Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender role in sleep disturbances among older adults with traumatic brain injury

Abstract Older adults are particularly vulnerable to poor long-term outcomes, and the rate of TBI in this group is increasing. Studies have shown females experience worse outcomes from TBI than males, however this research has been limited. The aim of this study is to examine gender effects on the frequency of sleep disturbances in older adults post-TBI. An analysis was conducted on data obtained from the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set. A total of 405 patients greater than 60 years of age were examined. Sleep disturbances were measured using the Nighttime Behavioural Disturbances domain of the Neuropsychiatric Inventory–Questionnaire (NPI-Q). A significant difference (p = 0.025) in reported sleep disturbance was identified in the female TBI population relative to the female non-TBI population. In the male non-TBI group, 14.8% (n = 12) experienced nighttime disturbances while 19.8% (n = 17) of those with TBI experienced nighttime disturbances. This difference was not significant (p = 0.305). These results suggest there is a greater impact from traumatic brain injury on sleep disturbances in older females than males. Further research examining gender differences in older adults related to neuropsychiatric outcomes of TBI should be considered given the implications for treatment.
Normative, passionate, or problematic? Identification of adolescent gamer subtypes over time

Background and aims For most youngsters, gaming is a fun and innocent leisure activity. However, some adolescents are prone to develop problematic gaming behavior. It is therefore important to have a comprehensive understanding of psychosocial and game-related characteristics that differentiate highly engaged gamers from problematic gamers. To that end, this study evaluated the stability and consistency of Internet gaming criteria (as suggested by the DSM-5) and psychosocial characteristics in a two-wave longitudinal study including 1928 young adolescents (mean age = 13.3 years, SD = 0.91, 57% boys). Methods A confirmatory factor analysis revealed good stability of the Internet gaming disorder (IGD) construct over time. Latent class analyses revealed three classes for boys (recreational, engaged, and problematic) and two classes for girls (recreational and engaged). Results Significant differences between classes emerged for problem criteria (conflict and problems in social life), gaming duration, impulsivity, social competence, and attention/hyperactivity. The absence of a problematic gaming class for girls suggests that girls are less likely to develop problematic gaming behavior. Discussion The IGD criteria as proposed by the DSM-5 are a helpful tool to identify problematic gamers, although the results of this study suggest that using a strict cut-off point might result in false positives, particularly for boys. Problem criteria appeared to be the most sensitive and specific in identifying the problematic gamer, whereas escapism criteria were the least specific and sensitive. Careful consideration of the current proposed criteria to identify problematic gaming behavior could benefit the research and clinical field.
MicroRNA-939-5p directly targets IGF-1R to inhibit the aggressive phenotypes of osteosarcoma through deactivating the PI3K/Akt pathway

The dysregulation of microRNA-939-5p (miR-939) is involved in the development of multiple types of human cancer. However, the expression and roles of miR-939 in osteosarcoma (OS) have yet to be clarified. The expression level of miR-939 in OS was measured using reverse transcription quantitative polymerase chain reaction (RT-qPCR). A Cell Counting Kit-8 assay, flow cytometry analysis, Transwell migration and invasion assays, and a tumor xenograft assay were employed to explore the effects of miR-939 in OS cells. Bioinformatics analysis, RT-qPCR, western blot analysis and luciferase reporter assays were performed to explore its underlying mechanism. Expression of miR-939 was decreased in both OS tissues and cell lines. The decreased miR-939 expression was notably correlated with clinical stage and distant metastasis in patients with OS, where low miR-939 levels were correlated with lower overall survival. miR-939 overexpression decreased OS cell proliferation, migration and invasion in vitro; induced cell apoptosis, and impaired tumor growth in vivo. Mechanistically, insulin-like growth factor 1 receptor (IGF-1R) was characterized as direct target gene of miR-939 in OS. The tumor-suppressing effects of miR-939 in OS cells were imitated by IGF-1R knockdown. In addition, exogenous IGF-1R expression abolished the tumor suppressive roles of miR-939 in OS cells. miR-939 was implicated in the deactivation of the PI3K/Akt pathway in OS in vitro and in vivo through regulating IGF-1R expression. The present study demonstrated that miR-939 exerted tumor-suppressing roles in the malignancy of OS cells by directly targeting IGF-1R and inactivating the PI3K/AKT pathway. Therefore, this miRNA may be a promising target for anticancer therapy in patients with OS.
MicroRNA-195 inhibits epithelial-mesenchymal transition by targeting G protein-coupled estrogen receptor 1 in endometrial carcinoma

Epithelial-mesenchymal transition (EMT) has been shown to exert promoting effects on the progression of a number of cancer types, including endometrial carcinoma (EC). MicroRNA (miRNA or miR)-195 has been shown to function as a tumor suppressor. This study aimed to explore the potential role of miR-195 in the EMT process of EC. miR-195 overexpression (Mimics) and mimics control (Mock) vectors were constructed and transfected into human endometrial cancer cells (AN3-CA and Hec1A) using Lipofectamine 2000, and cell viability was detected using the Cell Counting kit-8 (CCK-8). The invasive and migratory capacities of the cells transfected with the Mimics or Mock vectors were assessed by Transwell and wound healing assays. Relative mRNA and protein levels were analyzed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blot analysis, respectively. Using TargetScan prediction, the potential target of miR-195 was identified and was further verified by dual-luciferase reporter assay. Following transfection with miR-195 mimics, the viability of the AN3-CA and Hec1A cells decreased in a time-dependent manner, specifically at 24 h. The wound closure rate and the number of invaded cells in the Mimics group were much lower than those in the Control and Mock groups (P<0.01). miR-195 overexpression significantly upregulated the mRNA and protein levels of tissue inhibitor of metalloproteinase 2 (TIMP-2), while it downregulated the expression levels of matrix metalloproteinase (MMP)-2 and MMP-9. Moreover, the phosphorylation levels of PI3K and AKT were also notably decreased (P<0.01). G protein-coupled estrogen receptor 1 (GPER) was identified as a target of miR-195. On the whole, the findings of this study indicate that the inhibitory effects of miR195 on EC cell migration and invasion are associated with the PI3K/AKT signaling pathway and GPER expression.
Electric field modulation on negative Poisson's ratio of Two-dimensional arsenic and antimony monolayers by first-principles calculation

We have introduced the auxetic effect in MBP-analog Sb for the first time, and achieve NPR modulations on monolayer As and Sb via first-principles calculation. Comparing with As, the monolayer Sb is phonon unstable. By applying an uniaxial strain along each directions, we discovered a zigzag-vertical reversibility on out-of-plane NPR, and the NPR values for monolayer As and Sb are simulated to be -0.125/-0.172 and -0.036/-0.063 by applying strain along zigzag/vertical directions. The NPR values could be significantly manipulated by applying vertical electrical fields, as increased up to 70.3% for monolayer As and decreased up to 55.6% for monolayer Sb. Such intrinsic NPR and electric field modulation could make those monolayers potential applications in auxetic optoelectronic devices, electrodes and sensors, leading to novel multi-functionalities.
Dynamic Approximate Shortest Paths and Beyond: Subquadratic and Worst-Case Update Time

Consider the following distance query for an n-node graph G undergoing edge insertions and deletions: given two sets of nodes I and J, return the distances between every pair of nodes in I × J. %It generalizes several versions of the dynamic shortest paths problem such as Single-Source and All-Pairs Shortest Paths (SSSP and APSP). This query is rather general and captures several versions of the dynamic shortest paths problem. In this paper, we develop an efficient (1 + ε) -approximation algorithm for this query using fast matrix multiplication. Our algorithm leads to answers for some open problems for Single-Source and All-Pairs Shortest Paths (SSSP and APSP), as well as for Diameter, Radius, and Eccentricities. Below are some highlights. Note that all our algorithms guarantee worst-case update time and are randomized (Monte Carlo), but do not need the oblivious adversary assumption. Subquadratic update time for SSSP, Diameter, Centralities, ect. : When we want to maintain distances from a single node explicitly (without queries), a fundamental question is to beat trivially calling Dijkstra's static algorithm after each update, taking Θ(n^2) update time on dense graphs. A better time complexity was not known even with amortization. It was known to be improbable for exact algorithms and for combinatorial any-approximation algorithms to polynomially beat the Ω(n^2) bound (under some conjectures) [Roditty, Zwick, ESA'04; Abboud, V. Williams, FOCS'14]. Our algorithm with I={s} and J=V(G) implies a (1 + ε) -approximation algorithm for this, guaranteeing Õ(n^1.823/ε^2) worst-case update time for directed graphs with positive real weights in [1, W]. ^2 With ideas from [Roditty, V. Williams, STOC'13], we also obtain the first subquadratic worst-case update time for (5/3+ε) -approximating the eccentricities and (1.5 + ε) -approximating the diameter and radius for unweighted graphs (with small additive errors). We also obtain the first subquadratic worst-case update time for (1 + ε) -approximating the closeness centralities for undirected unweighted graphs. Worst-case update time for APSP: When we want to maintain distances between all-pairs of nodes explicitly, the Õ(n^2) amortized update time by Demetrescu and Italiano [STOC'03] already matches the trivial Ω(n^2) lower bound. A fundamental question is whether it can be made worst-case. The state-of-the-art algorithm takes Õ(n^2+2/3) worst-case update time to maintain the distances exactly [Abraham, Chechik, Krinninger, SODA'17; Thorup STOC'05]. When it comes to (1 + ε) approximation, this bound is still higher than calling the Õ(n^ω/ε) -time static algorithm of Zwick [FOCS'98], where ω≈ 2.373. Our algorithm with I=J=V(G) implies nearly tight bounds for this, namely Õ(n^2/ε^1 + ω) for undirected unweighted graphs and Õ(n^2.045/ε^2) for directed graphs with positive real weights. Besides this, we also obtain the first dynamic APSP algorithm with subquadratic update time and sublinear query time.
Multi-agent Interactive Prediction under Challenging Driving Scenarios

In order to drive safely on the road, autonomous vehicles are expected to predict future outcomes of its surrounding environment and react properly. In fact, many researchers have been focused on solving behavioral prediction problems for autonomous vehicles. However, very few of them consider multi-agent prediction under challenging driving scenarios such as urban environments. In this paper, we proposed a prediction method that is able to predict various complicated driving scenarios where heterogeneous road entities, signal lights, and static map information are taken into account. Moreover, the proposed multi-agent interactive prediction (MAIP) system is capable of simultaneously predicting any number of road entities while considering their mutual interactions. A case study of a simulated challenging urban intersection scenario is provided to demonstrate the performance and capability of the proposed prediction system.
Almost Sure Convergence of Randomized Urn Models with Finite Mean.

We consider a randomized urn model containing objects of finitely many colors in this article. The replacement matrices are allowed to be random, subject to the minimum conditions that the mechanism to choose color and the replacement matrix at each step are conditionally independent given the past, as well as, the conditional expectations of the replacement matrices are close to a (possibly random) irreducible (and hence positive recurrent) matrix. We obtain almost sure convergence of the configuration vector, the proportion vector and the count vector under finite first moment condition alone. The convergence is shown to be $L^1$ as well. We show that first moment assumption is sufficient when the replacement matrix sequence is i.i. d. and independent of the past choices of the color. This significantly improves the similar results for urn models obtained by Athreya and Ney (1972), by weakening the moment assumptions on replacement matrices from $L \log_+ L$ to $L^1$. For more general adaptive sequence of replacement matrix, a little more than $L \log_+ L$ condition is required.
PolSAR Image Classification Based on Dilated Convolution and Pixel-Refining Parallel Mapping network in the Complex Domain

Efficient and accurate polarimetric synthetic aperture radar (PolSAR) image classification with a limited number of prior labels is always full of challenges. For general supervised deep learning classification algorithms, the pixel-by-pixel algorithm achieves precise yet inefficient classification with a small number of labeled pixels, whereas the pixel mapping algorithm achieves efficient yet edge-rough classification with more prior labels required. To take efficiency, accuracy and prior labels into account, we propose a novel pixel-refining parallel mapping network in the complex domain named CRPM-Net and the corresponding training algorithm for PolSAR image classification. CRPM-Net consists of two parallel sub-networks: a) A transfer dilated convolution mapping network in the complex domain (C-Dilated CNN) activated by a complex cross-convolution neural network (Cs-CNN), which is aiming at precise localization, high efficiency and the full use of phase information; b) A complex domain encoder-decoder network connected parallelly with C-Dilated CNN, which is to extract more contextual semantic features. Finally, we design a two-step algorithm to train the Cs-CNN and CRPM-Net with a small number of labeled pixels for higher accuracy by refining misclassified labeled pixels. We verify the proposed method on AIRSAR and E-SAR datasets. The experimental results demonstrate that CRPM-Net achieves the best classification results and substantially outperforms some latest state-of-the-art approaches in both efficiency and accuracy for PolSAR image classification. The source code and trained models for CRPM-Net is available at: this https URL.
Understanding and Improving One-shot Neural Architecture Optimization

The ability of accurately ranking candidate architectures is the key to the performance of neural architecture search~(NAS). One-shot NAS is proposed to cut the expense but shows inferior performance against conventional NAS and is not adequately stable. We find that the ranking correlation between architectures under one-shot training and the ones under stand-alone training is poor, which misleads the algorithm to discover better architectures. We conjecture that this is owing to the gaps between one-shot training and stand-alone complete training. In this work, we empirically investigate several main factors that lead to the gaps and so weak ranking correlation. We then propose NAO-V2 to alleviate such gaps where we: (1) Increase the average updates for individual architecture to a relatively adequate extent. (2) Encourage more updates for large and complex architectures than small and simple architectures to balance them by sampling architectures in proportion to their model sizes. (3) Make the one-shot training of the supernet independent at each iteration. Comprehensive experiments verify that our proposed method is effective and robust. It leads to a more stable search that all the top architectures perform well enough compared to baseline methods. The final discovered architecture shows significant improvements against baselines with a test error rate of 2.60% on CIFAR-10 and top-1 accuracy of 74.4% on ImageNet under the mobile setting. Code and model checkpoints are publicly available at this https URL.
Including Dust Coagulation in Hydrodynamic Models of Protoplanetary Disks: Dust Evolution in the Vicinity of a Jupiter-mass Planet

Dust growth is often neglected when building models of protoplanetary disks due to its complexity and computational expense. However, it does play a major role in shaping the evolution of protoplanetary dust and planet formation. In this paper, we present a numerical model coupling 2D hydrodynamic evolution of a protoplanetary disk, including a Jupiter-mass planet, and dust coagulation. This is obtained by including multiple dust fluids in a single grid-based hydrodynamic simulation and solving the Smoluchowski equation for dust coagulation on top of solving for the hydrodynamic evolution. We find that fragmentation of dust aggregates trapped in a pressure bump outside of the planetary gap leads to an enhancement in the density of small grains. We compare the results obtained from the full-coagulation treatment to the commonly used, fixed-dust-size approach and to previously applied, less computationally intensive methods for including dust coagulation. We find that the full-coagulation results cannot be reproduced using the fixed-size treatment, but some can be mimicked using a relatively simple method for estimating the characteristic dust size in every grid cell.
Charge carrier injection electroluminescence with CO functionalized tips on single molecular emitters.

We investigate electroluminescence of single molecular emitters on NaCl on Ag(111) and Au(111) with submolecular resolution in a low-temperature scanning probe microscope with tunneling current, atomic force and light detection capabilities. Role of the tip state is studied in the photon maps of a prototypical emitter, zinc phthalocyanine (ZnPc), using metal and CO-metal tips. CO-functionalization is found to have an impact on the resolution and contrast of the photon maps due to the localized overlap of the p-orbitals on the tip with the molecular orbitals of the emitter. The possibility of using the same CO-functionalized tip for tip-enhanced photon detection and high resolution atomic force is demonstrated. We study the electroluminescence of ZnPc, induced by charge carrier injection at sufficiently high bias voltages. We propose that the distinct level alignment of the ZnPc frontier orbitals with the Au(111) and Ag(111) Fermi levels governs the primary excitation mechanisms as the injection of electrons and holes from the tip into the molecule, respectively. These findings put forward the importance of the tip status in the photon maps and contribute to a better understanding of the photophysics of organic molecules on surfaces.
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous seizures, which affects about one percent of the worlds population. Most of the current seizure detection approaches strongly rely on patient history records and thus fail in the patient-independent situation of detecting the new patients. To overcome such limitation, we propose a robust and explainable epileptic seizure detection model that effectively learns from seizure states while eliminates the inter-patient noises. A complex deep neural network model is proposed to learn the pure seizure-specific representation from the raw non-invasive electroencephalography (EEG) signals through adversarial training. Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure. The proposed approach is evaluated over the Temple University Hospital EEG (TUH EEG) database. The experimental results illustrate that our model outperforms the competitive state-of-the-art baselines with low latency. Moreover, the designed attention mechanism is demonstrated ables to provide fine-grained information for pathological analysis. We propose an effective and efficient patient-independent diagnosis approach of epileptic seizure based on raw EEG signals without manually feature engineering, which is a step toward the development of large-scale deployment for real-life use.
On Communication Complexity of Fixed Point Computation

Brouwer’s fixed point theorem states that any continuous function from a compact convex space to itself has a fixed point. Roughgarden and Weinstein (FOCS 2016) initiated the study of fixed point computation in the two-player communication model, where each player gets a function from [0,1]^n to [0,1]^n, and their goal is to find an approximate fixed point of the composition of the two functions. They left it as an open question to show a lower bound of 2^{\Omega (n)} for the (randomized) communication complexity of this problem, in the range of parameters which make it a total search problem. We answer this question affirmatively. Additionally, we introduce two natural fixed point problems in the two-player communication model. Each player is given a function from [0,1]^n to [0,1]^{n/2}, and their goal is to find an approximate fixed point of the concatenation of the functions. Each player is given a function from [0,1]^n to [0,1]^{n}, and their goal is to find an approximate fixed point of the mean of the functions. We show a randomized communication complexity lower bound of 2^{\Omega (n)} for these problems (for some constant approximation factor). Finally, we initiate the study of finding a panchromatic simplex in a Sperner-coloring of a triangulation (guaranteed by Sperner’s lemma) in the two-player communication model: A triangulation T of the d-simplex is publicly known and one player is given a set S_A\subset T and a coloring function from S_A to \lbrace 0,\ldots ,d/2\rbrace, and the other player is given a set S_B\subset T and a coloring function from S_B to \lbrace d/2+1,\ldots ,d\rbrace, such that S_A\dot{\cup }S_B=T, and their goal is to find a panchromatic simplex. We show a randomized communication complexity lower bound of |T|^{\Omega (1)} for the aforementioned problem as well (when d is large). On the positive side, we show that if d\le 4 then there is a deterministic protocol for the Sperner problem with O((\log |T|)^2) bits of communication.
TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-Based Chatbots

We consider the importance of different utterances in the context for selecting the response usually depends on the current query. In this paper, we propose the model TripleNet to fully model the task with the triple instead of in previous works. The heart of TripleNet is a novel attention mechanism named triple attention to model the relationships within the triple at four levels. The new mechanism updates the representation of each element based on the attention with the other two concurrently and symmetrically.We match the triple centered on the response from char to context level for prediction.Experimental results on two large-scale multi-turn response selection datasets show that the proposed model can significantly outperform the state-of-the-art methods.