Miracle-dz/UniFAR-FTUs huggingface dataset:
Construct Validity of the Upper-Limb Interlimb Coordination Test in Stroke

Background Coordination impairments are under-evaluated in patients with stroke due to the lack of validated assessments resulting in an unclear relationship between coordination deficits and functional limitations. Objective Determine the construct validity of the new clinical upper-limb (UL) Interlimb Coordination test (ILC2) in individuals with chronic stroke. Methods Thirteen individuals with stroke, ≥40 years, with ≥30° isolated supination of the more-affected (MAff) arm, who could understand instructions and 13 healthy controls of similar age participated in a cross-sectional study. Participants performed synchronous bilateral anti-phase forearm rotations for 10 seconds in 4 conditions: self-paced internally-paced (IP1), fast internally-paced (IP2), slow externally-paced (EP1), and fast externally-paced (EP2). Primary (continuous relative phase-CRP, cross-correlation, lag) and secondary outcome measures (UL and trunk kinematics) were compared between groups. Results Participants with stroke made slower UL movements than controls in all conditions, except EP1. Cross-correlation coefficients were lower (i.e., closer to 0) in stroke in IP1, but CRP and lag were similar between groups. In IP1 and matched-speed conditions (IP1 for healthy and IP2 for stroke), stroke participants used compensatory trunk and shoulder movements. The synchronicity sub-scale and total scores of ILC2 were related to temporal coordination in IP2. Interlimb Coordination test total score was related to greater shoulder rotation of the MAff arm. Interlimb Coordination test scores were not related to clinical scores. Conclusion Interlimb Coordination test is a valid clinical measure that may be used to objectively assess UL interlimb coordination in individuals with chronic stroke. Further reliability testing is needed to determine the clinical utility of the scale.
Trends of Colorectal Cancer Incidence in Kazakhstan

Background and objective: Colorectal cancer (CRC) remains one of the most widespread human malignancies. The aim of this study was to study trends of the incidence of CRC in Kazakhstan. Materials and Method: This retrospective study was done using descriptive and analytical methods of oncoepidemiology. Results: During the study period from 2009 to 2018, 28,950 new cases of CRC were recorded, 13,779 (47.6%) cases were allocated to men and 15,171 (52.4%) to women. It was found that the incidence rate of CRC increased from 14.79 (2009) to 17.72 in 2018 and the overall growth was 2.93 cases per 100,000. This increase was due to the age structure – ∑ΔA=+1.42, the risk of acquiring illness – ∑ΔR=+1.31, and their combined effect – ∑ΔRA=+0.20. The component analysis results revealed that the increase in the number of patients with CRC was mainly due to the growth of the population (ΔP=+37.7%), changes in age structure (ΔA=+26.6%), and changes associated with the risk of acquiring illness (ΔR=+24.6%). The number of patients (both sexes) was increasing in many regions largely due to the influence of the age structure of the population. In addition, it was found that growth in the number of patients in most regions, both men and women, was associated primarily with the risk of acquiring illness. Conclusion: The findings of the current study showed increasing trends in the incidence of CRC in all regions of the country. These changes were mainly influenced by demographic factors (population size and age structure), risk of acquiring the disease, and their combined effect.
Book-Sharing for Parenting and Child Development in South Africa: A Randomized Controlled Trial.

This study evaluated the impact of a parenting intervention on children's cognitive and socioemotional development in a group of caregivers and their 21-to-28-month-old children in a low-income South African township. A randomized controlled trial compared an experimental group (n = 70) receiving training in dialogic book-sharing (8 weekly group sessions) with a wait-list control group (n = 70). They were assessed before the intervention, immediately following it, and at a six month follow-up. The intervention had positive effects on child language and attention, but not behavior problems, prosocial behavior, or theory of mind. Intervention caregivers were less verbally and psychologically harsh, showed more sensitivity and reciprocity and more complex cognitive talk. This program benefitted parenting and child development and holds promise for low-income contexts.
Glomerular Endothelial Cell-Derived microRNA-192 Regulates Nephronectin Expression in Idiopathic Membranous Glomerulonephritis

Visual Abstract Significance Statement MicroRNA-192-5p (miR-192-5p) is upregulated in the glomeruli and urine of patients with idiopathic membranous glomerulonephritis (iMGN). It derives from glomerular endothelial cells, is packed into exosomes, and decreases podocyte nephronectin (NPNT) in the glomerular basement membrane (GBM) by paracrine signaling. Patients with iMGN have reduced glomerular NPNT expression. Whole-body knockdown of npnt in zebrafish and podocyte-specific knockout of Npnt in mice damage the GBM, increasing lucidity of the lamina rara interna, which admits high molecular weight proteins. Reduced NPNT leading to GBM leakiness might be an important part of iMGN pathophysiology, initiating podocyte antigen presentation, and admitting autoantibodies into the subepithelial space. NPNT might be a prognostic parameter and noninvasive marker for iMGN. Monitoring and targeting this miR could be a promising diagnostic and therapeutic approach for iMGN. Background Autoantibodies binding to podocyte antigens cause idiopathic membranous glomerulonephritis (iMGN). However, it remains elusive how autoantibodies reach the subepithelial space because the glomerular filtration barrier (GFB) is size selective and almost impermeable for antibodies. Methods Kidney biopsies from patients with iMGN, cell culture, zebrafish, and mouse models were used to investigate the role of nephronectin (NPNT) regulating microRNAs (miRs) for the GFB. Results Glomerular endothelial cell (GEC)-derived miR-192-5p and podocyte-derived miR-378a-3p are upregulated in urine and glomeruli of patients with iMGN, whereas glomerular NPNT is reduced. Overexpression of miR-192-5p and morpholino-mediated npnt knockdown induced edema, proteinuria, and podocyte effacement similar to podocyte-derived miR-378a-3p in zebrafish. Structural changes of the glomerular basement membrane (GBM) with increased lucidity, splitting, and lamellation, especially of the lamina rara interna, similar to ultrastructural findings seen in advanced stages of iMGN, were found. IgG-size nanoparticles accumulated in lucidity areas of the lamina rara interna and lamina densa of the GBM in npnt-knockdown zebrafish models. Loss of slit diaphragm proteins and severe structural impairment of the GBM were further confirmed in podocyte-specific Npnt knockout mice. GECs downregulate podocyte NPNT by transfer of miR-192-5p–containing exosomes in a paracrine manner. Conclusions Podocyte NPNT is important for proper glomerular filter function and GBM structure and is regulated by GEC-derived miR-192-5p and podocyte-derived miR-378a-3p. We hypothesize that loss of NPNT in the GBM is an important part of the initial pathophysiology of iMGN and enables autoantigenicity of podocyte antigens and subepithelial immune complex deposition in iMGN.
Planification et modèle graphique pour la génération dynamique de scénarios en environnements virtuels. (Planning and graphical models for the dynamic generation of scenarios in virtual environment)

Our work is related to the training of crisis management in virtual environments. The specification of possible unfoldings of events in a simulation is essential for human learning in a virtual environment. This allows both to propose and orchestrate personalized learning situations and also to bring the learner toward relevant and educative scenarios. The work presented in this thesis focuses on the dynamic generation of scenarios and their execution in a virtual environment. For that, we aim at a set of objectives that are often contradictory : the freedom of action of the user, the generation of various scenarios that respect the authorial intent, the narrative control and the capacity of the system to adapt to deviations from the learner. The different approaches of interactive storytelling tackle more or less some of these objectives, but it is difficult to satisfy them all, and this is the challenge of our work. In addition to these objectives, we also aim at facilitating the modeling of the narrative content, which is still a real issue today when it comes to model complex environments such as the ones related to crisis management. We propose an emergent approach where the scenario experienced by the learner will emerge from the interactions between the learner, the virtual characters and our narrative system MENTA. MENTA is in charge of the narrative control by proposing a set of adjustments (over the simulation) that satisfies narrative objectives chosen by the trainer (e. g., a list of specific skills). These adjustments take the form of a prescribed scenario that is generated by MENTA via a planning engine that we have coupled with fuzzy cognitive maps through a macro-operator FRAG. A FRAG is used to model FRAGment of scenario in the form of scripted sequences of actions/events. The originality of our approach relies on a strong coupling between planning and graphical models which preserves the exploration capability and the generative power of a planning engine (which contributes to the generation of various and adaptable scenarios), while facilitating the modeling of narrative content as well as the authorial intent thanks to fragments of scenario that are scripted by the author and used during the planning process. We have worked on a concrete application example of scenarios dealing with the management of a massive influx of victims. Then, we have implemented MENTA and generated scenarios related to this example. Finally, we have tested and analyzed the performance of our system.
High‐Throughput and Dosage‐Controlled Intracellular Delivery of Large Cargos by an Acoustic‐Electric Micro‐Vortices Platform

Intracellular delivery of cargos for cell engineering plays a pivotal role in transforming medicine and biomedical discoveries. Recent advances in microfluidics and nanotechnology have opened up new avenues for efficient, safe, and controllable intracellular delivery, as they improve precision down to the single-cell level. Based on this capability, several promising micro- and nanotechnology approaches outperform viral and conventional non-viral techniques in offering dosage-controlled delivery and/or intracellular delivery of large cargos. However, to achieve this level of precision and effectiveness, they are either low in throughput, limited to specific cell types (e.g., adherent vs. suspension cells), or complicated to operate with. To address these challenges, here we introduce a versatile and simple-to-use intracellular delivery microfluidic platform, termed Acoustic-Electric Shear Orbiting Poration (AESOP). Hundreds of acoustic microstreaming vortices form the production line of the AESOP platform, wherein hundreds of thousands of cells are trapped, permeabilized, and mixed with exogenous cargos. Using AESOP, we show intracellular delivery of a wide range of molecules (from <1 kDa to 2 MDa) with high efficiency, cell viability, and dosage-controlled capability into both suspension and adherent cells and demonstrate throughput at 1 million cells/min per single chip. In addition, we demonstrate AESOP for two gene editing applications that require delivery of large plasmids: i) eGFP plasmid (6.1 kbp) transfection, and ii) CRISPR-Cas9-mediated gene knockout using a 9.3 kbp plasmid DNA encoding Cas9 protein and sgRNA. Compared to alternative platforms, AESOP not only offers dosage-controlled intracellular delivery of large plasmids (>6kbp) with viabilities over 80% and comparable delivery efficiencies, but also is an order of magnitude higher in throughput, compatible with both adherent and suspension cell lines, and simple to operate.
Differentiating global luminance, arousal and cognitive signals on pupil size and microsaccades

Pupil size reflects a proxy for neural activity associated with global luminance, arousal and cognitive processing. Microsaccades are also modulated by arousal and cognitive processing. Are these effects of arousal and cognitive signals on pupil size and microsaccades coordinated? If so, via what neural mechanisms? We hypothesized that if pupil size and microsaccades are coordinately modulated by these processes, pupil size immediately before microsaccade onset, as an index for ongoing cognitive and arousal processing, should correlate with microsaccade responses during tasks alternating these signals. Here, we examined the relationship between pupil size and microsaccade responses in tasks that included variations in global luminance, arousal and inhibitory control. Higher microsaccade peak velocities correlated with larger pre‐microsaccade pupil response related to arousal and inhibitory control signals. In contrast, pupil responses evoked by global luminance signals did not correlate with microsaccade responses. Given the central role of the superior colliculus in microsaccade generation, these results suggest the critical involvement of the superior colliculus to coordinate pupil and microsaccade responses for arousal and inhibitory control modulations, but not for the pupil luminance modulation.
CMS 165 Foundations of Machine Learning Homework 3

In [1], the convergence analysis of tensor power method is shown for a tensor T which has an orthogonal tensor decomposition. In this question, we will look at the case where tensors have non-orthogonal tensor decomposition. Non-orthogonal tensor decompositions can be reduced to orthogonal tensor decomposition and then tensor power method can be used to recover rank-1 components of orthogonal tensor decomposition. Question 2. Mathematically describe the conversion of non-orthogonal tensor decomposition to orthogonal decomposition. How can we obtain the non-orthogonal components from the output of tensor power method? Hint: In order to answer this question and for further details, it would be good to refer to TensorBook on Piazza.
12 Months of Balance Training Decreases Ankle-Joint Injury Frequency in Young Girl Basketball Players

Summary Aim: Balance training can help reduce the frequency of ankle joint injuries in adults, but data on young girls is relatively scant. Methods: Out of 91 female basketball players (7-16 years), 40 players were assigned to an experimental balance training group, and 51 players were assigned to a control group. Both groups performed their typical basketball training but the experimental group performed additional balance-focused exercises during the warm-up of their basketball training sessions. Results: A total of 47 girls (52 %) reported at least one ankle joint injury in their entire basketball history (52.5 % in the experimental, 50.9% in the control group). There was a positive correlation between the number of ankle joint injuries and age before the experiment (r = 0.34, p < 0.001). During the 12-month study period, girls who participated in balance training suffered significantly fewer ankle joint injuries than the control group (χ2 = 3.8423, df = 1, p-value < 0.05). Increased risk for ankle joint injury in the future was confirmed according to logistic regression for girls with previous ankle joint injury in history (4.33 times) and girls who did not participate in the balance training program (3.97 times). Conclusions: A progressive, barefoot balance training program that is simple and short enough to be performed during normal warm-ups can reduce the likelihood of suffering an ankle joint injury in young girls basketball players, which may be useful for athletes in other sports as well.
PyTFL: A Python-based Neural Team Formation Toolkit

We present PyTFL, a library written in Python for the team formation task. In team formation task, the main objective is to form a team of experts given a set of skills. We demonstrate an efficient and well-structured open-source toolkit that can easily be imported into Python. Our toolkit incorporates state-of-the-art approaches for team formation, e.g., neural-based team formation, and supports team formation sub-tasks such as collaboration graph preparation, model training and validation, systematic evaluation based on qualitative and quantitative team metrics, and efficient team formation and prediction. While there are strong research papers on the team formation problem, PyTFL is the first toolkit to be publicly released for this purpose.
Spatio-Temporal-Social Multi-Feature-based Fine-Grained Hot Spots Prediction for Content Delivery Services in 5G Era

The arrival of 5G networks has extensively promoted the growth of content delivery services (CDSs). Understanding and predicting the spatio-temporal distribution of CDSs are beneficial to mobile users, Internet Content Providers and carriers. Conventional methods for predicting the spatio-temporal distribution of CDSs are mostly base-stations (BSs) centric, leading to weak generalization and spatio coarse-grained. To improve the spatio accuracy and generalization of modeling, we propose user-centric methods for CDSs spatio-temporal analysis. With geocoding and spatio-temporal graphs modeling algorithms, CDSs records collected from mobile devices are modeled as dynamic graphs with spatio-temporal attributes. Moreover, we propose a spatio-temporal-social multi-feature extraction framework for spatio fine-grained CDSs hot spots prediction. Specifically, an edge-enhanced graph convolutional block is designed to encode CDSs information based on the social relations and the spatio dependence features. Besides, we introduce the Long Short Term Memory (LSTM) to further capture the temporal dependence. Experiments on two real-world CDSs datasets verified the effectiveness of the proposed framework, and ablation studies are taken to evaluate the importance of each feature.
CLC-RS: A Chinese Legal Case Retrieval System with Masked Language Ranking

With the ever-increasing size of legal cases in China, relevant legal case retrieval given a user query has attracted considerable attention. Conventional keyword-based retrieval systems look for matching cases that contain one or more words specified by the user. However, keyword search is sharply focused on finding the exact terms specified in the query, making the retrieval systems miss many relevant documents. In addition, it is difficult for new users to identify appropriate keywords for accurate legal case retrieval. In this paper, we develop a novel Chinese legal case retrieval system (called CLC-RS), which improves the quality of semantic search with natural language queries in the legal domain. CLC-RS performs legal case retrieval in a two-stage fashion. First, we employ a classic token-based ranking method to efficiently reduce the solution space, returning a subset of candidate legal cases. Then, we deploy a novel masked language ranking model to re-rank the candidate legal cases. The experimental results show that the proposed system is both efficient and effective, providing a practical information retrieval (IR) system for retrieving Chinese legal cases. The web site for the developed CLC-RS system is available at: https://www.delilegal. com/.
Influence Maximization With Co-Existing Seeds

In the classical influence maximization problem we aim to select a set of nodes, called seeds, to start an efficient information diffusion process. More precisely, the goal is to select seeds such that the expected number of nodes reached by the diffusion process is maximized. In this work we study a variant of this problem where an unknown (up to a probability distribution) set of nodes, referred to as co-existing seeds, joins in starting the diffusion process even if not selected. This setting allows to model that, in certain situations, some nodes are willing to act as "voluntary seeds'' even if not chosen by the campaign organizer. This may for example be due to the positive nature of the information campaign (e.g., public health awareness programs, HIV prevention, financial aid programs), or due to external social driving effects (e.g., nodes are friends of selected seeds in real life or in other social media). In this setting, we study two types of optimization problems. While the first one aims to maximize the expected number of reached nodes, the second one endeavors to maximize the expected increment in the number of reached nodes in comparison to a non-intervention strategy. The problems (particularly the second one) are motivated by cooperative game theory. For various probability distributions on co-existing seeds, we obtain several algorithms with approximation guarantees as well as hardness and hardness of approximation results. We conclude with experiments that demonstrate the usefulness of our approach when co-existing seeds exist.
Adversarial Kernel Sampling on Class-imbalanced Data Streams

This paper investigates online active learning in the setting of class-imbalanced data streams, where labels are allowed to be queried of with limited budgets. In this setup, conventional learning would be biased towards majority classes and consequently harm the performance. To address this issue, imbalance learning technique adopts both asymmetric losses and asymmetric queries to tackle the imbalance. Although this approach is effective, it may not guarantee the performance in an adversarial setting where the actual labels are unknown, and they may be chosen by the adversary To learn a promising hypothesis in class-imbalanced and adversarial environment, we propose an asymmetric min-max optimization framework for online classification. The derived algorithm can track the imbalance and bound the choices of an adversary simultaneously. Despite the promising result, this algorithm assumes that the label is provided for every input, while label is scare and labeling is expensive in real-world application. To this end, we design a confidence-based sampling strategy to query the informative labels within a budget. We theoretically analyze this algorithm in terms of mistake bound, and two asymmetric measures. Empirically, we evaluate the algorithms on multiple real-world imbalanced tasks. Promising results could be achieved on various application domains.
Budget-constrained Truss Maximization over Large Graphs: A Component-based Approach

Cohesive substructure identification is one fundamental task of graph analytics. Recently, a useful problem of dense subgraph maximization has attracted significant attentions, which aims at enlarging a dense subgraph pattern using a few new edge insertions, e.g., k-core maximization. As a more cohesive subgraph of k-core, k-truss requires that each edge has at least k-2 triangles within this subgraph. However, the problem of k-truss maximization has not been studied yet. In this paper, we motivate and formulate a new problem of budget-constrained k-truss maximization. Given a budget of b edges and an integer k≥2, the problem is to find and insert b new edges into a graph G such that the resulted k-truss of G is maximized. We theoretically prove the NP-hardness of k-truss maximization problem. To efficiently tackle it, we analyze non-submodular property of k-truss newcomers function and develop non-conventional heuristic strategies for edge insertions. We first identify high-quality candidate edges with regard to (k-1)-light subgraphs and propose a greedy algorithm using per-edge insertion. Besides further improving the efficiency by pruning disqualified candidate edges, we finally develop a component-based dynamic programming algorithm for enlarging k-truss mostly, which makes a balance of budget assignment and inserts multiple edges simultaneously into all (k-1)-light components. Extensive experiments on nine real-world graphs demonstrate the efficiency and effectiveness of our proposed methods.