Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on Guide Sign Validity in Driving Simulator

The role of guide sign to inform road user about the information of network is important. How to design and locate guide sign to increase traffic operation efficiency is a key point for traffic engineers. Driving simulator is useful devised to study guide sign in the process and system control. For the purpose of studying guide signs using the tool of driving simulator, guide sign’s validity in driving simulator was studied. Results of this experiment are the foundation of further study on guide sign. Simulator calibration procedure for guide sign was set up in this study. Legibility distance as measure of performance was used to evaluate the validity of guide sign in driving simulator. Thirty two participants were recruited. Results indicated legibility distance and speed were inversely related with the method of data mining. Legibility distance and text height of guide sign were positive related. When speed is 20km/h, 30km/h, 40km/h, magnifying power of text height is 4.3, 4.1, 3.8, while guide signs are absolute validity in driving simulator.
Fast inference of deep neural networks in FPGAs for particle physics

Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power FPGA (Field Programmable Gate Array) hardware has only just begun. FPGA-based trigger and data acquisition systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson. While we focus on a specific example, the lessons are far-reaching. A companion compiler package for this work is developed based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to identify the problems in particle physics that would benefit from performing neural network inference with FPGAs. For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.
Variable-Length Intrinsic Randomness Allowing Positive Value of the Average Variational Distance

This paper considers the problem of variable-length intrinsic randomness. We propose the average variational distance as the performance criterion from the viewpoint of a dual relationship with the problem formulation of variable-length resolvability. Previous study has derived the general formula of the ϵ-variable-length resolvability. We derive the general formula of the ϵ-variable-length intrinsic randomness. Namely, we characterize the supremum of the mean length under the constraint that the value of the average variational distance is smaller than or equal to a constant ϵ. Our result clarifies a dual relationship between the general formula of ϵ-variable-length resolvability and that of ϵ-variable-length intrinsic randomness. We also derive a lower bound of the quantity characterizing our general formula.
Economical and Balanced Energy Usage in the Smart Home Infrastructure: A Tutorial and New Results

The smart home infrastructure features the automatic control of various household appliances in the advanced metering infrastructure, which enables the connection of individual smart home systems to a smart grid. In such an infrastructure, each smart meter receives electricity price from utilities and uses a smart controller to schedule the household appliances accordingly. This helps shift the heavy energy load from peak hours to nonpeak hours. Such an architecture significantly improves the reliability of the power grid through reducing the peak energy usage, while benefiting the customers through reducing electricity bills. This paper presents a tutorial on the development of the smart controller to schedule household appliances, which is also known as smart home scheduling. For each individual user, a dynamic programming-based algorithm that schedules household appliances with discrete power levels is introduced. Based on it, a game theoretic framework is designed for multi-user smart home scheduling to mitigate the accumulated energy usage during the peak hours. The simulation results demonstrate that it can reduce the electricity bill by 30.11% while still improving peak-to-average ratio (PAR) in the power grid. Furthermore, the deployment of smart home scheduling techniques in a big city is discussed. In such a context, the parallel computation is explored to tackle the large computational complexity, a machine assignment approximation algorithm is proposed to accelerate the smart home scheduling, and a new hieratical framework is proposed to reduce the communication overhead. The simulation results on large test cases demonstrate that the city level hierarchical smart home scheduling can achieve the bill reduction of 43.04% and the PAR reduction of 47.50% on average.
MxiN Differentially Regulates Monomeric and Oligomeric Species of the Shigella Type Three Secretion System ATPase Spa47

Shigella rely entirely on the action of a single type three secretion system (T3SS) to support cellular invasion of colonic epithelial cells and to circumvent host immune responses. The ATPase Spa47 resides at the base of the Shigella needle‐like type three secretion apparatus (T3SA), supporting protein secretion through the apparatus and providing a likely means for native virulence regulation by Shigella and a much needed target for non‐antibiotic therapeutics to treat Shigella infections. Here, we show that MxiN is a differential regulator of Spa47 and that its regulatory impact is determined by the oligomeric state of the Spa47 ATPase, with which it interacts. Both in vitro and in vivo characterization find that MxiN interaction with Spa47 requires the six N‐terminal residues of Spa47 that are also necessary for stable Spa47 oligomer formation and activation. This interaction with MxiN negatively influences the activity of Spa47 oligomers while upregulating the ATPase activity of monomeric Spa47. Detailed kinetic analyses of monomeric and oligomeric Spa47 in the presence and absence of MxiN uncover additional mechanistic insights into the regulation of Spa47 by MxiN, suggesting that the MxiN/Spa47 species resulting from interaction with monomeric and oligomeric Spa47 are functionally distinct and that both could be involved in Shigella T3SS regulation. Uncovering Spa47 regulation by MxiN addresses an important gap in the current understanding of how Shigella controls T3SA activity and provides the first description of differential T3SS ATPase regulation by a native T3SS protein.
Rapid Response to Intensive Treatment for Bulimia Nervosa and Purging Disorder: A Randomized Controlled Trial of a CBT Intervention to Facilitate Early Behavior Change

Objective: Rapid response to cognitive behavior therapy (CBT) for eating disorders (i.e., rapid and substantial change to key eating disorder behaviors in the initial weeks of treatment) robustly predicts good outcome at end-of-treatment and in follow up. The objective of this study was to determine whether rapid response to day hospital (DH) eating disorder treatment could be facilitated using a brief adjunctive CBT intervention focused on early change. Method: 44 women (average age 27.3 [8.4]; 75% White, 6.3% Black, 6.9% Asian) were randomly assigned to 1 of 2 4-session adjunctive interventions: CBT focused on early change, or motivational interviewing (MI). DH was administered as usual. Outcomes included binge/purge frequency, Eating Disorder Examination-Questionnaire and Difficulties in Emotion Regulation Scale. Intent-to-treat analyses were used. Results: The CBT group had a higher rate of rapid response (95.7%) compared to MI (71.4%; p = .04, V = .33). Those who received CBT also had fewer binge/purge episodes (p = .02) in the first 4 weeks of DH. By end-of-DH, CBT participants made greater improvements on overvaluation of weight and shape (p = .008), and emotion regulation (ps < .008). Across conditions, there were no significant baseline differences between rapid and nonrapid responders (ps > .05). Conclusions: The results of this study demonstrate that rapid response can be clinically facilitated using a CBT intervention that explicitly encourages early change. This provides the foundation for future research investigating whether enhancing rates of rapid response using such an intervention results in improved longer term outcomes.
Searching for Category-Consistent Features

This article introduces a generative model of category representation that uses computer vision methods to extract category-consistent features (CCFs) directly from images of category exemplars. The model was trained on 4,800 images of common objects, and CCFs were obtained for 68 categories spanning subordinate, basic, and superordinate levels in a category hierarchy. When participants searched for these same categories, targets cued at the subordinate level were preferentially fixated, but fixated targets were verified faster when they followed a basic-level cue. The subordinate-level advantage in guidance is explained by the number of target-category CCFs, a measure of category specificity that decreases with movement up the category hierarchy. The basic-level advantage in verification is explained by multiplying the number of CCFs by sibling distance, a measure of category distinctiveness. With this model, the visual representations of real-world object categories, each learned from the vast numbers of image exemplars accumulated throughout everyday experience, can finally be studied.
Drug Use and Incarceration among Rural Appalachian Women: Findings From a Jail Sample

ABSTRACT Background/Objective: The purpose of this paper is to examine drug use and incarceration history among rural Appalachian women. Methods: This study involved random selection, screening, and interviews with rural women from local jails in Appalachia. Results: Of the women randomly selected and screened, 97% met criteria for substance use intervention. Significant factors associated with incarceration history included age, education, custody status, and mental health. A significant interaction was observed between male sex partners and drug use on incarceration history. Conclusions: Study findings suggest that the drug/crime relationship among rural Appalachian women is associated with their high-risk home environment, partner relationships, and mental health. Specifically, in addition to drug use, factors such as family and child relationships, anxiety, victimization, and relationships with partners should also be considered in the trajectory of criminal careers among rural Appalachian women.
Gene Expression Profiling of a Mouse Model of Pancreatic Islet Dysmorphogenesis

BACKGROUND In the past decade, several transcription factors critical for pancreas organogenesis have been identified. Despite this success, many of the factors necessary for proper islet morphogenesis and function remain uncharacterized. Previous studies have shown that transgenic over-expression of the transcription factor Hnf6 specifically in the pancreatic endocrine cell lineage resulted in disruptions in islet morphogenesis, including dysfunctional endocrine cell sorting, increased individual islet size, increased number of peripheral endocrine cell types, and failure of islets to migrate away from the ductal epithelium. The mechanisms whereby maintained Hnf6 causes defects in islet morphogenesis have yet to be elucidated. METHODOLOGY/PRINCIPAL FINDINGS We exploited the dysmorphic islets in Hnf6 transgenic animals as a tool to identify factors important for islet morphogenesis. Genome-wide microarray analysis was used to identify differences in the gene expression profiles of late gestation and early postnatal total pancreas tissue from wild type and Hnf6 transgenic animals. Here we report the identification of genes with an altered expression in Hnf6 transgenic animals and highlight factors with potential importance in islet morphogenesis. Importantly, gene products involved in cell adhesion, cell migration, ECM remodeling and proliferation were found to be altered in Hnf6 transgenic pancreata, revealing specific candidates that can now be analyzed directly for their role in these processes during islet development. CONCLUSIONS/SIGNIFICANCE This study provides a unique dataset that can act as a starting point for other investigators to explore the role of the identified genes in pancreatogenesis, islet morphogenesis and mature beta cell function.
Capturing the Interpersonal Implications of Evolved Preferences? Frequency of Sex Shapes Automatic, but Not Explicit, Partner Evaluations

A strong predisposition to engage in sexual intercourse likely evolved in humans because sex is crucial to reproduction. Given that meeting interpersonal preferences tends to promote positive relationship evaluations, sex within a relationship should be positively associated with relationship satisfaction. Nevertheless, prior research has been inconclusive in demonstrating such a link, with longitudinal and experimental studies showing no association between sexual frequency and relationship satisfaction. Crucially, though, all prior research has utilized explicit reports of satisfaction, which reflect deliberative processes that may override the more automatic implications of phylogenetically older evolved preferences. Accordingly, capturing the implications of sexual frequency for relationship evaluations may require implicit measurements that bypass deliberative reasoning. Consistent with this idea, one cross-sectional and one 3-year study of newlywed couples revealed a positive association between sexual frequency and automatic partner evaluations but not explicit satisfaction. These findings highlight the importance of automatic measurements to understanding interpersonal relationships.
Variances in family carers' quality of life based on selected relationship and caregiving indicators: A quantitative secondary analysis

AIMS AND OBJECTIVES To determine subgroups of family carers based on family relational and caregiving variables and to explore group differences in relation to selected carer outcomes. BACKGROUND Family caregiving in later life holds a myriad of positive and negative outcomes for family members' well-being. However, factors that constitute family carers' experience and explain variances are less well understood. DESIGN A secondary data analysis using cross-sectional data from a controlled randomised trial with community-dwelling people 80 years or older and their families. METHODS A total of 277 paired data sets of older persons and their family carers were included into the analysis. Data were collected via mailed questionnaires and a geriatric nursing assessment. A two-step cluster analysis was performed to determine subgroups. To discern group differences, appropriate tests for differences with Bonferroni correction were used. RESULTS Two family carer groups were identified. The low-intensity caregiver group (57% of carers) reported high relationship quality and self-perceived ease of caregiving. In contrast, the high-intensity caregiver group (43% of carers) experienced significantly lower relationship quality, felt less prepared and appraised caregiving as more difficult, time intensive and burdensome. The latter cared for older, frailer and more dependent octogenarians and had significantly lower levels of quality of life and self-perceived health compared to the low-intensity caregiver group. CONCLUSIONS A combination of family relational and caregiving variables differentiates those at risk for adverse outcomes. Family carers of frailer older people tend to experience higher strain, lower relationship quality and ability to work together as a family. IMPLICATIONS FOR PRACTICE Nurses should explicitly assess family carer needs, in particular when older persons are frail. Family carer support interventions should address caregiving preparedness, demand and burden, as well as concerns situated in the relationship.
Defining the Most Appropriate Primary End Point in Phase 2 Trials of Immune Checkpoint Inhibitors for Advanced Solid Cancers: A Systematic Review and Meta-analysis

Importance Checkpoint inhibitors have a unique mechanism of action that differs from chemotherapy or targeted therapies. The validity of objective response rate (ORR) as a surrogate for progression-free survival (PFS) and overall survival (OS) in checkpoint-inhibitor trials is uncertain. Objective To determine the types of primary end points used in phase 2 checkpoint-inhibitor trials, and to assess the strength of associations for ORR with PFS and OS. Data Sources Trials listed in electronic databases from 2000 to 2017 (PREMEDLINE, MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials). Study Selection Advanced solid cancers in phase 2 and phase 3 trials. Data Extraction and Synthesis Correlations between ORR odds ratios and hazard ratios (HRs) for PFS and OS were examined for randomized comparisons. Within checkpoint-inhibitor treatment arms, correlations for ORR with 6-month PFS and 12-month OS rates were examined. All analyses were weighted by trial size. Multivariable models to predict 6-month PFS and 12-month OS rates from ORR were developed and their performance validated in an independent sample of trials. Main Outcomes and Measures Correlation coefficient (r) of ORR with PFS and OS. Results Of 87 phase 2 trials identified, ORR was the most common (52 [60%]) primary end point. Twenty randomized clinical trials with 25 treatment comparisons were identified. Checkpoint-inhibitor therapy was associated with pooled ORR of 24% (95% CI, 18%-31%). For randomized comparisons, r between ORR odds ratio and PFS HR was 0.63 (95% CI, 0.35-0.89), ORR odds ratio and OS HR was 0.57 (95% CI, 0.23-0.89), and between PFS HR and OS HR was 0.42 (95% CI, 0.04-0.81). Within the checkpoint-inhibitor arms, r correlation coefficients between ORR with 6-month PFS, ORR with 12-month OS, and 6-month PFS with 12-month OS were 0.37 (95% CI, −0.06 to 0.95), 0.08 (95% CI, −0.17 to 0.70), and 0.74 (95% CI, 0.57-0.92), respectively. In validation, when 6-month PFS was used to predict 12-month OS, there was a good calibration between actual and predicted 12-month OS. When ORR was used to predict 6-month PFS and 12-month OS rates, respectively, the actual vs predicted rates calibrated poorly. Conclusions and Relevance In checkpoint-inhibitor trials, ORR correlated poorly with OS. For future phase 2 studies, 6-month PFS rate is recommended as an end point.
Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference

We study parameter estimation and asymptotic inference for sparse nonlinear regression. More specifically, we assume the data are given by y = f(x > ) + , where f is nonlinear. To recover , we propose an ‘1-regularized least-squares estimator. Unlike classical linear regression, the corresponding optimization problem is nonconvex because of the nonlinearity of f. In spite of the nonconvexity, we prove that under mild conditions, every stationary point of the objective enjoys an optimal statistical rate of convergence. In addition, we provide an ecient algorithm that provably converges to a stationary point. We also access the uncertainty of the obtained estimator. Specifically, based on any stationary point of the objective, we construct valid hypothesis tests and confidence intervals for the low dimensional components of the high-dimensional parameter . Detailed numerical results are provided to back up our theory.
Stability Analysis and Application for Delayed Neural Networks Driven by Fractional Brownian Noise

This paper deals with two types of the stability problem for the delayed neural networks driven by fractional Brownian noise (FBN). The existence and the uniqueness of the solution to the main system with respect to FBN are proved via fixed point theory. Based on Hilbert–Schmidt operator theory and analytic semigroup principle, the mild solution of the stochastic neural networks is obtained. By applying the stochastic analytic technique and some well-known inequalities, the asymptotic stability criteria and the exponential stability condition are established. Both numerical example and practical application for synchronization control of multiagent system are provided to illustrate the effectiveness and potential of the proposed techniques.
The Influence of Glycosylation of Natural and Synthetic Prenylated Flavonoids on Binding to Human Serum Albumin and Inhibition of Cyclooxygenases COX-1 and COX-2

The synthesis of different classes of prenylated aglycones (α,β-dihydroxanthohumol (2) and (Z)-6,4’-dihydroxy-4-methoxy-7-prenylaurone (3)) was performed in one step reactions from xanthohumol (1)—major prenylated chalcone naturally occurring in hops. Obtained flavonoids (2–3) and xanthohumol (1) were used as substrates for regioselective fungal glycosylation catalyzed by two Absidia species and Beauveria bassiana. As a result six glycosides (4–9) were formed, of which four glycosides (6–9) have not been published so far. The influence of flavonoid skeleton and the presence of glucopyranose and 4-O-methylglucopyranose moiety in flavonoid molecule on binding to main protein in plasma, human serum albumin (HSA), and inhibition of cyclooxygenases COX-1 and COX-2 were investigated. Results showed that chalcone (1) had the highest binding affinity to HSA (8.624 × 104 M−1) of all tested compounds. It has also exhibited the highest inhibition of cyclooxygenases activity, and it was a two-fold stronger inhibitor than α,β-dihydrochalcone (2) and aurone (3). The presence of sugar moiety in flavonoid molecule caused the loss of HSA binding activity as well as the decrease in inhibition of cyclooxygenases activity.
Lucidone suppresses dengue viral replication through the induction of heme oxygenase-1

ABSTRACT Dengue virus (DENV) infection causes life-threatening diseases such as dengue hemorrhagic fever and dengue shock syndrome. Currently, there is no effective therapeutic agent or vaccine against DENV infection; hence, there is an urgent need to discover anti-DENV agents. The potential therapeutic efficacy of lucidone was first evaluated in vivo using a DENV-infected Institute of Cancer Research (ICR) suckling mouse model by monitoring body weight, clinical score, survival rate, and viral titer. We found that lucidone effectively protected mice from DENV infection by sustaining survival rate and reducing viral titers in DENV-infected ICR suckling mice. Then, the anti-DENV activity of lucidone was confirmed by western blotting and quantitative-reverse-transcription-polymerase chain reaction analysis, with an EC50 value of 25 ± 3 μM. Lucidone significantly induced heme oxygenase-1 (HO-1) production against DENV replication by inhibiting DENV NS2B/3 protease activity to induce the DENV-suppressed antiviral interferon response. The inhibitory effect of lucidone on DENV replication was attenuated by silencing of HO-1 gene expression or blocking HO-1 activity. In addition, lucidone-stimulated nuclear factor erythroid 2-related factor 2 (Nrf2), which is involved in transactivation of HO-1 expression for its anti-DENV activity. Taken together, the mechanistic investigations revealed that lucidone exhibits significant anti-DENV activity in in vivo and in vitro by inducing Nrf2-mediated HO-1 expression, leading to blockage of viral protease activity to induce the anti-viral interferon (IFN) response. These results suggest that lucidone is a promising candidate for drug development.