpG ODN, were used to construct the Lip E7/CpG vaccine. The anti-tumor effects and potential mechanism of Lip E7/CpG were assessed by assays of tumor growth inhibition, immune cells, in vivo cytotoxic T lymphocyte (CTL) responses and cytokines, chemokines, CD31, Ki67 and p53 expression in the TME. In addition, toxicity of Lip E7/CpG to major organs was evaluated. Results Lip E7/CpG had a diameter of 122.21±8.37 nm and remained stable at 4°C for 7 days. Co-delivery of HPV16 E7 peptide and CpG ODN by liposomes exerted potent anti-tumor effects in large (tumor volume ≥200mm3) TC-1 grafted tumor-bearing mice with inhibition rates of 80% and 78% relative to the control and Free E7/CpG groups, respectively. Vaccination significantly increased numbers of CD4+ and CD8+ T cells, and IFN-γ-producing cells in spleens and tumors and enhanced HPV-specific CTL responses, while reducing numbers of inhibitory cells including myeloid-derived suppressor cells and macrophages. Expression of cytokines and chemokines was altered and formation of tumor blood vessels was reduced in the Lip E7/CpG group, indicating possible modulation of the immunosuppressive TME to promote anti-tumor responses. Lip E7/CpG did not cause morphological changes in major organs. Conclusion Lip E7/CpG induced anti-tumor effects by enhancing cellular immunity and improving tumor-associated immunosuppression. Mannose-modified liposomes are the promising vaccine delivery strategy for cancer immunotherapy.
6 MHz BAW resonators fabricated using new piezoelectric crystals PrCa4O(BO3)3 and NdCa4O(BO3)3

Thickness shear mode Bulk Acoustic Wave (BAW) resonators with frequency of 6 MHz, were fabricated using monoclinic piezoelectric crystals PrCa4O(BO3)3 (PrCOB) and NdCa4O(BO3)3 (NdCOB). Zero temperature coefficient of frequency (TCF) characteristics were achieved over the temperature range of –140 °C to 200 °C for (YXt)–1.5° cut PrCOB and (YXt)15° cut NdCOB, with the turnover temperature at 20 °C. The electromechanical coupling factor k26 and the piezoelectric coefficient d26 were determined to be 30.2% and 15.8 pC/N for PrCOB, 29.0% and 15.1 pC/N for NdCOB resonators, respectively. The temperature independent frequency behavior, large coupling factor, high piezoelectric coefficient, together with noticeable mechanical quality factors (Q > 2,500), make PrCOB and NdCOB crystals good candidates for sensing applications with expanded temperature usage range. (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Towards end-to-end speech enhancement with a variational U-Net architecture

In this paper, we investigate the viability of a variational U-Net architecture for denoising of single-channel audio data. Deep network speech enhancement systems commonly aim to estimate filter masks, or opt to skip preprocessing steps to directly work on the waveform signal, potentially neglecting relationships across higher dimensional spectro-temporal features. We study the adoption of a probabilistic bottleneck, as well as dilated convolutions, into the classic U-Net architecture. Evaluation of a number of network variants is carried out using signal-to-distortion ratio and perceptual model scores, with audio data including known and unknown noise types as well as reverberation. Our experiments show that the residual (skip) connections in the proposed system are required for successful end-to-end signal enhancement, i.e., without filter mask estimation. Further, they indicate a slight advantage of the variational U-Net architecture over its non-variational version in terms of signal enhancement performance under reverberant conditions. Specifically, PESQ scores show increases of 0.28 and 0.49 in reverberant and non-reverberant scenes, respectively. Anecdotal evidence points to improved suppression of impulsive noise sources with the variational end-to-end U-Net compared to the recurrent mask estimation network baseline.
Effect of Earth-Moon’s gravity on TianQin’s range acceleration noise

TianQin is a proposed space gravitational-wave detection mission using circular high Earth orbits. The geocentric concept has raised questions about the disturbing effect of the nearby gravity field of the Earth-Moon system on the highly-sensitive intersatellite ranging measurements. Here we examine the issue through high precision numerical orbit simulation with detailed gravity-field models. By evaluating range accelerations between distant free-falling test masses, the study shows that the majority of the Earth-Moon's gravity disturbances are not in TianQin's detection frequency band above $10^{-4}$ Hz, and hence present no showstoppers to the mission.
Informal physics programs as communities of practice: How can program structures support university students' identities?.

Many undergraduate and graduate physics students participate in some form of public engagement throughout the course of their studies, often through groups supported by physics departments and universities. These informal teaching and learning programs can offer unique opportunities for physics identity development. Understanding how physics identities can be fostered will allow us to work toward a field that is inclusive of more identities. In this study, we build on previous work to investigate student-facilitator experiences in three informal physics programs using an operationalized Communities of Practice framework. Through our analysis, we identify different structures within these programs that support physics identity development.
Stochastic Gradient Descent with Large Learning Rate

As a simple and efficient optimization method in deep learning, stochastic gradient descent (SGD) has attracted tremendous attention. In the vanishing learning rate regime, SGD is now relatively well understood, and the majority of theoretical approaches to SGD set their assumptions in the continuous-time limit. However, the continuous-time predictions are unlikely to reflect the experimental observations well because the practice often runs in the large learning rate regime, where the training is faster and the generalization of models are often better. In this paper, we propose to study the basic properties of SGD and its variants in the non-vanishing learning rate regime. The focus is on deriving exactly solvable results and relating them to experimental observations. The main contributions of this work are to derive the stable distribution for discrete-time SGD in a quadratic loss function with and without momentum. Examples of applications of the proposed theory considered in this work include the approximation error of variants of SGD, the effect of mini-batch noise, the escape rate from a sharp minimum, and and the stationary distribution of a few second order methods.
Strong laws of large numbers for a growth-fragmentation process with bounded cell sizes

Growth-fragmentation processes model systems of cells that grow continuously over time and then fragment into smaller pieces. Typically, on average, the number of cells in the system exhibits asynchronous exponential growth and, upon compensating for this, the distribution of cell sizes converges to an asymptotic profile. However, the long-term stochastic behaviour of the system is more delicate, and its almost sure asymptotics have been so far largely unexplored. In this article, we study a growth-fragmentation process whose cell sizes are bounded above, and prove the existence of regimes with differing almost sure long-term behaviour.
Inductive Bias and Language Expressivity in Emergent Communication

Referential games and reconstruction games are the most common game types for studying emergent languages. We investigate how the type of the language game affects the emergent language in terms of: i) language compositionality and ii) transfer of an emergent language to a task different from its origin, which we refer to as language expressivity. With empirical experiments on a handcrafted symbolic dataset, we show that languages emerged from different games have different compositionality and further different expressivity.
Fine-grained Angular Contrastive Learning with Coarse Labels

Few-shot learning methods offer pre-training techniques optimized for easier later adaptation of the model to new classes (unseen during training) using one or a few examples. This adaptivity to unseen classes is especially important for many practical applications where the pre-trained label space cannot remain fixed for effective use and the model needs to be "specialized" to support new categories on the fly. One particularly interesting scenario, essentially overlooked by the few-shot literature, is Coarse-to-Fine Few-Shot (C2FS), where the training classes (e.g. animals) are of much ‘coarser granularity’ than the target (test) classes (e.g. breeds). A very practical example of C2FS is when the target classes are sub-classes of the training classes. Intuitively, it is especially challenging as (both regular and few-shot) supervised pre-training tends to learn to ignore intra-class variability which is essential for separating sub-classes. In this paper, we introduce a novel ’Angular normalization’ module that allows to effectively combine supervised and self-supervised contrastive pre-training to approach the proposed C2FS task, demonstrating significant gains in a broad study over multiple baselines and datasets. We hope that this work will help to pave the way for future research on this new, challenging, and very practical topic of C2FS classification.
Competition in Cross-situational Word Learning: A Computational Study

Children learn word meanings by tapping into the commonalities across different situations in which words are used and overcome the high level of uncertainty involved in early word learning experiences. In a set of computational studies, we show that to successfully learn word meanings in the face of uncertainty, a learner needs to use two types of competition: words competing for association to a referent when learning from an observation and referents competing for a word when the word is used.
TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecture

Federated learning has emerged as a new paradigm of collaborative machine learning; however, many prior studies have used global aggregation along a star topology without much consideration of the communication scalability or the diurnal property relied on clients' local time variety. In contrast, ring architecture can resolve the scalability issue and even satisfy the diurnal property by iterating nodes without an aggregation. Nevertheless, such ring-based algorithms can inherently suffer from the high-variance problem. To this end, we propose a novel algorithm called TornadoAggregate that improves both accuracy and scalability by facilitating the ring architecture. In particular, to improve the accuracy, we reformulate the loss minimization into a variance reduction problem and establish three principles to reduce variance: Ring-Aware Grouping, Small Ring, and Ring Chaining. Experimental results show that TornadoAggregate improved the test accuracy by up to 26.7% and achieved near-linear scalability.
A Multi-dimensional Study of Requirements Changes in Agile Software Development Projects

Agile processes are now widely practiced by software engineering (SE) teams, and the agile manifesto claims that agile methods support responding to changes well. However, no study appears to have researched whether this is accurate in reality. Requirements changes (RCs) are inevitable in any software development environment, and we wanted to acquire a holistic picture of how RCs occur and are handled in agile SE teams in practice. We also wanted to know whether responding to changes is the only or a main reason for software teams to use agile in their projects. To do this we conducted a mixed-methods research study which comprised of interviews of 10 agile practitioners from New Zealand and Australia, a literature review, and an in-depth survey with the participation of 40 agile practitioners world-wide. Through this study we identified different types of RCs, their origination including reasons for origination, forms, sources, carriers, and events at which they originate, challenging nature, and finally whether agile helps to respond to changes or not. We also found that agile teams seem to be reluctant to accept RCs, and therefore, they use several mitigation strategies. Additionally, as they accept the RCs, they use a variety of techniques to handle them. Furthermore, we found that agile allowing better response to RCs is only a minor reason for practicing agile. Several more important reasons included being able to deliver the product in a shorter period and increasing team productivity. Practitioners stated this improves the agile team environment and thus are the real motivators for teams to practice agile. Finally, we provide a set of practical recommendations that can be used to better handle RCs effectively in agile software development environments.
Risk of recurrent stroke for Asian stroke patients treated with non-vitamin K antagonist oral anticoagulant and warfarin

Aim: The aim of this study was to establish whether non-vitamin K antagonist oral anticoagulants (NOACs) are superior to warfarin in preventing stroke recurrence for atrial fibrillation (AF) patients with an ischemic or hemorrhagic stroke at the baseline. Methods: From 1 January 2009 to 31 December 2017, stroke patients with AF treated with oral anticoagulants in the National Health Insurance Research Database in Taiwan were enrolled. The study was retrospective cohort design. Outcome measures were ischemic and hemorrhagic stroke recurrence. The Cox proportional hazard model was used to obtain the hazard ratio (HR). Results: In total, 39,840 stroke patients with AF treated with NOAC and 42,583 treated with warfarin were identified. NOACs were superior to warfarin in preventing all recurrent stroke [adjusted HR: 0.67, 95% confidence interval (CI), 0.63–0.71, p < 0.001]. Results for the ischemic stroke population were the same as that for all types for stroke (adjusted HR: 0.66, 95% CI, 0.62–0.70, p < 0.001). For the hemorrhagic stroke population, NOACs were equivalent to warfarin in preventing ischemic stroke (adjusted HR: 1.11, 95% CI, 0.86–0.43, p < 0.001), but NOACs were superior to warfarin in preventing hemorrhagic stroke (adjusted HR: 0.64, 95% CI, 0.55–0.74, p < 0.001). Conclusions: NOACs were generally superior to warfarin in terms of efficacy and safety in previous stroke patients. The robustness of our findings was verified and should add new information to current recommendations for Asian stroke patients in selecting NOACs.
Preventive Effects of Chrysanthemum coronarium L. Extract on Bone Metabolism In Vitro and In Vivo

Osteoporosis is characterized by decreased bone mass and bone microarchitectural failure, leading to an enhanced risk of bone fractures. Chrysanthemum coronarium L. (CC) is a natural plant with powerful antioxidant activity. This study investigated the antiosteoporotic effects of CC extracts in in vitro cell cultures and in vivo bone loss animal models. CC stimulated osteoblast differentiation and mineralized bone formation by osteoblasts by increasing the expression of bone formation markers (alkaline phosphatase, osteoprotegerin, and osteoprotegerin/receptor activator nuclear factor-κB ligand ratio) in the murine preosteoblastic cell line MC3T3-E1. Additionally, CC was found to inhibit osteoclast differentiation by downregulating bone resorption markers (tartrate-resistant acid phosphatase, cathepsin K, dendritic cell-specific transmembrane protein, and calcitonin receptor) in the murine macrophage-like cell line RAW264.7. CC prevented ovariectomy-induced bone loss, preserved trabecular microarchitecture, and improved serum bone turnover markers in an osteoporotic mouse model. These findings suggest that CC extract may be considered as a natural therapeutic or preventive agent for osteoporotic bone loss.
Understanding Public Sentiments, Opinions and Topics about COVID-19 using Twitter

The COVID-19 pandemic has caused widespread devastation throughout the world. In addition to the health and economical impacts, there is an enormous emotional toll associated with the constant stress of daily life with the numerous restrictions in place to combat the pandemic. To better understand the impact of COVID-19, we proposed a framework that utilizes public tweets to derive the sentiments, emotions and discussion topics of the general public in various regions and across multiple timeframes. Using this framework, we study and discuss various research questions relating to COVID-19, namely: (i) how sentiments/emotions change during the pandemic? (ii) how sentiments/emotions change in relation to global events? and (iii) what are the common topics discussed during the pandemic?