Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Culture on Trust in Automation

Trust in automation has become a topic of intensive study since the late 1990s and is of increasing importance with the advent of intelligent interacting systems. While the earliest trust experiments involved human interventions to correct failures/errors in automated control systems, a majority of subsequent studies have investigated information acquisition and analysis decision aiding tasks such as target detection for which automation reliability is more easily manipulated. Despite the high level of international dependence on automation in industry, almost all current studies have employed Western samples primarily from the U.S. The present study addresses these gaps by running a large sample experiment in three (U.S., Taiwan, and Turkey) diverse cultures using a “trust sensitive task” consisting of both automated control and target detection subtasks. This article presents results for the target detection subtask for which reliability and task load were manipulated. The current experiments allow us to determine whether reported effects are universal or specific to Western culture, vary in baseline or magnitude, or differ across cultures. Results generally confirm consistent effects of manipulations across the three cultures as well as cultural differences in initial trust and variation in effects of manipulations consistent with 10 cultural hypotheses based on Hofstede's Cultural Dimensions and Leung and Cohen's theory of Cultural Syndromes. These results provide critical implications and insights for correct trust calibration and to enhance human trust in intelligent automation systems across cultures. Additionally, our results would be useful in designing intelligent systems for users of different cultures. Our article presents the following contributions: First, to the best of our knowledge, this is the first set of studies that deal with cultural factors across all the cultural syndromes identified in the literature by comparing trust in the Honor, Face, Dignity cultures. Second, this is the first set of studies that uses a validated cross-cultural trust measure for measuring trust in automation. Third, our experiments are the first to study the dynamics of trust across cultures.
Mediators of the Effect of Childhood Socioeconomic Status on Late Midlife Cognitive Abilities: A Four Decade Longitudinal Study

Abstract Background and Objectives Childhood socioeconomic status (cSES) is found to predict later-life cognitive abilities, yet the mechanisms underlying these associations remain unclear. The objective of this longitudinal study was to examine the direct and indirect paths through which cSES influences late midlife cognitive outcomes. Research Design and Methods Participants were 1,009 male twins in the Vietnam Era Twin Study of Aging (VETSA). At mean ages 20 and 62, participants completed a standardized test for general cognitive ability (GCA). The age 62 cognitive assessment also included in-person tests of processing speed, episodic memory, abstract reasoning, working memory, verbal fluency, visual-spatial ability, and executive functions. At mean age 56, participants were interviewed regarding their own and their parents’ education and occupation, and completed questionnaires about cognitive leisure activities and sociodemographic information. Multiple mediation analyses were conducted to examine the direct path effects and indirect path effects of cSES through age 20 GCA, adult SES, and cognitive leisure activities on seven cognitive outcomes at age 62, adjusting for age, ethnicity, and non-independence of observations. Results Total (direct plus indirect) effects were significant for all measures with the exception of executive functions. Men from lower cSES backgrounds had poorer cognitive functioning in late midlife. The direct effect of cSES was partially mediated for abstract reasoning, and was fully mediated for the remaining six cognitive outcomes. Total indirect effects accounted for at least half of the total effects in each model, with paths through age 20 GCA explaining most of the total indirect effects. Discussion and Implications cSES predicted cognitive functioning in late middle age Using multiple mediation models, we show that lower cSES predicts poorer cognition in late midlife primarily through young adult cognitive ability and to a lesser extent through SES in adulthood and engagement in cognitively stimulating activities.
Universal Approximation by a Slim Network with Sparse Shortcut Connections

Over recent years, deep learning has become a mainstream method in machine learning. More advanced networks are being actively developed to solve real-world problems in many important areas. Among successful features of network architectures, shortcut connections are well established to take the outputs of earlier layers as the inputs to later layers, and produce excellent results such as in ResNet and DenseNet. Despite the power of shortcuts, there remain important questions on the underlying mechanism and associated functionalities. For example, will adding shortcuts lead to a more compact structure? How to use shortcuts for an optimal efficiency and capacity of the network model? Along this direction, here we demonstrate that given only one neuron in each layer, the shortcuts can be sparsely placed to let the slim network become an universal approximator. Potentially, our theoretically-guaranteed sparse network model can achieve a learning performance comparable to densely-connected networks on well-known benchmarks.
Laparoscopic right hemicolectomy oriented by superior mesenteric artery for right colon cancer: efficacy evaluation with a match-controlled analysis

Background Laparoscopic right hemicolectomy (LRC) with a principle of D3 lymphadenectomy seems to be appropriate in treatment of right-sided colon cancer (RCC). This study aimed to evaluate clinical efficacy of superior mesenteric artery (SMA)-guided LRC (SLRC) for RCC patients. Patients and methods Data for RCC patients with radical resection were retrieved from our database and electronic medical records (January 2010 to December 2014). Patients undergoing SLRC procedure were compared with those undergoing conventional laparoscopic right hemi-colectomy (CLRC), with a match ratio of 1:2 for group balance. Perioperative and long-term outcomes were compared between two groups. Results In sum, 102 matched patients were selected, with a median follow-up of 32 (range, 3–68) months. The mean operative time was significantly reduced in the SLRC group compared to the CLRC group (206.9 vs 240.0 minutes, P=0.007), with increased incidence of postoperative complications observed (14.7% vs 8.8%, P=0.499). Average length of stay after surgery (7.4 vs 8.0 days), estimated blood loss (85.3 vs 105.4 mL), number of harvested (28.4 vs 28.2) and positive (0.6 vs 0.9) lymph nodes, and overall costs ($4826.9 vs $4874.6) were comparable between two groups (P>0.05). The 3-year disease-free survival rate (89.4% vs 92.1%, P=0.840) and overall survival rate (93.0% vs 83.1%, P=0.273) were similar in both groups. Older age (≥65 years, P=0.049) and advanced tumor stage (≥II, P=0.009) were independent risk factors of recurrence. Conclusion The perioperative and oncologic outcomes of SLRC were not superior, but comparable to CLRC. SMA-guided dissection was a feasible surgical approach in treatment of RCC.
Learning to Attend Relevant Regions in Videos from Eye Fixations

Attentively important regions in video frames account for a majority part of the semantics in each frame. This information is helpful in many applications not only for entertainment (such as auto generating commentary and tourist guide) but also for robotic control which holds a larascope supported for laparoscopic surgery. However, it is not always straightforward to define and locate such semantic regions in videos. In this work, we attempt to address the problem of attending relevant regions in videos by leveraging the eye fixations labels with a RNN-based visual attention model. Our experimental results suggest that this approach holds a good potential to learn to attend semantic regions in videos while its performance also heavily relies on the quality of eye fixations labels.
TRPV1 pore turret dictates distinct DkTx and capsaicin gating

Significance The TRPV1 channel integrates noxious stimuli from multiple sources to evoke an appropriate pain response. However, while different stimuli evoke distinct channel activation, the underlying molecular mechanisms remain unclear. Here, we aim at elucidating the structural determinants that regulate the different TRPV1 responses to two toxins: capsaicin and double-knot toxin (DkTx). We found that the channel pore turret domain imposes two opposite effects on the responses to these toxins. While it restricts the ion conductance of the DkTx-evoked current, it stabilizes the open channel state evoked by capsaicin. Together, our results indicate that TRPV1 pore turret dictates different gating mechanisms in response to agonists acting through different binding domains. Many neurotoxins inflict pain by targeting receptors expressed on nociceptors, such as the polymodal cationic channel TRPV1. The tarantula double-knot toxin (DkTx) is a peptide with an atypical bivalent structure, providing it with the unique capability to lock TRPV1 in its open state and evoke an irreversible channel activation. Here, we describe a distinct gating mechanism of DkTx-evoked TRPV1 activation. Interestingly, DkTx evokes significantly smaller TRPV1 macroscopic currents than capsaicin, with a significantly lower unitary conductance. Accordingly, while capsaicin evokes aversive behaviors in TRPV1-transgenic Caenorhabditis elegans, DkTx fails to evoke such response at physiological concentrations. To determine the structural feature(s) responsible for this phenomenon, we engineered and evaluated a series of mutated toxins and TRPV1 channels. We found that elongating the DkTx linker, which connects its two knots, increases channel conductance compared with currents elicited by the native toxin. Importantly, deletion of the TRPV1 pore turret, a stretch of amino acids protruding out of the channel’s outer pore region, is sufficient to produce both full conductance and aversive behaviors in response to DkTx. Interestingly, this deletion decreases the capsaicin-evoked channel activation. Taken together with structure modeling analysis, our results demonstrate that the TRPV1 pore turret restricts DkTx-mediated pore opening, probably through steric hindrance, limiting the current size and mitigating the evoked downstream physiological response. Overall, our findings reveal that DkTx and capsaicin elicit distinct TRPV1 gating mechanisms and subsequent pain responses. Our results also indicate that the TRPV1 pore turret regulates the mechanisms of channel gating and permeation.
Performance of Reed-Solomon Based Quasi-Cyclic LDPC Codes Based on Protograph

In this paper, we exploit construction of Reed-Solomon(RS) based Quasi-Cyclic Low-Density Parity-Check(QC-LDPC) codes using protograph combining two existing QC-LDPC codes construction and experiment the performance of the codes. Two constructions have benefits: One is the construction RS based QC-LDPC codes whose girth is at least 8 and another is protograph based QC-LDPC codes to increase the upper bounds of minimum Hamming distance. We construct the protographs with three strategies and two of them have coding gain in sense of bit error rate compare to existing Reed Solomon based QC-LDPC codes.
Development of the Submental Nasal Appearance Scale for the Assessment of Repaired Unilateral Complete Cleft Lip: A Pilot Study

Objective: To develop the “Submental Nasal Appearance Scale” (SNAS), which is an easy-to-use objectified tool to represent a cleft surgeon’s standard for assessment of the nasal appearance from the submental perspective. Design: Eighty-five photographs of patients with unilateral complete cleft lip and palate were selected and cropped, displaying the submental view. Sixty-one photographs were used to develop 5 sets of reference photographs. Three cleft surgeons graded 24 photographs with these sets and subjectively graded the overall nasal appearance as well. Internal agreement for both methods was calculated, as well as correlation between them. The SNAS was created, by only using the combination of sets that showed the highest reliability and correlation. Setting: Boston Children’s Hospital, Boston, Massachusetts. Patients: Six- to 9-year-old patients with unilateral complete cleft lip and palate. Results: The intrarater and interrater reliability was 0.84 and 0.79, respectively, for the SNAS and 0.76 and 0.62, respectively, for the overall appearance assessment. The correlation was 0.74 between the methods. Conclusions: The SNAS is a reliable tool that reflects a cleft surgeon’s standard and could be used independently or in combination with existing rating scales using the frontal and/or lateral view, for assessment after cleft lip repair.
Long Working Hours and Job Quality in Europe: Gender and Welfare State Differences

Chronic extreme long working hours (LWH) have been found consistently associated with poor health status. However, the evidence for moderately LWH (41–60 h a week) is contradictory. Although poor job quality has been proposed as one of the mechanisms of this relationship, there are almost no studies about LWH and job quality. The objectives of this study were to analyze the association between moderately LWH and job quality in the EU27, as well as to examine differences by welfare regimes and gender. This is a cross-sectional study based on data from the 2010 European Working Conditions Survey. A subsample of employees from the EU27 aged 16–64 years who worked 30–60 h a week was selected (12,574 men and 8787 women). Overall, moderately LWH were not consistently associated with poor job quality except among women from Eastern European countries. Therefore, in the EU27 poor job quality does not seem to explain the relationship between moderately LWH and poor health status. The findings among women from Eastern European countries may be related to their weakened position in the labor market and to their work-family conflict resulting from a process of re-familisation that constrains their choices for a good job.
Optimal Uplink Channel Estimation Algorithm for OFDM Based MmWave Massive MIMO Systems

Practical channel estimation algorithms for millimeter wave massive multiple-input multiple-output (mmWave massive MIMO) systems with huge number of antennas at the base station must achieve high spectrum efficiency. Furthermore, the uplink channel estimation becomes very challenging since the required pilot overhead used for channel estimation and feedback can be prohibitively large. In conventional channel estimation algorithms, the channel model is approximated using a virtual channel model with quantized angles of arrival/departure (AoA/AoD). In this paper, we consider the continually distributed AoA/AoD, we show that the mmWave massive MIMO channels share common cosparsity properties, and we propose a structured analysis compressive sensing (SACS) based algorithm which exploits those common cosparsity properties for channel estimation with low overhead. Simulation results show that our proposal can accurately estimate the channel with low overhead, and is capable of attaining the optimal Cramer-Rao Lower Bound (CRLB).
Robust and Rapid Adaption for Concept Drift in Software System Anomaly Detection

Anomaly detection is critical for web-based software systems. Anecdotal evidence suggests that in these systems, the accuracy of a static anomaly detection method that was previously ensured is bound to degrade over time. It is due to the significant change of data distribution, namely concept drift, which is caused by software change or personal preferences evolving. Even though dozens of anomaly detectors have been proposed over the years in the context of software system, they have not tackled the problem of concept drift. In this paper, we present a framework, StepWise, which can detect concept drift without tuning detection threshold or per-KPI (Key Performance Indicator) model parameters in a large scale KPI streams, take external factors into account to distinguish the concept drift which under operators' expectations, and help any kind of anomaly detection algorithm to handle it rapidly. For the prototype deployed in Sogou, our empirical evaluation shows StepWise improve the average F-score by 206% for many widely-used anomaly detectors over a baseline without any concept drift detection.
Relational Long Short-Term Memory for Video Action Recognition

Spatial and temporal relationships, both short-range and long-range, between objects in videos, are key cues for recognizing actions. It is a challenging problem to model them jointly. In this paper, we first present a new variant of Long Short-Term Memory, namely Relational LSTM, to address the challenge of relation reasoning across space and time between objects. In our Relational LSTM module, we utilize a non-local operation similar in spirit to the recently proposed non-local network to substitute the fully connected operation in the vanilla LSTM. By doing this, our Relational LSTM is capable of capturing long and short-range spatio-temporal relations between objects in videos in a principled way. Then, we propose a two-branch neural architecture consisting of the Relational LSTM module as the non-local branch and a spatio-temporal pooling based local branch. The local branch is utilized for capturing local spatial appearance and/or short-term motion features. The two branches are concatenated to learn video-level features from snippet-level ones which are then used for classification. Experimental results on UCF-101 and HMDB-51 datasets show that our model achieves state-of-the-art results among LSTM-based methods, while obtaining comparable performance with other state-of-the-art methods (which use not directly comparable schema). Further, on the more complex large-scale Charades dataset, we obtain a large 3.2% gain over state-of-the-art methods, verifying the effectiveness of our method in complex understanding.
Effect of increased inspiratory muscle work on blood flow to inactive and active limbs during submaximal dynamic exercise

What is the central question of this study? Increased respiratory muscle activation is associated with neural and cardiovascular consequences via the respiratory muscle metaboreflex. Does increased sympathetic vasoconstriction originating from the respiratory musculature elicit a reduction in blood flow to an inactive limb in order to maintain blood flow to an active limb? What is the main finding and its importance? Arm blood flow was reduced whereas leg blood flow was preserved during mild leg exercise with inspiratory resistance. Blood flow to the active limb is maintained via sympathetic control of blood flow redistribution when the respiratory muscle‐induced metaboreflex is activated.
Molecular characteristics and comparative genomics analysis of a clinical Enterococcus casseliflavus with a resistance plasmid

Purpose The aim of this work was to investigate the molecular characterization of a clinical Enterococcus casseliflavus strain with a resistance plasmid. Materials and methods En. casseliflavus EC369 was isolated from a patient in a hospital in southern China. The minimum inhibitory concentration was found by means of the agar dilution method to determine the antimicrobial susceptibilities of the strains. Whole-genome sequencing and comparative genomics analysis were performed to analyze the mechanism of antibiotic resistance and the horizontal gene transfer of the resistance gene-related mobile genetic elements. Results En. casseliflavus EC369 showed resistance to erythromycin, kanamycin, and streptomycin, but was susceptible to vancomycin, ampicillin, and streptothricin and other antimicrobials. There were six resistance genes (aph3′, ant6, bla, sat4, and two ermBs) carried by a transposon identified on the plasmid pEC369 and a complete resistance gene cluster of vancomycin and a tet (M) gene encoded on the chromosome. This is the first complete plasmid sequence reported in clinically isolated En. casseliflavus. The plasmid with the greatest sequence identity with pEC369 was the plasmid of Enterococcus sp. FDAARGOS_375, followed by the plasmids of Enterococcus faecium strains F12085 and pRE25, whereas the sequence with the greatest identity to the resistance genes carrying a transposon of pEC369 was on the chromosome of Staphylococcus aureus strain GD1677. Conclusion The resistance profiles of En. casseliflavus EC369 might contribute to the resistance genes encoded on the plasmid. The fact that the most similar sequence to the transposon carrying resistance genes of pEC369 was encoded in the chromosome of a S. aureus strain provides insights into the mechanism of dissemination of multidrug resistance between bacteria of different species or genera through horizontal gene transfer.
Robust Website Fingerprinting Through the Cache Occupancy Channel

Website fingerprinting attacks, which use statistical analysis on network traffic to compromise user privacy, have been shown to be effective even if the traffic is sent over anonymity-preserving networks such as Tor. The classical attack model used to evaluate website fingerprinting attacks assumes an on-path adversary, who can observe all traffic traveling between the user's computer and the Tor network. In this work we investigate these attacks under a different attack model, in which the adversary is capable of running a small amount of unprivileged code on the target user's computer. Under this model, the attacker can mount cache side-channel attacks, which exploit the effects of contention on the CPU's cache, to identify the website being browsed. In an important special case of this attack model, a JavaScript attack is launched when the target user visits a website controlled by the attacker. The effectiveness of this attack scenario has never been systematically analyzed, especially in the open-world model which assumes that the user is visiting a mix of both sensitive and non-sensitive sites. In this work we show that cache website fingerprinting attacks in JavaScript are highly feasible, even when they are run from highly restrictive environments, such as the Tor Browser. Specifically, we use machine learning techniques to classify traces of cache activity. Unlike prior works, which try to identify cache conflicts, our work measures the overall occupancy of the last-level cache. We show that our approach achieves high classification accuracy in both the open-world and the closed-world models. We further show that our techniques are resilient both to network-based defenses and to side-channel countermeasures introduced to modern browsers as a response to the Spectre attack.
Tail Latency Optimized Resource Allocation in Fogbased 5G Networks

Meeting the stringent latency requirements for delay-sensitive scenarios is a challenge in 5G Networks. Fog computing can reduce the end to end latency considerably by extending data processing capabilities to the network edge. However, the “soft real-time” characteristic of fog-based 5G networks may intensify the effect of long-tail on latency-critical scenarios, which are very sensitive to tail latency. To alleviate the long-tail latency, an online resource allocation algorithm with low complexity is proposed in this paper. With this algorithm, latency based dynamic resource allocation can be achieved through the cooperation of monitor and orchestrator of fog controllers. The simulation results show the effectiveness of this algorithm on latency optimization, tail latency reduction and resource utilization improvement.