Miracle-dz/UniFAR-FTUs huggingface dataset:
Melanoma Prognosis - Accuracy of the American Joint Committee on Cancer Staging Manual Eighth Edition.

BACKGROUND The American Joint Committee on Cancer (AJCC) maintains that the eighth edition of its Staging Manual (AJCC8) has improved accuracy compared to the seventh (AJCC7). However, there are concerns that implementation may disrupt analysis of active clinical trials for stage III patients. We used an independent cohort of melanoma patients to test the extent to which AJCC8 has improved prognostic accuracy compared to AJCC7. METHODS We analyzed a cohort of 1,315 prospectively enrolled patients. We assessed primary tumor and nodal classification of stage I-III patients using AJCC7 and AJCC8 to assign disease stages at diagnosis. We compared recurrence-free (RFS) and overall survival (OS) using Kaplan-Meier curves and log-rank tests. We then compared concordance indices of discriminatory prognostic ability and area under the curve (AUC) of 5-year survival to predict RFS/OS. All statistical tests were two-sided. RESULTS Stage IIC continued to have worse outcomes than those for stage IIIA patients, with 5-year RFS of 26.5% (95%CI=12.8-55.1%) vs. 56.0% (95%CI=37.0-84.7%) by AJCC8 (P = 0.002). For stage I, removing mitotic index as T classification factor decreased its prognostic value, although not statistically significantly (RFS C-index=0.63 [95%CI=0.56-0.69] to 0.56 [95%CI=0.49-0.63], P = 0.07; OS C-index=0.48 [95%CI=0.38-0.58] to 0.48 [95%CI=0.41-0.56], P = 0.90). For stage II, prognostication remained constant (RFS C-index=0.65 [95%CI=0.57-0.72]; OS C-index=0.61 [95%CI=0.50-0.72]), and. For stage III, AJCC8 yielded statistically significantly enhanced prognostication for RFS (C-index=0.65 [95%CI=0.60-0.70] to 0.70 [95%CI=0.66-0.75], P = 0.01). CONCLUSIONS Compared with AJCC7, we demonstrate that AJCC8 enables more accurate prognosis for patients with stage III melanoma. Restaging a large cohort of patients can enhance the analysis of active clinical trials.
Smartphone-based Mental State Estimation: A Survey from a Machine Learning Perspective

: Monitoring mental health has received considerable attention as a countermeasure against the increasing occurrence of mental illness worldwide. However, current monitoring services incur costs because users are required to attach wearable devices or answer questions. To reduce such costs, many studies have used smartphone-based passive sensing technology to capture a user’s mental state. This paper reviews those studies from the perspective of machine learning and statistical analysis. Forty-four studies published since 2011 have been reviewed and summarized from three perspectives: designed features, machine learning algorithm, and evaluation method. The features considered include location and mobility, activity, speech, sleep, phone usage, and context features. Tasks are classiﬁed as correlation analysis, regression tasks, and classiﬁcation tasks. The machine learning algorithm used for each task is summarized. Evaluation metrics and cross validation methods are also summarized. For those who are not necessarily machine learning experts, we aim to provide information on typical machine learning framework for smartphone-based mental state estimation. For experts in the ﬁeld, we hope this review will be a helpful tool to check for potential omissions.
DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks

Learning node representations in graphs is important for many applications such as link prediction, node classification, and community detection. Existing graph representation learning methods primarily target static graphs while many real-world graphs evolve over time. Complex time-varying graph structures make it challenging to learn informative node representations over time. We present Dynamic Self-Attention Network (DySAT), a novel neural architecture that learns node representations to capture dynamic graph structural evolution. Specifically, DySAT computes node representations through joint self-attention along the two dimensions of structural neighborhood and temporal dynamics. Compared with state-of-the-art recurrent methods modeling graph evolution, dynamic self-attention is efficient, while achieving consistently superior performance. We conduct link prediction experiments on two graph types: communication networks and bipartite rating networks. Experimental results demonstrate significant performance gains for DySAT over several state-of-the-art graph embedding baselines, in both single and multi-step link prediction tasks. Furthermore, our ablation study validates the effectiveness of jointly modeling structural and temporal self-attention.
Influence of rapid eye movement sleep on all-cause mortality: a community-based cohort study

Introduction: Although the proportion and duration of rapid eye movement (REM) sleep are correlated with neurological and cardiovascular diseases, whether REM sleep is associated with all-cause mortality in community-based populations remains unknown. Methods: A prospective study was performed within the Sleep Heart Health Study (SHHS, Registration NO. NCT00005275). Total sleep time, sleep efficiency, and REM sleep were measured using polysomnography. Cox proportional hazards regression models were used to estimate the association of the REM sleep with all-cause mortality. Results: Over a mean follow-up period of 11.0 ± 3.1 y, 1234 individuals (21.9%) died. In the entire population, reduced REM sleep was significantly associated with increasing all-cause mortality. After adjustment for age, sex, race, body mass index, smoking status, total cholesterol, triglycerides, high-density lipoprotein, history of diabetes and hypertension, and the apnea–hypopnea index, the duration and proportion of REM sleep were found to be significantly associated with all-cause mortality when the lowest and the highest REM quartile groups were compared (hazard ratio, 95% confidence interval: 1.727, 1.434-2.079; 1.545, 1.298-1.839; respectively). Conclusion: The proportion and duration of REM sleep are negatively associated with all-cause mortality. This finding emphasizes the importance of personalized sleep management in community-based populations.
A Modular, Smart, and Wearable System for High Density sEMG Detection

Objective: The use of linear or bi-dimensional electrode arrays for surface EMG detection (HD-sEMG) is gaining attention as it increases the amount and reliability of information extracted from the surface EMG. However, the complexity of the setup and the encumbrance of HD-sEMG hardware currently limits its use in dynamic conditions. The aim of this paper was to develop a miniaturized, wireless, and modular HD-sEMG acquisition system for applications requiring high portability and robustness to movement artifacts. Methods: A system with modular architecture was designed. Its core is a miniaturized 32-channel amplifier (Sensor Unit - SU) sampling at 2048 sps/ch with 16 bit resolution and wirelessly transmitting data to a PC or a mobile device. Each SU is a node of a Body Sensor Network for the synchronous signal acquisition from different muscles. Results: A prototype with two SUs was developed and tested. Each SU is small (3.4 cm × 3 cm × 1.5 cm), light (16.7 g), and can be connected directly to the electrodes; thus, avoiding the need for customary, wired setup. It allows to detect HD-sEMG signals with an average noise of 1.8 μVRMS and high performance in terms of rejection of power-line interference and motion artefacts. Tests performed on two SUs showed no data loss in a 22 m range and a ±500 μs maximum synchronization delay. Conclusions: Data collected in a wide spectrum of experimental conditions confirmed the functionality of the designed architecture and the quality of the acquired signals. Significance: By simplifying the experimental setup, reducing the hardware encumbrance, and improving signal quality during dynamic contractions, the developed system opens new perspectives in the use of HD-sEMG in applied and clinical settings.
Fast Item Ranking under Neural Network based Measures

Recently, plenty of neural network based recommendation models have demonstrated their strength in modeling complicated relationships between heterogeneous objects (i.e., users and items). However, the applications of these fine trained recommendation models are limited to the off-line manner or the re-ranking procedure (on a pre-filtered small subset of items), due to their time-consuming computations. Fast item ranking under learned neural network based ranking measures is largely still an open question. In this paper, we formulate ranking under neural network based measures as a generic ranking task, Optimal Binary Function Search (OBFS), which does not make strong assumptions for the ranking measures. We first analyze limitations of existing fast ranking methods (e.g., ANN search) and explain why they are not applicable for OBFS. Further, we propose a flexible graph-based solution for it, Binary Function Search on Graph (BFSG). It can achieve approximate optimal efficiently, with accessible conditions. Experiments demonstrate effectiveness and efficiency of the proposed method, in fast item ranking under typical neural network based measures.
Pseudo Dyna-Q: A Reinforcement Learning Framework for Interactive Recommendation

Applying reinforcement learning (RL) in recommender systems is attractive but costly due to the constraint of the interaction with real customers, where performing online policy learning through interacting with real customers usually harms customer experiences. A practical alternative is to build a recommender agent offline from logged data, whereas directly using logged data offline leads to the problem of selection bias between logging policy and the recommendation policy. The existing direct offline learning algorithms, such as Monte Carlo methods and temporal difference methods are either computationally expensive or unstable on convergence. To address these issues, we propose Pseudo Dyna-Q (PDQ). In PDQ, instead of interacting with real customers, we resort to a customer simulator, referred to as the World Model, which is designed to simulate the environment and handle the selection bias of logged data. During policy improvement, the World Model is constantly updated and optimized adaptively, according to the current recommendation policy. This way, the proposed PDQ not only avoids the instability of convergence and high computation cost of existing approaches but also provides unlimited interactions without involving real customers. Moreover, a proved upper bound of empirical error of reward function guarantees that the learned offline policy has lower bias and variance. Extensive experiments demonstrated the advantages of PDQ on two real-world datasets against state-of-the-arts methods.
Bioclimatic building design considering urban microclimate

The urban context is often simplified or neglected in building energy models (BEM) due to their incapacity to consider accurately all heat fluxes provided from the environment. Simplifying too much the urban context can have consequences on the accuracy of the BEM predictions. Many approaches exist to consider the impact of the urban environment on a building’s dynamic behaviour, its heating demand as well as the thermal comfort in summer. Taking into account BEM capabilities and the way consulting agencies work, a method is tested to consider the urban microclimate at the bioclimatic design stage of a building project.
Spatial and temporal resolution of mORC4 fluorescent variants reveals structural requirements for achieving higher order self-association and pronuclei entry

The Origin Replication Complex (ORC), which is a multi-subunit protein complex composed of six proteins ORC1–6, is essential for initiating licensing at DNA replication origins. We have previously reported that ORC4 has an alternative function wherein it forms a cage surrounding the extruded chromatin in female meiosis and is required for polar body extrusion (PBE). As this is a highly unexpected finding for protein that normally binds DNA, we tested whether ORC4 can actually form larger, higher order structures, which would be necessary to form a cage-like structure. We generated two fluorescent constructs of mouse ORC4, mORC4-EGFP and mORC4-FlAsH, to examine its spatial dynamics during oocyte activation in live cells. We show that both constructs were primarily monomeric throughout the embryo but self-association into larger units was detected with both probes. However, mORC4-FlAsH clearly showed higher order self-association and unique spatial distribution while mORC4-EGFP failed to form large structures during Anaphase II. Interestingly, both variants were found in the pronuclei suggesting that its role in DNA licensing is still functional. Our results with both constructs support the prediction that ORC4 can form higher order structures in the cytoplasm, suggesting that it is possible to form a cage-like structure. The finding that FlAsH labeled ORC4 formed demonstrably larger higher order structures than ORC4-GFP suggests that ORC4 oligomerization is sensitive to the bulky addition of GFP at its carboxy terminus.
A Novel Picorna-Like Virus in a Wabash Pigtoe (Fusconaia flava) from the Upper Mississippi River, USA

ABSTRACT Unionid mussels are threatened by multiple environmental stressors and have experienced mass mortality events over the last several decades, but the role of infectious disease in unionid health and population declines remains poorly understood. Although several microbial agents have been found in unionids, to date only one virus has been documented—Lea plague virus (Arenaviridae) in propagated Triangle Shell mussels (Hyriopsis cumingii) in China. We used next-generation DNA sequencing to screen hemolymph of seven individuals of five unionid species from the Upper Mississippi River basin, USA for viruses. We identified the complete polyprotein gene of a novel picornalike virus in one individual of the Wabash Pigtoe (Fusconaia flava). The virus is a member of the Nora virus clade of picornalike viruses and is most closely related to viruses from arthropods in China. We did not detect viruses in another Wabash Pigtoe or in animals of the other four species. It is premature to make inferences about the role of this virus in the health of Wabash Pigtoes or other unionid species or the origin or transmission of this virus. Nevertheless, to our knowledge, our results represent the first report of a virus in wild North American unionids. Technologies based on next-generation DNA sequencing should prove useful for identifying new viruses and investigating their role in unionid health and disease.
LDA Ensembles for Interactive Exploration and Categorization of Behaviors

We define behavior as a set of actions performed by some actor during a period of time. We consider the problem of analyzing a large collection of behaviors by multiple actors, more specifically, identifying typical behaviors and spotting anomalous behaviors. We propose an approach leveraging topic modeling techniques – LDA (Latent Dirichlet Allocation) Ensembles – to represent categories of typical behaviors by topics that are obtained through topic modeling a behavior collection. When such methods are applied to text in natural languages, the quality of the extracted topics are usually judged based on the semantic relatedness of the terms pertinent to the topics. This criterion, however, is not necessarily applicable to topics extracted from non-textual data, such as action sets, since relationships between actions may not be obvious. We have developed a suite of visual and interactive techniques supporting the construction of an appropriate combination of topics based on other criteria, such as distinctiveness and coverage of the behavior set. Two case studies on analyzing operation behaviors in the security management system and visiting behaviors in an amusement park, and the expert evaluation of the first case study demonstrate the effectiveness of our approach.
SIMULATIONS OF RF FIELD-INDUCED THERMAL FEEDBACK IN NIOBIUM AND Nb3Sn CAVITIES

Thermal feedback is a known limitation for SRF cavities made of low-purity niobium, as the increased losses at higher temperature described by BCS theory create a feedback mechanism that can eventually result in a runaway effect and associated cavity quench. In a similar manner, niobium cavities coated with Nb3Sn may also be subject to increased losses from thermal feedback, as Nb3Sn is possessed of a much lower thermal conductivity than niobium, although this effect will be mitigated by the thin film nature of the coating. In order to better understand the degree to which thermal feedback plays a role in the performance of Nb3Sn cavities, it is necessary to understand how the various components of the problem play a role in the outcome. In this paper, we present the first results from simulations performed at Cornell University that model RF induced thermal feedback in both conventional niobium cavities and niobium cavities coated with a thin film of Nb3Sn. The impacts of layer thickness, niobium substrate thermal conductivity, and trapped flux on the performance of the cavity are discussed.
Using the SpyTag SpyCatcher system to label smooth muscle myosin II filaments with a quantum dot on the regulatory light chain

The regulatory light chain (RLC) of myosin is commonly tagged to monitor myosin behavior in vitro, in muscle fibers, and in cells. The goal of this study was to prepare smooth muscle myosin (SMM) filaments containing a single head labeled with a quantum dot (QD) on the RLC. We show that when the RLC is coupled to a QD at Cys‐108 and exchanged into SMM, subsequent filament assembly is severely disrupted. To address this, we used a novel approach for myosin by implementing the SpyTag002 SpyCatcher002 system to prepare SMM incorporated with RLC constructs fused to SpyTag or SpyCatcher. We show that filament assembly, actin‐activated steady‐state ATPase activities, ability to be phosphorylated, and selected enzymatic and mechanical properties were essentially unaffected if either SpyTag or SpyCatcher were fused to the C‐terminus of the RLC. Crucially for our application, we also show that a QD coupled to SpyCatcher can be covalently attached to a RLC‐Spy incorporated into a SMM filament without disrupting the filament, and that the filaments can move along actin in vitro.
Survey of Computational Approaches to Lexical Semantic Change

Our languages are in constant flux driven by external factors such as cultural, societal and technological changes, as well as by only partially understood internal motivations. Words acquire new meanings and lose old senses, new words are coined or borrowed from other languages and obsolete words slide into obscurity. Understanding the characteristics of shifts in the meaning and in the use of words is useful for those who work with the content of historical texts, the interested general public, but also in and of itself. The findings from automatic lexical semantic change detection, and the models of diachronic conceptual change are currently being incorporated in approaches for measuring document across-time similarity, information retrieval from long-term document archives, the design of OCR algorithms, and so on. In recent years we have seen a surge in interest in the academic community in computational methods and tools supporting inquiry into diachronic conceptual change and lexical replacement. This article is an extract of a survey of recent computational techniques to tackle lexical semantic change currently under review. In this article we focus on diachronic conceptual change as an extension of semantic change.
Learning to Reconstruct People in Clothing From a Single RGB Camera

We present Octopus, a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving with a reconstruction accuracy of 4 to 5mm, while being orders of magnitude faster than previous methods. From semantic segmentation images, our Octopus model reconstructs a 3D shape, including the parameters of SMPL plus clothing and hair in 10 seconds or less. The model achieves fast and accurate predictions based on two key design choices. First, by predicting shape in a canonical T-pose space, the network learns to encode the images of the person into pose-invariant latent codes, where the information is fused. Second, based on the observation that feed-forward predictions are fast but do not always align with the input images, we predict using both, bottom-up and top-down streams (one per view) allowing information to flow in both directions. Learning relies only on synthetic 3D data. Once learned, Octopus can take a variable number of frames as input, and is able to reconstruct shapes even from a single image with an accuracy of 5mm. Results on 3 different datasets demonstrate the efficacy and accuracy of our approach.
A Hybrid Data Cleaning Framework Using Markov Logic Networks

With the increase of dirty data, data cleaning turns into a crux of data analysis. The accuracy limitation of the existing integrity constraints-based cleaning approaches results from insufficient rules. In this paper, we present a novel hybrid data cleaning framework on top of Markov logic networks (MLNs), termed as <inline-formula><tex-math notation="LaTeX">${\sf MLNClean}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">MLNClean</mml:mi></mml:math><inline-graphic xlink:href="gao-ieq1-3012472.gif"/></alternatives></inline-formula>, which is capable of learning instantiated rules to supplement the insufficient integrity constraints. <inline-formula><tex-math notation="LaTeX">${\sf MLNClean}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">MLNClean</mml:mi></mml:math><inline-graphic xlink:href="gao-ieq2-3012472.gif"/></alternatives></inline-formula> consists of two steps, i.e., <italic>pre-processing</italic> and <italic>two-stage data cleaning</italic>. In the pre-processing step, <inline-formula><tex-math notation="LaTeX">${\sf MLNClean}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">MLNClean</mml:mi></mml:math><inline-graphic xlink:href="gao-ieq3-3012472.gif"/></alternatives></inline-formula> first infers a set of probable instantiated rules according to MLNs and then builds a two-layer MLN index structure to generate multiple data versions and facilitate the cleaning process. In the two-stage data cleaning step, <inline-formula><tex-math notation="LaTeX">${\sf MLNClean}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">MLNClean</mml:mi></mml:math><inline-graphic xlink:href="gao-ieq4-3012472.gif"/></alternatives></inline-formula> first presents a concept of <italic>reliability score</italic> to clean errors within each data version separately, and afterward eliminates the conflict values among different data version using a novel concept of <italic>fusion score</italic>. Considerable experimental results on both real and synthetic scenarios demonstrate the effectiveness of <inline-formula><tex-math notation="LaTeX">${\sf MLNClean}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">MLNClean</mml:mi></mml:math><inline-graphic xlink:href="gao-ieq5-3012472.gif"/></alternatives></inline-formula> in practice.