Miracle-dz/UniFAR-FTUs huggingface dataset:
Facile Designing of Poly(vinyl alcohol-g-acrylonitrile)-Supported Flexible Graphene Films

Graphene has attracted considerable interest over recent years due to its good electrical and mechanical properties. In the present study, a safe and facile method has been adopted for the preparation of reduced graphene oxide solution and its subsequent homogeneous dispersion into the poly(vinyl alcohol-g-acrylonitrile) film prepared by in situ free radical polymerization method. The prepared polymer-supported graphene films were characterized by Fourier transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy, transmission electron microscopy, and Raman spectroscopy techniques. Graphical abstract
VEER: Disagreement-Free Multi-objective Configuration

Software comes with many configuration options, satisfying varying needs from users. Exploring those options for non-functional requirements can be tedious, time consuming, and even error-prone (if done manually). Worse, many software systems can be tuned to multiple objectives (e.g., faster response time, fewer memory requirements, decreased network traffic, decreased energy consumption, etc.). Learning how to adjust the system among these multiple objectives is complicated due to the trade-off among objectives; i.e., things that seem useful to achieve one objective could be detrimental to another objective. Consequentially, the optimizer built for one objective may have different (or even opposite) insights on how to locate good solutions from the optimizer built from another objective. In this paper, we define this scenario as the model disagreement problem. One possible solution to this problem is to find a one-dimensional approximation to the N-objective space. In this way, the case is converted to a single-objective optimization, which is naturally confusion-free. This paper demonstrates VEER, a tool that builds such an approximation by combining our dimensionality-reduction heuristic on top of one of the state-of-the-art optimizers, FLASH. VEER can explore very large configuration spaces by evaluating just a small fraction of the total number of configurations (e.g., a space of 81,000 configurations can be explored by 70 samples). The experimental result in this paper demonstrates the feasibility of our approach in terms of the on-par quality of the solution set generated by the optimizer, and the resolved model disagreement within the optimizer. Moreover, we demonstrate that VEER has an improved computational complexity compared to the original optimizer (by up to 1,000 times faster while maintaining on-par performance)
Closed-Loop Wireless Power Transfer With Adaptive Waveform and Beamforming: Design, Prototype, and Experiment

A closed-loop far-field wireless power transfer (WPT) system with adaptive waveform and beamforming using limited feedback is designed, prototyped, and experimented. Spatial domain and frequency domain are jointly exploited by utilizing waveform and beamforming at the transmitter in WPT system to adapt to the multipath fading channel and boost the output dc power. A closed-loop architecture based on a codebook design and an over-the-air limited feedback with low complexity is proposed. The codebook consists of multiple codewords where each codeword represents particular waveform and beamforming. The transmitter sweeps through the codebook and the receiver then feeds the optimal codeword index back to the transmitter, so that the waveform and beamforming can be adaptive for maximizing the output dc power without requiring explicit channel estimation and the knowledge of accurate Channel State Information. The proposed closed-loop WPT with adaptive waveform and beamforming using limited feedback is prototyped using a Software Defined Radio equipment and measured in two real indoor environments. It is experimentally shown that the proposed closed-loop WPT with adaptive waveform and beamforming is able to enhance the output dc power by up to 14.7 dB in comparison with conventional 1-tone 1-antenna WPT system.
W-RST: Towards a Weighted RST-style Discourse Framework

Aiming for a better integration of data-driven and linguistically-inspired approaches, we explore whether RST Nuclearity, assigning a binary assessment of importance between text segments, can be replaced by automatically generated, real-valued scores, in what we call a Weighted-RST framework. In particular, we find that weighted discourse trees from auxiliary tasks can benefit key NLP downstream applications, compared to nuclearity-centered approaches. We further show that real-valued importance distributions partially and interestingly align with the assessment and uncertainty of human annotators.
Rethinking Training from Scratch for Object Detection

The ImageNet pre-training initialization is the de-facto standard for object detection. He et al. found it is possible to train detector from scratch(random initialization) while needing a longer training schedule with proper normalization technique. In this paper, we explore to directly pre-training on target dataset for object detection. Under this situation, we discover that the widely adopted large resizing strategy e.g. resize image to (1333, 800) is important for fine-tuning but it's not necessary for pre-training. Specifically, we propose a new training pipeline for object detection that follows `pre-training and fine-tuning', utilizing low resolution images within target dataset to pre-training detector then load it to fine-tuning with high resolution images. With this strategy, we can use batch normalization(BN) with large bath size during pre-training, it's also memory efficient that we can apply it on machine with very limited GPU memory(11G). We call it direct detection pre-training, and also use direct pre-training for short. Experiment results show that direct pre-training accelerates the pre-training phase by more than 11x on COCO dataset while with even +1.8mAP compared to ImageNet pre-training. Besides, we found direct pre-training is also applicable to transformer based backbones e.g. Swin Transformer. Code will be available.
HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation

Superpixels serve as a powerful preprocessing tool in numerous computer vision tasks. By using superpixel representation, the number of image primitives can be largely reduced by orders of magnitudes. With the rise of deep learning in recent years, a few works have attempted to feed deeply learned features / graphs into existing classical superpixel techniques. However, none of them are able to produce superpixels in near real-time, which is crucial to the applicability of superpixels in practice. In this work, we propose a two-stage graph-based framework for superpixel segmentation. In the first stage, we introduce an efficient Deep Affinity Learning (DAL) network that learns pairwise pixel affinities by aggregating multi-scale information. In the second stage, we propose a highly efficient superpixel method called Hierarchical Entropy Rate Segmentation (HERS). Using the learned affinities from the first stage, HERS builds a hierarchical tree structure that can produce any number of highly adaptive superpixels instantaneously. We demonstrate, through visual and numerical experiments, the effectiveness and efficiency of our method compared to various state-of-the-art superpixel methods. 1
Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention

Natural language processing techniques have demonstrated promising results in keyphrase generation. However, one of the major challenges in neural keyphrase generation is processing long documents using deep neural networks. Generally, documents are truncated before given as inputs to neural networks. Consequently, the models may miss essential points conveyed in the target document. To overcome this limitation, we propose SEG-Net, a neural keyphrase generation model that is composed of two major components, (1) a selector that selects the salient sentences in a document and (2) an extractor-generator that jointly extracts and generates keyphrases from the selected sentences. SEG-Net uses Transformer, a self-attentive architecture, as the basic building block with a novel layer-wise coverage attention to summarize most of the points discussed in the document. The experimental results on seven keyphrase generation benchmarks from scientific and web documents demonstrate that SEG-Net outperforms the state-of-the-art neural generative methods by a large margin.
Advancing research on unconscious priming: When can scientists claim an indirect task advantage?

Current literature holds that many cognitive functions can be performed outside consciousness. Evidence for this view comes from unconscious priming. In a typical experiment, visual stimuli are masked such that participants are close to chance performance when directly asked to which of two categories the stimuli belong. This close-to-zero sensitivity is seen as evidence that participants cannot consciously report the category of the masked stimuli. Nevertheless, the category of the masked stimuli can indirectly affect responses to other stimuli (e.g., reaction times or brain activity)-an effect called priming. The priming effect is seen as evidence for a higher sensitivity to the masked stimuli in the indirect responses as compared with the direct responses. Such an apparent difference in sensitivities is taken as evidence that processing occurred unconsciously. But we show that this "standard reasoning of unconscious priming" is flawed: Sensitivities are not properly compared, creating the wrong impression of a difference in sensitivities even if there is none. We describe the appropriate way to determine sensitivities, replicate the behavioral part of a landmark study, develop methods to estimate sensitivities from reported summary statistics of published studies, and use these methods to reanalyze 15 highly influential studies. Results show that the interpretations of many studies need to be changed and that a community effort is required to reassess the vast literature on unconscious priming. This process will allow scientists to learn more about the true boundary conditions of unconscious priming, thereby advancing the scientific understanding of consciousness. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising

In this paper, we propose a regularization technique for noisy-image super-resolution and image denoising. Total variation (TV) regularization is adopted in many image processing applications to preserve the local smoothness. However, TV prior is prone to oversmoothness, staircasing effect, and contrast losses. Nonlocal TV (NLTV) mitigates the contrast losses by adaptively weighting the smoothness based on the similarity measure of image patches. Although it suppresses the noise effectively in the flat regions, it might leave residual noise surrounding the edges especially when the image is not oversmoothed. To address this problem, we propose the bilateral spectrum weighted total variation (BSWTV). Specially, we apply a locally adaptive shrink coefficient to the image gradients and employ the eigenvalues of the covariance matrix of the weighted image gradients to effectively refine the weighting map and suppress the residual noise. In conjunction with the data fidelity term derived from a mixed Poisson–Gaussian noise model, the objective function is decomposed and solved by the alternating direction method of multipliers (ADMM) algorithm. In order to remove the outliers and facilitate the convergence stability, the weighting map is smoothed by a Gaussian filter with an iteratively decreased kernel width and updated in a momentum-based manner in each ADMM iteration. We benchmark our method with the state-of-the-art approaches on the public real-world datasets for super-resolution and image denoising. Experiments show that the proposed method obtains outstanding performance for super-resolution and achieves promising results for denoising on real-world images.
Limitations of Implicit Bias in Matrix Sensing: Initialization Rank Matters

In matrix sensing, we first numerically identify the sensitivity to the initialization rank as a new limitation of the implicit bias of gradient flow. We will partially quantify this phenomenon mathematically, where we establish that the gradient flow of the empirical risk is implicitly biased towards low-rank outcomes and successfully learns the planted low-rank matrix, provided that the initialization is low-rank and within a specific “capture neighborhood”. This capture neighborhood is far larger than the corresponding neighborhood in local refinement results; the former contains all models with zero training error whereas the latter is a small neighborhood of a model with zero test error. These new insights enable us to design an alternative algorithm for matrix sensing that complements the high-rank and near-zero initialization scheme which is predominant in the existing literature.
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer

Dealing with severe class imbalance poses a major challenge for many real-world applications, especially when the accurate classification and generalization of minority classes are of primary interest. In computer vision and NLP, learning from datasets with long-tail behavior is a recurring theme, especially for naturally occurring labels. Existing solutions mostly appeal to sampling or weighting adjustments to alleviate the extreme imbalance, or impose inductive bias to prioritize generalizable associations. Here we take a novel perspective to promote sample efficiency and model generalization based on the invariance principles of causality. Our contribution posits a meta-distributional scenario, where the causal generating mechanism for label-conditional features is invariant across different labels. Such causal assumption enables efficient knowledge transfer from the dominant classes to their under-represented counterparts, even if their feature distributions show apparent disparities. This allows us to leverage a causal data augmentation procedure to enlarge the representation of minority classes. Our development is orthogonal to the existing imbalanced data learning techniques thus can be seamlessly integrated. The proposed approach is validated on an extensive set of synthetic and real-world tasks against state-of-the-art solutions.
Fed+: A Uniﬁed Approach to Robust Personalized Federated Learning

We present a class of methods for robust, personalized federated learning, called Fed+, that uniﬁes many federated learning algorithms. The principal advantage of this class of methods is to better accommodate the real-world characteristics found in federated training, such as the lack of IID data across parties, the need for robustness to outliers or stragglers, and the requirement to perform well on party-speciﬁc datasets. We achieve this through a problem formulation that allows the central server to employ robust ways of aggregating the local models while keeping the structure of local computation intact. Without making any statistical assumption on the degree of heterogeneity of local data across parties, we provide convergence guarantees for Fed+ for convex and non-convex loss functions and robust aggregation. The Fed+ theory is also equipped to handle heterogeneous computing environments including stragglers without additional assumptions; speciﬁcally, the convergence results cover the general setting where the number of local update steps across parties can vary. We demonstrate the ben-eﬁts of Fed+ through extensive experiments across standard benchmark datasets as well as on a challenging real-world problem in ﬁnancial portfolio management where the heterogeneity of party-level data can lead to training failure in standard federated learning approaches.
Adapting Long Context NLM for ASR Rescoring in Conversational Agents

Neural Language Models (NLM), when trained and evaluated with context spanning multiple utterances, have been shown to consistently outperform both conventional n-gram language models and NLMs that use limited context. In this paper, we investigate various techniques to incorporate turn based context history into both recurrent (LSTM) and Transformer-XL based NLMs. For recurrent based NLMs, we explore context carry over mechanism and feature based augmentation, where we incorporate other forms of contextual information such as bot response and system dialogue acts as classified by a Natural Language Understanding (NLU) model. To mitigate the sharp nearby, fuzzy far away problem with contextual NLM, we propose the use of attention layer over lexical metadata to improve feature based augmentation. Additionally, we adapt our contextual NLM towards user provided on-the-fly speech patterns by leveraging encodings from a large pre-trained masked language model and performing fusion with a Transformer-XL based NLM. We test our proposed models using N-best rescoring of ASR hypotheses of task-oriented dialogues and also evaluate on downstream NLU tasks such as intent classification and slot labeling. The best performing model shows a relative WER between 1.6% and 9.1% and a slot labeling F1 score improvement of 4% over non-contextual baselines.
Deep Residual Learning in Spiking Neural Networks

Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-based approaches due to discrete binary activation and complex spatial-temporal dynamics. Considering the huge success of ResNet in deep learning, it would be natural to train deep SNNs with residual learning. Previous Spiking ResNet mimics the standard residual block in ANNs and simply replaces ReLU activation layers with spiking neurons, which suffers the degradation problem and can hardly implement residual learning. In this paper, we propose the spike-element-wise (SEW) ResNet to realize residual learning in deep SNNs. We prove that the SEW ResNet can easily implement identity mapping and overcome the vanishing/exploding gradient problems of Spiking ResNet. We evaluate our SEW ResNet on ImageNet, DVS Gesture, and CIFAR10-DVS datasets, and show that SEW ResNet outperforms the state-of-the-art directly trained SNNs in both accuracy and time-steps. Moreover, SEW ResNet can achieve higher performance by simply adding more layers, providing a simple method to train deep SNNs. To our best knowledge, this is the first time that directly training deep SNNs with more than 100 layers becomes possible. Our codes are available at https://github.com/fangwei123456/Spike-Element-Wise-ResNet.
Comparison of Extracorporeal Shockwave Therapy and Blind Steroid Injection in Patients With Coccydynia

Objective Extracorporeal shockwave therapy (ESWT) emerged recently for the treatment of musculoskeletal conditions owing to its regenerative and anti-inflammatory effects. In this study, the aim was to compare the efficacies of ESWT and steroid injection in patients with coccydynia. Design Thirty-four patients with coccydynia were randomized into two groups. In the ESWT group, patients received radial shockwave for three weekly sessions. The second treatment group received an injection of steroid in the tip of the coccyx or sacrococcygeal junction. Outcome measures were visual analog scale, Short-Form Health Survey, and Dallas Pain Questionnaire. Visual analog scale was measured at baseline and 1 wk, 1 mo, 2 mos, and 6 mos after the intervention. Short-Form Health Survey and Dallas Pain Questionnaire were assessed before and 1 mo after treatment. Results The visual analog scale score was significantly decreased in all follow-ups in both groups. There was also a significant decrease between 1 wk and three other posttreatment visits in the ESWT treatment group. Time and group interaction effect was significant on the visual analog scale (F2.64, 84.63 = 2.95, P = 0.043), in favor of ESWT. Concerning the Short-Form Health Survey and Dallas Pain Questionnaire, only some dimensions improved. Conclusions ESWT is an effective intervention in patients with coccydynia and had more long-lasting efficacy than steroid in terms of pain relief over 6 mos of observation.
Employment status after traumatic brain injury and the effect of concomitant injuries on return to work

ABSTRACT Purpose This study aims to describe the employment status and pattern among survivors of traumatic brain injury after motor vehicle accidents, and to explore the effects of demographic, injury variables and concomitant injuries on the employment status . Method A retrospective analyses of 370 medical reports written for patients who sustained traumatic brain injury from motor vehicle accidents was conducted. To establish the employment pattern, the pre-injury employment history was compared to the latest employment status documented. Types and severity of concomitant injuries were rated according to Abbreviated Injury Scale criteria. All significant variables were further analyzed using logistic regression to explore predictors of employment. Results Up to 87% of the patients sustained concomitant injuries, with more than two-thirds (72%) scoring ≤ 2 on the Abbreviated Injury Scale. One hundred and eighty-two patients (49.2%) successfully returned to work. Among those who returned to work, 34% returned to former employment with pre-injury job description. Severity of traumatic brain injury, length of acute hospital stay, ambulation status and cognitive status were found to be significant predictive factors for employment status post traumatic brain injury. Presence of concomitant extremity injuries was found to influence the employment pattern among traumatic brain injury survivors. Conclusion The return to work rate was somewhat low and was not influenced by presence of concomitant injuries.
Factors influencing health behaviour change during pregnancy: a systematic review and meta-synthesis

ABSTRACT Pregnancy is an opportune time for women to make healthy changes to their lifestyle, however, many women struggle to do so. Multiple reasons have been posited as to why this may be. This review aimed to synthesise this literature by identifying factors that influence women’s health behaviour during pregnancy, specifically in relation to dietary behaviour, physical activity, smoking, and alcohol use. Bibliographic databases (MEDLINE, PsycINFO, CINAHL-P, MIDIRS) were systematically searched to retrieve studies reporting qualitative data regarding women’s experiences or perceptions of pregnancy-related behaviour change relating to the four key behaviours. Based on the eligibility criteria, 30,852 records were identified and 92 studies were included. Study quality was assessed using the CASP tool and data were thematically synthesised. Three overarching themes were generated from the data. These were (1) A time to think about ‘me’, (2) Adopting the ‘good mother’ role, and (3) Beyond mother and baby. These findings provide an improved understanding of the various internal and external factors influencing women’s health behaviour during the antenatal period. This knowledge provides the foundations from which future pregnancy-specific theories of behaviour change can be developed and highlights the importance of taking a holistic approach to maternal behaviour change in clinical practice.