Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Sexual Assault Resistance, Bystander, and Men’s Social Norms Strategies to Prevent Sexual Violence on College Campuses: A Call to Action

Sexual assault prevention on college campuses often includes programming directed at men, women, and all students as potential bystanders. Problematically, specific types of sexual assault prevention are often implemented on campuses in isolation, and sexual assault risk reduction and resistance education programs for women are rarely integrated with other approaches. With increasing focus on the problem of sexual assault on college campuses, it is timely to envision a comprehensive and interconnected prevention approach. Implementing comprehensive prevention packages that draw upon the strengths of existing approaches is necessary to move toward the common goal of making college campuses safer for all students. Toward this goal, this commentary unpacks the models and mechanisms on which current college sexual assault prevention strategies are based with the goal of examining the ways that they can better intersect. The authors conclude with suggestions for envisioning a more synthesized approach to campus sexual assault prevention, which includes integrated administration of programs for women, men, and all students as potential bystanders on college campuses.
The Pain Divide: a cross-sectional analysis of chronic pain prevalence, pain intensity and opioid utilisation in England

Objectives Our central research question was, in England, are geographical inequalities in opioid use driven by health need (pain)? To answer this question, our study examined: (1) if there are regional inequalities in rates of chronic pain prevalence, pain intensity and opioid utilisation in England; (2) if opioid use and chronic pain are associated after adjusting for individual-level and area-level confounders. Design Cross-sectional study design using data from the Health Survey for England 2011. Setting England. Primary and secondary outcome measures Chronic pain prevalence, pain intensity and opioid utilisation. Participants Participant data relating to chronic pain prevalence, pain intensity and opioid usage data were obtained at local authority level from the Health Survey for England 2011; in total, 5711 respondents were included in our analysis. Methods Regional and local authority data were mapped, and a generalised linear model was then used to explore the relationships between the data. The model was adjusted to account for area-level and individual-level variables. Results There were geographical variations in chronic pain prevalence, pain intensity and opioid utilisation across the English regions—with evidence of a ‘pain divide’ between the North and the South, whereby people in the North of England more likely to have ‘severely limiting’ or ‘moderately limiting’ chronic pain. The intensity of chronic pain was significantly and positively associated with the use of opioid analgesics. Conclusions There are geographical differences in chronic pain prevalence, pain intensity and opioid utilisation across England—with evidence of a ‘pain divide’. Given the public health concerns associated with the long-term use of opioid analgesics—and their questionable activity in the management of chronic pain—more guidance is needed to support prescribers in the management of chronic pain, so the initiation of opioids can be avoided.
All-Neural Multi-Channel Speech Enhancement

This study proposes a novel all-neural approach for multi-channel speech enhancement, where robust speaker localization, acoustic beamforming, post-filtering and spatial filtering are all done using deep learning based time-frequency (T-F) masking. Our system first performs monaural speech enhancement on each microphone signal to obtain the estimated ideal ratio masks for beamforming and robust time delay of arrival (TDOA) estimation. Then with the estimated TDOA, directional features indicating whether each T-F unit is dominated by the signal coming from the estimated target direction are computed. Next, the directional features are combined with the spectral features extracted from the beamformed signal to achieve further enhancement. Experiments on a two-microphone setup in reverberant environments with strong diffuse babble noise demonstrate the effectiveness of the proposed approach for multi-channel speech enhancement.
Powerful or Powerless? Children’s, Parents’, and Occupational Therapists’ Perceptions of Powered Mobility

Abstract Aim: In Québec, Canada, the prevalence of children using powered mobility (PM) is not reflective of evidence supporting its use and for achieving developmental milestones. The aim of this study was to explore the perceptions of four key stakeholder groups in a metropolitan area regarding daily use, barriers, facilitators, and clinical practice associated with use of PM. Methods: Using convenience sampling, semi-structured qualitative interviews were conducted with children (n = 6), parents (n = 2), rehabilitation center occupational therapists (OTs; n = 4), and special needs school-based OTs (n = 6). Drawings were used as a supplemental data collection strategy with children. Interviews were audio recorded and transcribed verbatim to conduct thematic analysis. Results: Three overarching themes were identified: (1) “A sense of liberty, except…”, highlighting environmental obstacles reducing social participation; (2) “A necessity, for better or for worse,” covering benefits and drawbacks of PM; and (3) “First choice versus last resort,” raising clinical differences related to provision, assessment, and training. Conclusions: Stakeholders’ perceptions illustrated benefits of PM, yet use is contingent on the physical, institutional, and societal environments, leading stakeholders to feel both powerful and powerless as users, parents, or clinicians.
Sub-5 nm monolayer black phosphorene tunneling transistors

The successful fabrication of sub-5 nm 2D MoS2 field-effect transistors (FETs) announces the approaching post-silicon era. It is possible for tunneling field-effect transistors (TFETs) based on monolayer black phosphorene (ML BP) to work well in the sub-5 nm region because of its moderate direct band gap, anisotropic electronic properties and high carrier mobility. We simulate the device performance limit of the ML BP TFETs at the sub-5 nm scale using ab initio quantum transport calculations. We predict that the on-state currents (Ion) of the sub-5 nm ML BP TFETs will exceed those of the ML WTe2 TFETs, which possess the highest Ion among the transition-metal dichalcogenide family. In particular, the Ion of the ML BP TFETs can fulfill the 2028 requirements of the international technology roadmap for semiconductors (ITRS) for the high-performance (HP) devices until the gate length is scaled down to 4 nm, while the delay times and power dissipations always surpass the 2028 requirements of the ITRS HP devices significantly in the whole sub-5 nm region.
Filtering and Interpolation of Inaccurate and Incomplete Depth Maps

Local algorithms for stereo fail to match accurately points in areas not discriminative enough, mainly texture-less regions. We propose a method for filtering incorrect depth estimates and filling in those areas which have not been matched. The proposed model combines total variation regularization with a non local term taking advantage of image self similarity. The method can be easily generalized to deal with other tasks, as for example stereo upsampling. The method is compared with state-of-the-art stereo methods, showing competitive results on the Middlebury stereo database.
Integrating Recurrence Dynamics for Speech Emotion Recognition

We investigate the performance of features that can capture nonlinear recurrence dynamics embedded in the speech signal for the task of Speech Emotion Recognition (SER). Reconstruction of the phase space of each speech frame and the computation of its respective Recurrence Plot (RP) reveals complex structures which can be measured by performing Recurrence Quantification Analysis (RQA). These measures are aggregated by using statistical functionals over segment and utterance periods. We report SER results for the proposed feature set on three databases using different classification methods. When fusing the proposed features with traditional feature sets, we show an improvement in unweighted accuracy of up to 5.7% and 10.7% on Speaker-Dependent (SD) and Speaker-Independent (SI) SER tasks, respectively, over the baseline. Following a segment-based approach we demonstrate state-of-the-art performance on IEMOCAP using a Bidirectional Recurrent Neural Network.
An Automated Assistant for Medical Scribes

Healthcare professionals spend a signiﬁcant amount of their time on administrative tasks rather than direct patient care. One effective way to alleviate some of this burden is to employ a medical scribe, who charts patient-physician encounters in real time. We present a complete implementation of an automated medical scribe assistant, which listens to the encounter and produces a draft of report text and the information used to create it, which can dramatically streamline the scribe’s task. This system is, to our knowledge, the ﬁrst automated scribe ever presented, and relies on multiple speech and language technologies, including speaker diarization, medical speech recognition, knowledge extraction, and natural language generation.
Accelerating Proposal Generation Network for Fast Face Detection on Mobile Devices

Face detection is a widely studied problem over the past few decades. Recently, significant improvements have been achieved via the deep neural network, however, it is still challenging to directly apply these techniques to mobile devices for its limited computational power and memory. In this work, we present a proposal generation acceleration framework for real-time face detection. More specifically, we adopt a popular cascaded convolutional neural network (CNN) as the basis, then apply our acceleration approach on the basic framework to speed up the model inference time. We are motivated by the observation that the computation bottleneck of this framework arises from the proposal generation stage, where each level of the dense image pyramid has to go through the network. In this work, we reduce the number of image pyramid levels by utilizing both global and local facial characteristics (i.e., global face and facial parts). Experimental results on public benchmarks WIDER-face and FDDB demonstrate the satisfactory performance and faster speed compared to the state-of-the-arts.
Voice Comparison and Rhythm: Behavioral Differences between Target and Non-target Comparisons

It is common to see voice recordings being presented as a forensic trace in court. Generally, a forensic expert is asked to analyze both suspect and criminal’s voice samples in order to indicate whether the evidence supports the prosecution (same-speaker) or defence (different-speakers) hypotheses. This process is known as Forensic Voice Comparison (FVC). Since the emergence of the DNA typing model, the likelihood-ratio (LR) framework has become the golden standard in forensic sciences. The LR not only supports one of the hypotheses but also quantiﬁes the strength of its support. However, the LR accepts some practical limitations due to its estimation process itself. It is particularly true when Automatic Speaker Recognition (ASpR) systems are considered as they are outputting a score in all situ-ations regardless of the case speciﬁc conditions. Indeed, several factors are not taken into account by the estimation process like the quality and quantity of information in both voice recordings, their phonological content or also the speakers intrinsic characteristics. In our recent study, we showed the importance of the phonemic content and we highlighted interesting differences between inter-speakers effects and intra-speaker’s ones. In this article, we wish to take our previous analysis a step far-ther and investigate the impact of rhythm variation separately on target and non-target trials.
Postpartum psychological distress after emergency team response during childbirth

Abstract Purpose: To evaluate the feasibility and acceptability of a protocol determining the relationship between emergency team response (ETR) during childbirth and acute stress disorder (ASD) and post-traumatic stress disorder (PTSD) symptoms. Methods: In a prospective, observational, cohort design, women experiencing ETR during childbirth were approached and recruited on postpartum day-1 and followed for six weeks. Demographics, obstetric and birth characteristics, ASD scores and PTSD scores (by Impact of Events Scale, IES and PCL-civilian) were recorded. Recruitment and retention rates were recorded, and scores were compared to women who did not experience ETR. Results: Three hundred sixty-nine were approached and 249 were enrolled (67.5% recruitment rate). One hundred twenty-five completed all procedures (50.2% retention). Twenty experienced ETR (3.5% event rate), 12 enrolled (60.0% recruitment rate) and 8 completed the study (66.7% retention). The ETR group had higher PCL and IES scores (PCL: ETR median 12, non-ETR median 2, p = .08; IES: ETR median 22.5, non-ETR median 20, p = .08). ASD scores were similar between groups. Conclusions: Methodology investigating the link between ETR and postpartum psychological distress is feasible and acceptable. A relationship between ETR and PTSD symptoms appears to exist, with ETR being associated with higher PTSD scores compared to non-ETR childbirths. Methods that incorporate awareness of the unique concerns of vulnerable populations are needed.
Keep Rollin’—Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots

We show dynamic locomotion strategies for wheeled quadrupedal robots that combine the advantages of both walking and driving. The developed optimization framework tightly integrates the additional degrees of freedom introduced by the wheels. Our approach relies on a zero-moment point-based motion optimization which continuously updates reference trajectories. The reference motions are tracked by a hierarchical whole-body controller which computes optimal generalized accelerations and contact forces by solving a sequence of prioritized tasks including the nonholonomic rolling constraints. Our approach has been tested on ANYmal, a quadrupedal robot that is fully torque-controlled including the nonsteerable wheels attached to its legs. We conducted experiments on flat and inclined terrains as well as over steps, whereby we show that integrating the wheels into the motion control and planning framework results in intuitive motion trajectories, which enable more robust and dynamic locomotion compared to other wheeled-legged robots. Moreover, with a speed of 4 m/s and a reduction of the cost of transport by 83%, we prove the superiority of wheeled-legged robots compared to their legged counterparts.
Weighting Pitch Contour and Loudness Contour in Mandarin Tone Perception in Cochlear Implant Listeners

Previous investigations found that loudness-contours within individual Mandarin monosyllables can drive categorical perception of Mandarin tone for cochlear implant (CI) users, while in normal hearing (NH) subjects the pitch contour is phonologically acknowledged to be the dominant cue. Here we further examine the weighting strategy of pitch induced and loudness induced contour identification on Mandarin tone perception by CI users. Twenty-seven versions of the disyllabic utterance /Lao3 Shi/ with orthogonally manipulated loudnesscontour and pitch-contour of the voiced portion of the second monosyllable /Shi/ served as the stimuli to both CI and NH subjects. In Mandarin, if /Shi/ is pronounced with high-flatpitched Tone 1 the word means “teacher”, with rising Tone 2 it means “well-behaved”, or with falling Tone 4 it means “always”. CI users generally had poorer word-recognition scores and their inter-subject variance was large. While NH subjects recognized tone reliably based on pitch-contour, half of the CI users relied on pitch-contour, the other half on loudness-contour, implying systematic differences in pitch coding in their CI processing. This paradigm of orthogonal manipulation of pitch and loudness contours could be developed into improved audiometric tests of Mandarin tone perception and pitch coding with CIs.
Semantic Organ Segmentation in 3D Whole-Body MR Images

Automated organ segmentation is a prerequisite for efficient analysis of MR data in large cohorts with thousands of participants. The feasibility and generalizability of previously proposed methods has mostly been demonstrated in smaller cohorts. The aim of this work is to implement and validate automated semantic 3D segmentation of liver and spleen on multi-contrast MR data of the body trunk which were acquired in a large epidemiological imaging study with the objective to provide a robust and general setup in a setting of limited training data. Liver and spleen were manually segmented in 173 MR images by an experienced radiologist, providing labeled ground-truth. Varying amount of training datasets were randomly chosen to train a convolutional neural network (CNN)-based segmentation with 4-fold patient-leave-out cross-validation and compared against a Random Forest (RF)-based segmentation. Validation amongst participants revealed high accuracies of 99.7%/99.9% for liver/spleen-segmentation with superiority of CNN to RF. In conclusion, automated semantic organ segmentation is feasible in a robust and general setup.
The Periodic Table of Data Structures

We describe the vision of being able to reason about the design space of data structures. We break this down into two questions: 1) Can we know all data structures that is possible to design? 2) Can we compute the performance of arbitrary designs on a given hardware and workload without having to implement the design or even access the target hardware? If those challenges are possible, then an array of exciting opportunities would become feasible such as interactive what-if design to improve the productivity of data systems researchers and engineers, and informed decision making in industrial settings with regards to critical hardware/workload/data structure design issues. Then, even fully automated discovery of new data structure designs becomes possible. Furthermore, the structure of the design space itself provides numerous insights and opportunities such as the existence of design continuums that can lead to data systems with deep adaptivity, and a new understanding of the possible performance trade-offs. Given the universal presence of data structures at the very core of any data-driven ﬁeld across all sciences and industries, reasoning about their design can have signiﬁcant beneﬁts, making it more feasible (easier, faster and cheaper) to adopt tailored state-of-the-art storage solutions. And this effect is going to become increasingly more critical as data keeps growing, hardware keeps changing and more applications/ﬁelds realize the transformative power and potential of data analytics. This paper presents this vision and surveys ﬁrst steps that demonstrate its feasibility.
Dense Chained Attention Network for Scene Text Recognition

Reading text in the wild is a challenging task in computer vision. Scene text suffers from various background noise, including shadow, irrelevant symbols and background texture. In order to reduce the disturbance of background noise, we propose a dense chained attention network with stacked attention modules for scene text recognition. Each attention module learns the attention map that is adapted to corresponding features to enhance the foreground text and suppress the background noise. Besides, the attention branch is designed with the convolution-deconvolution structure which rapidly captures global information to guide the discriminative feature selection. We stack multiple attention modules to gradually refine the attention maps and capture both the low-level appearance feature and the high-level semantic information. Extensive experiments on the standard benchmarks, the Street View Text, IIIT5K, and ICDAR datasets validate the superiority of the proposed method. The dense chained attention network achieves state-of-the-art or highly competitive recognition performance.
Speech Emotion Recognition Using Spectrogram & Phoneme Embedding

This paper proposes a speech emotion recognition method based on phoneme sequence and spectrogram. Both phoneme sequence and spectrogram retain emotion contents of speech which is missed if the speech is converted into text. We performed various experiments with different kinds of deep neural networks with phoneme and spectrogram as inputs. Three of those network architectures are presented here that helped to achieve better accuracy when compared to the state-of-the-art methods on benchmark dataset. A phoneme and spectrogram combined CNN model proved to be most accurate in recognizing emotions on IEMOCAP data. We achieved more than 4% increase in overall accuracy and average class accuracy as compared to the existing state-of-the-art methods.
Alcohol Intake Interacts with CDKAL1, HHEX, and OAS3 Genetic Variants, Associated with the Risk of Type 2 Diabetes by Lowering Insulin Secretion in Korean Adults

BACKGROUND Since alcohol intake increases the prevalence of type 2 diabetes (T2DM) in Koreans, we tested the hypothesis that the interactions of genetic variants involved in β-cell function and mass with alcohol intake increase the T2DM risk. METHODS The single nucleotide polymorphisms (SNPs) were selected by genome-wide association study for insulin secretion after adjusting for age, gender, area of residence, body mass index, and alcohol intake (p < 1 × 10-4 ) in 8,842 middle-aged adults in the Ansan/Ansung cohort. Genetic risk scores (GRSs) were calculated by summing the risk alleles of 4 selected SNPs, CDKAL1 rs7754840 and rs9460546, HHEX rs5015480, and OAS3 rs2072134. The GRSs were categorized into 3 groups by tertiles, and the association between GRS and insulin secretion was measured using logistic regression after adjusting for confounding factors in the Ansan/Ansung cohort. The results were confirmed by the Rural cohort. RESULTS HOMA-IR was higher and HOMA-B was much lower in the High-GRS than the Low-GRS in both cohorts. T2DM risk was higher by approximately 1.5-fold in the High-GRS than in the Low-GRS in both cohorts. In the High-GRS group, HOMA-B decreased by 0.89- and 0.62-fold in comparison with the Low-GRS in the Ansan/Ansung cohort and Rural cohort. The GRS interacted with alcohol intake to increase the risk of developing T2DM in the Ansan/Ansung cohort (p = 0.036) and Rural cohort (p = 0.071). The risk of T2DM increased in the High-GRS group with high alcohol intake and it was associated with decreased HOMA-B. High alcohol intake decreased HOMA-B regardless of GRS, and HOMA-B was lower in the descending order of Medium-GRS, Low-GRS, and High-GRS. However, HOMA-IR was not altered by alcohol intake, but was elevated in the High-GRS more than in the other groups. CONCLUSIONS Subjects with a High-GRS had an elevated risk of T2DM even with moderate alcohol intakes due to lower HOMA-B. High alcohol intake appears to be a risk factor for all Asians regardless of alcohol intake.