Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal Fusion of Smart Home and Text-based Behavior Markers for Clinical Assessment Prediction

New modes of technology are offering unprecedented opportunities to unobtrusively collect data about people's behavior. While there are many use cases for such information, we explore its utility for predicting multiple clinical assessment scores. Because clinical assessments are typically used as screening tools for impairment and disease, such as mild cognitive impairment (MCI), automatically mapping behavioral data to assessment scores can help detect changes in health and behavior across time. In this article, we aim to extract behavior markers from two modalities, a smart home environment and a custom digital memory notebook app, for mapping to 10 clinical assessments that are relevant for monitoring MCI onset and changes in cognitive health. Smart-home-based behavior markers reflect hourly, daily, and weekly activity patterns, while app-based behavior markers reflect app usage and writing content/style derived from free-form journal entries. We describe machine learning techniques for fusing these multimodal behavior markers and utilizing joint prediction. We evaluate our approach using three regression algorithms and data from 14 participants with MCI living in a smart-home environment. We observed moderate to large correlations between predicted and ground-truth assessment scores, ranging from r = 0.601 to r = 0.871 for each clinical assessment.
SAND20XX-XXXXR LDRD PROJECT NUMBER: 14-2449 LDRD PROJECT TITLE: Non-resonant Nanoscale Extreme Light Confinement PROJECT TEAM MEMBERS:

A wide spectrum of photonics activities Sandia is engaged in such as solid state lighting, photovoltaics, infrared imaging and sensing, quantum sources, rely on nanoscale or ultrasubwavelength light-matter interactions (LMI). The fundamental understanding in confining electromagnetic power and enhancing electric fields into ever smaller volumes is key to creating next generation devices for these programs. The prevailing view is that a resonant interaction (e.g. in microcavities or surface-plasmon polaritions) is necessary to achieve the necessary light confinement for absorption or emission enhancement. Here we propose new paradigm that is non-resonant and therefore broadband and can achieve light confinement and field enhancement in extremely small areas [~(/500)^2 ]. The proposal is based on a theoretical work[1] performed at Sandia. The paradigm structure consists of a periodic arrangement of connected small and large rectangular slits etched into a metal film named double-groove (DG) structure. The degree of electric field enhancement and power confinement can be controlled by the geometry of the structure. The key operational principle is attributed to quasistatic response of the metal electrons to the incoming electromagnetic field that enables non-resonant broadband behavior. For this exploratory LDRD we have fabricated some test double groove structures to enable verification of quasistatic electronic response in the mid IR through IR optical spectroscopy. We have addressed some processing challenges in DG structure fabrication to enable future design of complex sensor and detector geometries that can utilize its non-resonant field enhancement capabilities. ].
Entity Resolution On-Demand

Entity Resolution (ER) aims to identify and merge records that refer to the same real-world entity. ER is typically employed as an expensive cleaning step on the entire data before consuming it. Yet, determining which entities are useful once cleaned depends solely on the user's application, which may need only a fraction of them. For instance, when dealing with Web data, we would like to be able to filter the entities of interest gathered from multiple sources without cleaning the entire, continuously-growing data. Similarly, when querying data lakes, we want to transform data on-demand and return the results in a timely manner---a fundamental requirement of ELT ( Extract-Load-Transform ) pipelines. We propose BrewER , a framework to evaluate SQL SP queries on dirty data while progressively returning results as if they were issued on cleaned data. BrewER tries to focus the cleaning effort on one entity at a time, following an ORDER BY predicate. Thus, it inherently supports top-k and stop-and-resume execution. For a wide range of applications, a significant amount of resources can be saved. We exhaustively evaluate and show the efficacy of BrewER on four real-world datasets.
Lactate inhibits Ca21-activated Ca21-channel activity from skeletal muscle sarcoplasmic reticulum

Favero, Terence G. , Anthony C. Zable, David Colter, and Jonathan J. Abramson. Lactate inhibits Ca21-activated Ca21-channel activity from skeletal muscle sarcoplasmic reticulum. J. Appl. Physiol. 82(2): 447–452, 1997. — Sarcoplasmic reticulum (SR) Ca21-release channel function is modified by ligands that are generated during about of exercise. We have examined the effects of lactate on Ca21and caffeine-stimulated Ca21 release, [3H]ryanodine binding, and single Ca21-release channel activity of SR isolated from rabbit white skeletal muscle. Lactate, at concentrations from 10 to 30mM, inhibited Ca21and caffeine-stimulated [3H]ryanodine binding to and inhibited Ca21and caffeine-stimulated Ca21 release from SR vesicles. Lactate also inhibited caffeine activation of single-channel activity in bilayer reconstitution experiments. These findings suggest that intense muscle activity, which generates high concentrations of lactate, will disrupt excitation-contraction coupling. This may lead to decreases in Ca21 transients promoting a decline in tension development and contribute to muscle fatigue.
What is Missing in Asset-Pricing Factor Models?

Our objective is to develop a methodology to price the cross section of asset returns. Despite the hundreds of systematic risk factors considered in the literature (“factor zoo”), there is still a sizable pricing error. We show that what is missing in assetpricing factor models is not systematic risk factors but compensation for asset-specific risk. We use this insight to construct a stochastic discount factor (SDF) that prices the cross section of stock returns exactly, and therefore, resolves the factor zoo. Empirically, we demonstrate that more than half of the variation in this SDF is explained by an aggregate measure of asset-specific risk that is correlated with strategies reflecting market frictions and behavioral biases. JEL classification: C52, C58, G11, G12.
A Recursive Early-Stopping Phase King Protocol

Early-stopping consensus protocols guarantee termination within a number of rounds that depends only on the actual number f of faulty nodes in a run, not the maximum number of faults that can be tolerated. We consider early-stopping deterministic synchronous consensus with Byzantine faults in a fully connected message passing system of n nodes. Many such protocols are known, but so far none combine early-stopping in O(f+1) rounds with optimal resilience and a bit complexity of o(n2(f+1)). We provide two solutions to the above problem. The first is fairly simple and almost matches the above goals, but has worst-case message and bit complexities of Θ(n2 log (f+2)). The second reduces the bit complexity further to O(n2) by calling itself recursively at most twice on Θ(n)-sized subsets. The result is the first protocol that is simultenously optimally resilient, asymptotically optimally early-stopping, and asymptotically bit- and message-optimal.
IR Evaluation and Learning in the Presence of Forbidden Documents

Many IR collections contain forbidden documents (F-docs), i.e. documents that should not be retrieved to the searcher. In an ideal scenario F-docs are clearly flagged, hence the ranker can filter them out, guaranteeing that no F-doc will be exposed. However, in real-world scenarios, filtering algorithms are prone to errors. Therefore, an IR evaluation system should also measure filtering quality in addition to ranking quality. Typically, filtering is considered as a classification task and is evaluated independently of the ranking quality. However, due to the mutual affinity between the two, it is desirable to evaluate ranking quality while filtering decisions are being made. In this work we propose nDCGf, a novel extension of the nDCGmin metric[14], which measures both ranking and filtering quality of the search results. We show both theoretically and empirically that while nDCGmin is not suitable for the simultaneous ranking and filtering task, nDCGf is a reliable metric in this case. We experiment with three datasets for which ranking and filtering are both required. In the PR dataset our task is to rank product reviews while filtering those marked as spam. Similarly, in the CQA dataset our task is to rank a list of human answers per question while filtering bad answers. We also experiment with the TREC web-track datasets, where F-docs are explicitly labeled, sorting participant runs according to their ranking and filtering quality, demonstrating the stability, sensitivity, and reliability of nDCGf for this task. We propose a learning to rank and filter (LTRF) framework that is specifically designed to optimize nDCGf, by learning a ranking model and optimizing a filtering threshold used for discarding documents with lower scores. We experiment with several loss functions demonstrating their success in learning an effective LTRF model for the simultaneous learning and filtering task.
Fake or Not? Covid-19 Misinformation, Between Science and Social Networks (cid:63)

With the continuous spread of the COVID-19 pandemic, mis-information poses serious threats and concerns. COVID-19-related mis-information integrates a mixture of health aspects along with news and political misinformation. This mixture complicates the ability to judge whether a claim related to COVID-19 is fake or not. Eventually, this also impacts the ability to reuse knowledge and may bias machine learning models and their ability to judge whether a claim is false or not. In an eﬀort to deal with these issues, we aggregated several COVID-19 mis-information datasets and compared diﬀerences between learning models from individual datasets versus the aggregated ones.
Chapter 6 Digital Twins and Intelligent Decision Making

Chapter 5 discussed ways of simulating and modelling manufacturing systems an offline manner. Offline in this context means the simulation is running disconnected from the real system and is reliant on the user to update parameters and data to maintain the accuracy of the model. In contrast, this chapter details online simulations, where the model is connected directly to the physical system and is automatically updated as the system changes. This approach is common called a digital twin. In addition, this chapter will discuss decision support systems, which are software packages intended to enhance the decision making process discussed in Chapter 4, and to make complex problems solvable. A digital twin is a simulated replica of a complex system. Unlike more
Soil diversity at mars: comparison of dataset from gale and jezero craters

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Soil diversity at mars: comparison of dataset from gale and jezero craters A Cousin, P Y Meslin, E M Hausrath, E Cardarelli, J Lasue, O Forni, O Beyssac, L C Kah, L Mandon, Olivier Gasnault, et al.
Discovering Association Rules from Big Graphs

This paper tackles two challenges to discovery of graph rules. Existing discovery methods often (a) return an excessive number of rules, and (b) do not scale with large graphs given the intractability of the discovery problem. We propose an application-driven strategy to cut back rules and data that are irrelevant to users' interests, by training a machine learning (ML) model to identify data pertaining to a given application. Moreover, we introduce a sampling method to reduce a big graph G to a set H of small sample graphs. Given expected support and recall bounds, the method is able to deduce samples in H and mine rules from H to satisfy the bounds in the entire G . As proof of concept, we develop an algorithm to discover Graph Association Rules (GARs), which are a combination of graph patterns and attribute dependencies, and may embed ML classifiers as predicates. We show that the algorithm is parallelly scalable, i.e. , it guarantees to reduce runtime when more machines are used. We experimentally verify that the method is able to discover rules with recall above 91% when using sample ratio 10%, with speedup of 61 times.
Progress of Ni-based Composite Catalysts for Methane Dry Reforming

Methane Dry Reforming (DRM) catalysis converts the two greenhouse gases, CO2 and CH4 into syngas, which can be further utilized for liquid fuels production through Fischer-Tropsch synthesis. The DRM reaction is therefore of significance for both energy and environments. The key issue to promote industrialization of DRM reaction is to develop suitable catalysts. Nickel based composite catalysts have received extensive attention due to their outstanding catalytic activities comparable to those of precious metals and their bargain price. However, nickel based catalysts usually undergo severe deactivation due to the coke deposition and metal sintering during long-term reaction at high temperature, which seriously limit their industrial applications and development of the engineering of DRM. Many researches have been carried out to address this issue. In this review, the recent research progress in the activity, coke resistance, and sintering resistance of Ni-based composite catalysts are introduced from the aspect of catalyst components, structures, preparation methods, and simulations. The research trends of DRM catalysts are also predicted based on the recent research progress in single-atomic catalysis and the in-situ characterization techniques for catalysis.
Muscular adaptations and insulin-like growth factor-I (IGF-I) responses to resistance training are stretch-mediated

Introduction: Modulation of muscle characteristics was attempted through altering muscle stretch during resistance training. We hypothesized that stretch would enhance muscle responses. Methods: Participants trained for 8 weeks, loading the quadriceps in a shortened (SL, 0–50 knee flexion; n 5 10) or lengthened (LL, 40–90 ; n 5 11) position, followed by 4 weeks of detraining. Controls (CON; n 5 10) were untrained. Quadriceps strength, vastus lateralis architecture, anatomical cross-sectional area (aCSA), and serum insulin-like growth factor-1 (IGF-1) were measured at weeks 0, 8, 10, and 12. Results: Increases in fascicle length (29 6 4% vs. 14 6 4%), distal aCSA (53 6 12% vs. 18 6 8%), strength (26 6 6% vs. 7 6 3%), and IGF-1 (31 6 6% vs. 7 6 6%) were greater in LL compared with SL muscles (P<0.05). No changes occurred in CON. Detraining decrements in strength and aCSA were greater in SL than LL muscles (P<0.05). Conclusions: Enhanced muscle in vivo (and somewhat IGF-1) adaptations to resistance training are concurrent with muscle stretch, which warrants its inclusion within training. Muscle Nerve 49:108–119, 2014 Skeletal muscle architectural and morphological characteristics are important due to their direct influence on functional performance (i.e., strength and power). Therefore, a key to optimizing human function is to understand the mechanical stimuli that induce alterations in muscle characteristics/ properties. In relation to muscle architecture, there are reports of increases in fascicle length after resistance training. An increase in fascicle length is thought to be brought about by the addition of in-series sarcomeres. Muscle length (or passive tension/stretch) and excursion have been shown to be major regulators of serial sarcomerogenesis in animals and appear to be relatively independent of both muscle activation level and tension. However, in humans, there exists conflicting evidence from studies on the major mechanical stimuli for such an adaptation. In young adults, muscle contraction type (eccentric vs. concentric) was investigated as a possible primary candidate for fascicle length change. The investigators concluded that other factors (possibly excursion of muscle during resistance training) were the main mechanical stimuli for changes in fascicle length. In contrast, in older individuals, Reeves et al. found that eccentric contractions (through enhanced training stimulus and associated greater muscle–tendon strain) were the driving force behind greater increases in fascicle length, compared with conventional weight training. Therefore, the primary constituent in resistance training for regulating fascicle length in humans remains
Incremental Evaluation of Continuous Analytic Queries in a High-Level Query Language

Data analytics have received a significant attention in recent years, as huge amounts of data is generated each day from various sources. Analysis of these massive data poses an interesting but challenging task and requires new forms of processing to enable enhanced decision making, insight discovery and process optimization. In addition, besides their ever increasing volume, data sets change frequently, and as such, results to continuous queries have to be updated at short intervals. In this thesis, we address the problem of evaluating continuous queries over big data streams that are frequently updated. To this end, we adopt HIFUN, a high-level query language, proposed for expressing analytic queries over big data sets. HIFUN offers a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer where queries are evaluated, by encoding them as map-reduce jobs or as SQL group-by queries, thus supporting different types of data set formats. Using HIFUN, we design an algorithm for incremental evaluation of continuous queries, processing only the most recent data batch, and exploiting already computed information, without requiring the evaluation of the query over the complete data set. Subsequently, we translate the generic algorithm to both SQL and MapReduce using SPARK, exploiting the query rewriting methods provided by HIFUN. Using a synthetic data set, we demonstrate the effectiveness of our approach in achieving query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization in our implementation.
therapy: a review of 10 empirical investigations

Ten empirical investigations of Milan Family Therapy (MFT)are reviewed in this paper. The studies include both single group and comparative group outcome trials; investigations of therapeutic process; clinical audit and consumer satisfaction surveys. Substantive findings and methodological issues are discussed in the light of family therapy and individual psychotherapy research generally. present review show positive systemic change as a result of MFT. Improvement in marital and family functioning noted after therapy is sustained at 6 month follow-up and possibly longer. This finding is consistent across a variety of standardized and unstandardized self-report measures of systemic functioning.
Curvature as an Organizing Principle of Mid-level Visual Representation: A Semantic-preference Mapping Approach

A central challenge in visual neuroscience is understanding the mid-level representations of the ventral stream. We used a novel, data-driven approach (semantic-preference mapping) combined with an image-statistics approach (curvature index) to characterize the mid-level features of category-selective visual regions. First, we ﬁt voxelwise encoding models using a deep convolutional neural network (DCNN) to predict scene-evoked fMRI responses in object-selective and scene-selective regions. We then performed semantic-preference mapping to examine how the responses of these encoding models changed when speciﬁc object classes were removed from natural images. This analysis motivated the hypothesis that object-selective cortex model is best predicted by mid-level features with curved contours, while scene-selective cortex model is best predicted by mid-level features with rectilinear contours. We further developed an image-computable model that outputs a summary statistic for the prevalence of curved contours in local image patches, and we used this model to demonstrate the importance of curvature-preferences for linking DCNN representations with the representations of category-selective cortex models. Overall, our ﬁndings suggest that curvature is a key property of the mid-level representations that are shared between DCNNs and category-selective cortex models of the ventral visual stream.
An Overview of Convergence Analysis of Deep Neural Networks and the Visual Explanations of their Predictions

—In this report, we reviewed the theoretical and practical aspects of explaining the success of deep neural networks (DNN). The review is centered around two papers, one representing the issue of provable global convergence of the learning algorithms used in DNN [1], other representing a practical approach to explain the predictions generated by them [2]. In [1], a modified learning algorithm has been proposed which can provide provable global convergence guarantee of DNN beyond the so-called NTK regime. In [2], a generalized version of class activation map (CAM) has been introduced which can provide improved class-discriminative property, object localization ability, reliability and interpretability. Some potential future research directions in both domains have been provided in this review.
Recent Progress of Web-enable Material Database and a Case Study of NIMS and MatWeb

Web-enable material database(MDB)is a new model to obtain information based on the development of modern information technology,and is widely used in the fields of new material design;material selection;strategic decision consultation and so on.It is very important to grasp timely the latest tendency of web-enable MDBs and to utilize accurately and efficiently web-enable MDBs for scientific research and engineering application.Consequently the latest developments of web-enable MDBs were introduced in this paper;NIMS and MatWeb,two internationally famous online MDBs,were also studied.The paper expounds it from the following aspects:data service contents;search function;retrieved results processing etc.The aim of this paper is to help researchers select and use webenable MDBs precisely and efficiently.In the writer's view,the future developments of web-enable MDBs are as follows:one-stop service by web integration;more data types;more data quantities;more standards;as well as more complete information service derived from the database.