Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating Long Financial Report using Conditional Variational Autoencoders with Knowledge Distillation

Automatically generating financial report from a piece of news is quite a challenging task. Apparently, the difficulty of this task lies in the lack of sufficient background knowledge to effectively generate long financial report. To address this issue, this paper proposes the conditional variational autoencoders (CVAE) based approach which distills external knowledge from a corpus of news-report data. Experimental results demonstrate that the proposed approach could achieve the SOTA performance.
TAMPC: A Controller for Escaping Traps in Novel Environments

We propose an approach to online model adaptation and control in the challenging case of hybrid and discontinuous dynamics where actions may lead to difficult-to-escape “trap” states, under a given controller. We first learn dynamics for a system without traps from a randomly collected training set (since we do not know what traps will be encountered online). These “nominal” dynamics allow us to perform tasks in scenarios where the dynamics matches the training data, but when unexpected traps arise in execution, we must find a way to adapt our dynamics and control strategy and continue attempting the task. Our approach, Trap-Aware Model Predictive Control (TAMPC), is a two-level hierarchical control algorithm that reasons about traps and non-nominal dynamics to decide between goal-seeking and recovery policies. An important requirement of our method is the ability to recognize nominal dynamics even when we encounter data that is out-of-distribution w.r. t the training data. We achieve this by learning a representation for dynamics that exploits invariance in the nominal environment, thus allowing better generalization. We evaluate our method on simulated planar pushing and peg-in-hole as well as real robot peg-in-hole problems against adaptive control, reinforcement learning, trap-handling baselines, where traps arise due to unexpected obstacles that we only observe through contact. Our results show that our method outperforms the baselines on difficult tasks, and is comparable to prior trap-handling methods on easier tasks.
Alterations to Maternal Behavior and Brain by Exogenous Estrogen Exposure

Toxicological studies generally view pregnant animals as a conduit through which gestational exposure of offspring to chemicals can be achieved, allowing for the study of developmental toxicity. One class of compounds, endocrine disrupting chemicals (EDCs), has been shown to interfere with hormone signalling in a number of in vivo models, with effects that are more pronounced when exposures occur during development. Yet, evidence has also accumulated that the traditional means of assessing the health effects of EDCs, using test guidelines, are not sufficient to fully capture the effects of these compounds. Our work has focused on the effects of EDCs on more complex, integrated endpoints than the ones typically examined in test guidelines (such as organ weight, histopathology, number of live/dead offspring.) Here, we review evidence that EDCs interfere with the display of maternal behaviours, integrated endpoints affected by multiple hormones and
Flattening the COVID-19 Curve: The “Greek” case in the Global Pandemic

The global crisis caused by the COVID-19 pandemic, in conjunction with the economic consequences and the collapse of health systems, has raised serious concerns in Europe, which is the most affected continent by the pandemic since it recorded 2,388,694 cases and 190,091 deaths (39.6% of the worldwide total), of which 71.7% (136,238) are in the United Kingdom (43,414), Italy (34,708), France (29,778), and Spain (28,338). Unlike other countries, Greece, with about 310 confirmed cases and 18 deaths per million, is one bright exception in the study and analysis of this phenomenon. Focusing on the peculiarities of the disease spreading in Greece, both in epidemiological and in implementation terms, this paper applies an exploratory analysis of COVID-19 temporal spread in Greece and proposes a methodological approach for the modelling and prediction of the disease based on the Regression Splines algorithm and the change rate of the total infections. Also, it proposes a hybrid spline regression and complex network model of social distance measures evaluating and interpreting the spread of the disease. The overall approach contributes to decision making and support of the public health system and to the fight against the pandemic.
Second class currents, axial mass, and nuclear effects in hyperon production

We study the properties of the Cabibbo suppressed quasielastic production of $\Lambda$ and $\Sigma$ hyperons in antineutrino interactions with nuclei, including the effects of modified form factor axial mass, the second class current and SU(3) flavour violations. The hyperon and nucleon are subjected to the nuclear potential and the outgoing hyperon can undergo final state interactions. The hyperon potential has a significant effect on their production through absorption. We predict a significant enhancement of $\Lambda$ production compared with other hyperon production channels through $\Sigma \to \Lambda$ conversions. We produce predictions for several experiments by combining realistic neutrino energy distributions with suitable nuclear targets.
On the Elastic Properties and Fracture Patterns of MoX2 (X = S, Se, Te) Membranes: A Reactive Molecular Dynamics Study

We carried out fully-atomistic reactive molecular dynamics simulations to study the elastic properties and fracture patterns of transition metal dichalcogenide (TMD) MoX2 (X = S, Se, Te) membranes, in their 2H and 1T phases, within the framework of the Stillinger–Weber potential. Results showed that the fracture mechanism of these membranes occurs through a fast crack propagation followed by their abrupt rupture into moieties. As a general trend, the translated arrangement of the chalcogen atoms in the 1T phase contributes to diminishing their structural stability when contrasted with the 2H one. Among the TMDs studied here, 2H-MoSe2 has a higher tensile strength (25.98 GPa).
Large-scale Variation in Reionization History Caused by Baryon–Dark Matter Streaming Velocity

At cosmic recombination, there was supersonic relative motion between baryons and dark matter, which originated from baryonic acoustic oscillations in the early universe. This motion has been considered to have a negligible impact on the late stage of cosmic reionization because the relative velocity quickly decreases. However, recent studies have suggested that the recombination in gas clouds smaller than the local Jeans mass (≲108 M ☉) can affect the reionization history by boosting the number of ultraviolet photons required for ionizing the intergalactic medium. Motivated by this, we performed a series of radiation-hydrodynamic simulations to investigate whether the streaming motion can generate variation in the local reionization history by smoothing out clumpy small-scale structures and lowering the ionizing photon budget. We found that the streaming velocity can add a variation of Δz e ∼ 0.05–0.5 in the end-of-reionization redshift, depending on the level of X-ray preheating and the time evolution of ionizing sources. The variation tends to be larger when the ionizing efficiency of galaxies decreases toward later times. Given the long spatial fluctuation scales of the streaming motion (≳100 Mpc), it can help to explain the Lyα opacity variation observed from quasars and leave large-scale imprints on the ionization field of the intergalactic medium during the reionization. The pre-reionization heating by X-ray sources is another critical factor that can suppress small-scale gas clumping and can diminish the variation in z e introduced by the streaming motion.
DeFuzz: Deep Learning Guided Directed Fuzzing

Fuzzing is one of the most effective technique to identify potential software vulnerabilities. Most of the fuzzers aim to improve the code coverage, and there is lack of directedness (e.g., fuzz the specified path in a software). In this paper, we proposed a deep learning (DL) guided directed fuzzing for software vulnerability detection, named DeFuzz. DeFuzz includes two main schemes: (1) we employ a pre-trained DL prediction model to identify the potentially vulnerable functions and the locations (i.e., vulnerable addresses). Precisely, we employ Bidirectional-LSTM (BiLSTM) to identify attention words, and the vulnerabilities are associated with these attention words in functions. (2) then we employ directly fuzzing to fuzz the potential vulnerabilities by generating inputs that tend to arrive the predicted locations. To evaluate the effectiveness and practical of the proposed DeFuzz technique, we have conducted experiments on real-world data sets. Experimental results show that our DeFuzz can discover coverage more and faster than AFL. Moreover, DeFuzz exposes 43 more bugs than AFL on real-world applications.
Editorial: Deep Learning in Aging Neuroscience

Department Signal Theory, Networking and Communications, University of Granada, Granada, Spain, Department of Communications Engineering, University of Málaga, Málaga, Spain, Department of Computer Science, Centre for Medical Image Computing, University College London, London, United Kingdom, Dementia Research Centre, Institute of Neurology, University College London, London, United Kingdom, German Center for Neurodegenerative Diseases (DZNE),
面向医学文本处理的医学实体标注规范(Medical Entity Annotation Standard for Medical Text Processing)

随着智慧医疗的普及,利用自然语言处理技术识别医学信息的需求日益增长。 目前,针对医学实体而言,医学共享语料库仍处于空白状态,这对医学文本信息处理各项任务的进展造成了巨大阻力。 如何判断不同的医学实体类别? 如何界定不同实体间的涵盖范围? 这些问题导致缺乏类似通用场景的大规模规范标注的医学文本数据。 针对上述问题,该文参考了UMLS中定义的语义类型,提出面向医学文本信息处理的医学实体标注规范,涵盖了疾病、临床表现、医疗程序、医疗设备等9种医学实体,以及基于规范构建医学实体标注语料库。 该文综述了标注规范的描述体系、分类原则、混淆处理、语料标注过程以及医学实体自动标注基线实验等相关问题,希望能为医学实体语料库的构建提供可参考的标注规范,以及为医学实体识别提供语料支持。
层次化结构全局上下文增强的篇章级神经机器翻译(Hierarchical Global Context Augmented Document-level Neural Machine Translation)

如何有效利用篇章上下文信息一直是篇章级神经机器翻译研究领域的一大挑战。 本文提出利用来源于整个篇章的层次化全局上下文提高篇章级神经机器翻译性能。 为了实现该目标,本文模型分别获取当前句内单词与篇章内所有句子及单词之间的依赖关系,结合不同层次的依赖关系以获取含有层次化篇章信息的全局上下文。 最终源语言当前句子中的每个单词都能获取其独有的综合词和句级别依赖关系的上下文。 为了充分利用平行句对语料在训练中的优势本文使用两步训练法,在句子级语料训练模型的基础上使用含有篇章信息的语料进行二次训练以获得捕获全局上下文的能力。 在若干基准语料数据集上的实验表明本文提出的模型与若干强基准模型相比取得了有意义的翻译质量提升。 实验进一步表明,结合层次化篇章信息的上下文比仅使用词级别上下文更具优势。 除此之外,本文尝试通过不同方式将全局上下文与翻译模型结合并观察其对模型性能的影响,并初步探究篇章翻译中全局上下文在篇章中的分布情况。
First report on Bacterial Diversity of Potable Spring water of Indian Himalayan Region

Water quality of a region directly corroborates with the health index of people. People in the Himalayan hills mainly depend upon the spring water for potability. To determine the microbial ecology of the spring waters of Sikkim, the variable region of 16S rRNA has been sequenced using Illumina MiSeq. Phylum wise annotation showed the East and North district are mostly dominated by Proteobacteria (41% and 35.80%), whereas West and South district is dominated by Planctomycetes (38.46%) and Verrucomicrobia (33%). The consistent dominance phyla in the all the four districts were Bacteriodetes (34-24%) which was highest dominancy in North district and lowest in wets district. Genus wise distribution showed the abundance of Brevifolis, Flavobacterium, Verrucomicrobia subdivision3, Emticica, Cytophaga, Prosthecobacter, Planctomycetes, Varivorax, Arcicella, Isosphera, Sedimunibacterium etc. The East district showed highest dominancy of genus Emticicia whereas Planctomycetes in the West district. The North district was mainly dominated by genus Arcicella and Brevifollis in the South district. North on the antonymous showed totally different sets of microbial diversity. North district showed an abundance of Arcicella, Planctomycetes, Schlerensia and Azohydromonas. The heat map produced by Bray Curtis distance method produced three clusters which showed the close relationship between West and East district microbiome that further related to South district. The sample of North district formed out group that showed different community structure from other three districts. The principle component analysis was showed that the east and South district samples are closely related and distantly correlated to the west Sikkim, but the North district showed completely different microbial community. The canonical correspondence analysis showed correlation between bacterial diversity and hydrochemistry and it was found that the bacterial diversity was influenced by the concentration of different metallic ions like sodium, calcium, barium and iron. This is a first report from the Eastern Himalayan region of India and it largely enhances our knowledge about the microbial structure of potable spring water of Eastern Himalayan. This study is useful for Government of India as well as the state government to adopt the different strategic treatment procedures to improve the quality of water that is supplied to the community resides in the Himalayan regions and solely dependent on this untreated spring water.
Use of Field Flexural Demand Data for Reliability-Based Analysis and Design of Concrete Railroad Sleepers

Concrete sleepers are commonly used to constructed ballasted track infrastructure in demanding locations on heavy axle load (HAL) freight railroads with steep grades, sharp curves, and high annual gross tonnage. Center flexural cracking is one of the most common factors limiting the service life of concrete sleepers in North America, and rail seat cracking has also been documented as a performance concern. As such, development and implementation of a structural design method that enables optimization of sleeper design for varied applications and loading environments will reduce initial capital cost and recurring maintenance expense. Field instrumentation has been developed to reliably capture revenue service field flexural demands, facilitating a probabilistic design method for the flexural capacity of concrete sleepers with bending data as the primary input. This paper presents a design process based on structural reliability analysis (SRA) concepts whereby target values for reliability indices (β) for new designs are obtained and compared with existing designs for further design optimization. New (proposed) designs are quite different from current ones. The need for increased sleeper center bending capacity is indicated. Additionally, a reduction in rail seat bending capacity of approximately 40% is justified, reducing the size of the rail seat cross section by approximately the same magnitude. In most cases the proposed designs have fewer prestressing wires and a higher centroid of prestressing steel. In all cases the flexural capacities at the sleeper center and rail seat are better balanced from a structural reliability standpoint. The method proposed is applicable to ballasted track infrastructure constructed with monoblock concrete sleepers.
Nostalgia Proneness and the Collective Self

In two studies, we examined the association between nostalgia proneness (i.e., trait-level nostalgia) and importance of the collective self. In Study 1, we tested and supported the hypothesis that nostalgia proneness is positively correlated with relational collectivism, which entails an emphasis on one’s connections with close others and small social networks. In Study 2, we demonstrated that nostalgia proneness is also positively correlated with group collectivism, which emphasizes one’s membership in more abstract, larger social groups or categories, and was reflected in increased identification with a national ingroup. These findings offer insight into the nature of nostalgia proneness—a consequential and stable personality trait.
Adaptive Surround Modulation of MT Neurons: A Computational Model

The classical receptive field (CRF) of a spiking visual neuron is defined as the region in the visual field that can generate spikes when stimulated by a visual stimulus. Many visual neurons also have an extra-classical receptive field (ECRF) that surrounds the CRF. The presence of a stimulus in the ECRF does not generate spikes but rather modulates the response to a stimulus in the neuron's CRF. Neurons in the primate Middle Temporal (MT) area, which is a motion specialist region, can have directionally antagonistic or facilitatory surrounds. The surround's effect switches between directionally antagonistic or facilitatory based on the characteristics of the stimulus, with antagonistic effects when there are directional discontinuities but facilitatory effects when there is directional coherence. Here, we present a computational model of neurons in area MT that replicates this observation and uses computational building blocks that correlate with observed cell types in the visual pathways to explain the mechanism of this modulatory effect. The model shows that the categorization of MT neurons based on the effect of their surround depends on the input stimulus rather than being a property of the neurons. Also, in agreement with neurophysiological findings, the ECRFs of the modeled MT neurons alter their center-surround interactions depending on image contrast.
Molecular and Clinical Characterization of a Novel Prognostic and Immunologic Biomarker FAM111A in Diffuse Lower-Grade Glioma

Background Family with sequence similarity 111 member A (FAM111A), as a replication factor required for proliferating cell nuclear antigen (PCNA) loading, has been demonstrated a possible association with carcinogenesis. However, the role of FAM111A in lower-grade glioma (LGG) remains unclear. We aim at investigating the expression and function of FAM111A in lower-grade glioma at the molecular and clinical levels. Methods In total, 711 lower-grade glioma samples were analyzed in our research, including 182 RNA-seq data from the Chinese Glioma Genome Atlas (CGGA) dataset and 529 RNA-seq data from The cancer Genome Atlas (TCGA) dataset. R language and the GraphPad software were used for the majority of statistical analysis and graphical work. Results FAM111A expression was overexpressed in WHO grade III and IDH-wildtype lower-grade glioma. FAM111A was significantly downregulated in the IDHmut-Codel molecular subtype. Univariate and multivariate Cox analysis demonstrated that FAM111A was an independent prognostic factor in LGG patients. Functional characterization of FAM111A revealed that it was associated with inflammatory response and immune response to tumor cells. FAM111A could also act as an indicator of the stromal and immune population, especially for monocytic lineage, myeloid dendritic cells and fibroblasts. It was positively correlated with macrophages, especially the M2 macrophage cells. Furthermore, FAM111A revealed predictive value for the immune subtypes and immune checkpoint blockade therapy. Conclusion FAM111A expression was closely related to the malignant phenotype, molecular pathology and immune response of lower-grade glioma. It might be a promising target for LGG immunotherapeutic strategies.
HFNet: A CNN Architecture Co-designed for Neuromorphic Hardware With a Crossbar Array of Synapses

The hardware-software co-optimization of neural network architectures is a field of research that emerged with the advent of commercial neuromorphic chips, such as the IBM TrueNorth and Intel Loihi. Development of simulation and automated mapping software tools in tandem with the design of neuromorphic hardware, whilst taking into consideration the hardware constraints, will play an increasingly significant role in deployment of system-level applications. This paper illustrates the importance and benefits of co-design of convolutional neural networks (CNN) that are to be mapped onto neuromorphic hardware with a crossbar array of synapses. Toward this end, we first study which convolution techniques are more hardware friendly and propose different mapping techniques for different convolutions. We show that, for a seven-layered CNN, our proposed mapping technique can reduce the number of cores used by 4.9–13.8 times for crossbar sizes ranging from 128 × 256 to 1,024 × 1,024, and this can be compared to the toeplitz method of mapping. We next develop an iterative co-design process for the systematic design of more hardware-friendly CNNs whilst considering hardware constraints, such as core sizes. A python wrapper, developed for the mapping process, is also useful for validating hardware design and studies on traffic volume and energy consumption. Finally, a new neural network dubbed HFNet is proposed using the above co-design process; it achieves a classification accuracy of 71.3% on the IMAGENET dataset (comparable to the VGG-16) but uses 11 times less cores for neuromorphic hardware with core size of 1,024 × 1,024. We also modified the HFNet to fit onto different core sizes and report on the corresponding classification accuracies. Various aspects of the paper are patent pending.
HYGRIP: Full-Stack Characterization of Neurobehavioral Signals (fNIRS, EEG, EMG, Force, and Breathing) During a Bimanual Grip Force Control Task

1 Brain & Behavior Lab, Department of Computing and Department of Bioengineering, Imperial College London, London, United Kingdom, 2 EPSRC Centre for High Performance Embedded and Distributed Systems, Imperial College London, London, United Kingdom, UKRI Centre in Artificial Intelligence for Healthcare, Imperial College London, London, United Kingdom, Data Science Institute, London, United Kingdom
Field-Adapted Full Genome Sequencing of Peste-Des-Petits-Ruminants Virus Using Nanopore Sequencing

Peste-des-petits-ruminants virus (PPRV) is currently the focus of a control and eradication program. Full genome sequencing has the opportunity to become a powerful tool in the eradication program by improving molecular epidemiology and the study of viral evolution. PPRV is prevalent in many resource-constrained areas, with long distances to laboratory facilities, which can lack the correct equipment for high-throughput sequencing. Here we present a protocol for near full or full genome sequencing of PPRV. The use of a portable miniPCR and MinION brings the laboratory to the field and in addition makes the production of a full genome possible within 24 h of sampling. The protocol has been successfully used on virus isolates from cell cultures and field isolates from tissue samples of naturally infected goats.
Love and Romantic Relationships in the Voices of Patients Who Experience Psychosis: An Interpretive Phenomenological Analysis

Love is a universal experience that most people desire. A serious, long-term, and stigmatized illness makes entering and maintaining close relationships difficult, however. Ten persons, who have been diagnosed with schizophrenia, and lived with their illness for between years and decades of their lifetimes, shared their stories. They reported how the illness has influenced their emotional experiences regarding love and their intimate relationship experiences. We present here a qualitative interpretative phenomenological analysis (IPA) of their narratives. This analysis has been done with an explicit intention to give voice to the patient’s perspective. The results highlight how illness adjustment and hospitalizations have an alienating effect on relationships through stigmatization and self-stigmatization; how illness creates psychological obstacles to love, such as diminished trust toward oneself and others; and how long-term patients experience practical difficulties in creating and sustaining relationships, such as poverty. Moreover, we show how patients experience changes in sexuality and the risks involved in it and discuss possible coping strategies from their perspectives.