Miracle-dz/UniFAR-FTUs huggingface dataset:
BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer

As the applications of deep learning models on edge devices increase at an accelerating pace, fast adaptation to various scenarios with varying resource constraints has become a crucial aspect of model deployment. As a result, model optimization strategies with adaptive configuration are becoming increasingly popular. While single-shot quantized neural architecture search enjoys flexibility in both model architecture and quantization policy, the combined search space comes with many challenges, including instability when training the weight-sharing supernet and difficulty in navigating the exponentially growing search space. Existing methods tend to either limit the architecture search space to a small set of options or limit the quantization policy search space to fixed precision policies. To this end, we propose BatchQuant, a robust quantizer formulation that allows fast and stable training of a compact, single-shot, mixed-precision, weight-sharing supernet. We employ BatchQuant to train a compact supernet (offering over $10^{76}$ quantized subnets) within substantially fewer GPU hours than previous methods. Our approach, Quantized-for-all (QFA), is the first to seamlessly extend one-shot weight-sharing NAS supernet to support subnets with arbitrary ultra-low bitwidth mixed-precision quantization policies without retraining. QFA opens up new possibilities in joint hardware-aware neural architecture search and quantization. We demonstrate the effectiveness of our method on ImageNet and achieve SOTA Top-1 accuracy under a low complexity constraint ($<20$ MFLOPs). The code and models will be made publicly available at https://github.com/bhpfelix/QFA.
Learning to Act Safely With Limited Exposure and Almost Sure Certainty

This article puts forward the concept that learning to take safe actions in unknown environments, even with probability one guarantees, can be achieved without the need for an unbounded number of exploratory trials. This is indeed possible, provided that one is willing to navigate tradeoffs between optimality, level of exposure to unsafe events, and the maximum detection time of unsafe actions. We illustrate this concept in two complementary settings. We first focus on the canonical multiarmed bandit problem and study the intrinsic tradeoffs of learning safety in the presence of uncertainty. Under mild assumptions on sufficient exploration, we provide an algorithm that provably detects all unsafe machines in an (expected) finite number of rounds. The analysis also unveils a tradeoff between the number of rounds needed to secure the environment and the probability of discarding safe machines. We then consider the problem of finding optimal policies for a Markov decision process (MDP) with almost sure constraints. We show that the action-value function satisfies a barrier-based decomposition that allows for the identification of feasible policies independently of the reward process. Using this decomposition, we develop a barrier-learning algorithm, that identifies such unsafe state–action pairs in a finite expected number of steps. Our analysis further highlights a tradeoff between the time lag for the underlying MDP necessary to detect unsafe actions, and the level of exposure to unsafe events. Simulations corroborate our theoretical findings, further illustrating the aforementioned tradeoffs, and suggesting that safety constraints can speed up the learning process.
Synchronization in a two‐supplier assembly system: Combining a fixed lead‐time module with capacitated make‐to‐order production

A high‐tech manufacturer often produces products that consist of many modules. These modules are either sourced from one of its suppliers or produced in‐house. In this paper, we study the common case of an assembly system in which one module is sourced from a supplier with a fixed lead‐time, while the other module is produced by the manufacturer itself in a make‐to‐order production system. Since unavailability of one of the modules has costly consequences for the production of the end‐product, it is important to coordinate between the ordering policy for one module and the production of the other. We propose an order policy for the lead‐time module with base‐stock levels depending on the number of outstanding orders in the production system of the in‐house produced module. We prove monotonicity properties of this policy and show optimality. Furthermore, we conduct a computational experiment to evaluate how the costs of this policy compare to those of a policy with fixed base‐stock levels and show that average savings of up to 17% are attained.
High-dimensional Change-point Detection Using Generalized Homogeneity Metrics

Change-point detection has been a classical problem in statistics and econometrics. This work focuses on the problem of detecting abrupt distributional changes in the data-generating distribution of a sequence of high-dimensional observations, beyond the first two moments. This has remained a substantially less explored problem in the existing literature, especially in the high-dimensional context, compared to detecting changes in the mean or the covariance structure. We develop a nonparametric methodology to (i) detect an unknown number of change-points in an independent sequence of high-dimensional observations and (ii) test for the significance of the estimated change-point locations. Our approach essentially rests upon nonparametric tests for the homogeneity of two high-dimensional distributions. We construct a single change-point location estimator via defining a cumulative sum process in an embedded Hilbert space. As the key theoretical innovation, we rigorously derive its limiting distribution under the high dimension medium sample size (HDMSS) framework. Subsequently we combine our statistic with the idea of wild binary segmentation to recursively estimate and test for multiple change-point locations. The superior performance of our methodology compared to other existing procedures is illustrated via extensive simulation studies as well as over stock prices data observed during the period of the Great Recession in the United States.
Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence

Generating long and coherent text is an important but challenging task, particularly for open-ended language generation tasks such as story generation. Despite the success in modeling intra-sentence coherence, existing generation models (e.g., BART) still struggle to maintain a coherent event sequence throughout the generated text. We conjecture that this is because of the difficulty for the decoder to capture the high-level semantics and discourse structures in the context beyond token-level co-occurrence. In this paper, we propose a long text generation model, which can represent the prefix sentences at sentence level and discourse level in the decoding process. To this end, we propose two pretraining objectives to learn the representations by predicting inter-sentence semantic similarity and distinguishing between normal and shuffled sentence orders. Extensive experiments show that our model can generate more coherent texts than state-of-the-art baselines.
Sparse Spiking Gradient Descent

There is an increasing interest in emulating Spiking Neural Networks (SNNs) on neuromorphic computing devices due to their low energy consumption. Recent advances have allowed training SNNs to a point where they start to compete with traditional Artificial Neural Networks (ANNs) in terms of accuracy, while at the same time being energy efficient when run on neuromorphic hardware. However, the process of training SNNs is still based on dense tensor operations originally developed for ANNs which do not leverage the spatiotemporally sparse nature of SNNs. We present here the first sparse SNN backpropagation algorithm which achieves the same or better accuracy as current state of the art methods while being significantly faster and more memory efficient. We show the effectiveness of our method on real datasets of varying complexity (Fashion-MNIST, Neuromophic-MNIST and Spiking Heidelberg Digits) achieving a speedup in the backward pass of up to 150x, and 85% more memory efficient, without losing accuracy.
A Rigid Registration Method in TEVAR

Since the mapping relationship between definitized intra-interventional X-ray and undefined pre-interventional Computed Tomography(CT) is uncertain, auxiliary positioning devices or body markers, such as medical implants, are commonly used to determine this relationship. However, such approaches can not be widely used in clinical due to the complex realities. To determine the mapping relationship, and achieve a initializtion post estimation of human body without auxiliary equipment or markers, proposed method applies image segmentation and deep feature matching to directly match the X-ray and CT images. As a result, the well-trained network can directly predict the spatial correspondence between arbitrary X-ray and CT. The experimental results show that when combining our approach with the conventional approach, the achieved accuracy and speed can meet the basic clinical intervention needs, and it provides a new direction for intra-interventional registration.
Improving Online Forums Summarization via Hierarchical Unified Deep Neural Network

Online discussion forums are prevalent and easily accessible, thus allowing people to share ideas and opinions by posting messages in the discussion threads. Forum threads that significantly grow in length can become difficult for participants, both newcomers and existing, to grasp main ideas. To mitigate this problem, this study aims to create an automatic text summarizer for online forums. We present Hierarchical Unified Deep Neural Network to build sentence and thread representations for the forum summarization. In this scheme, Bi-LSTM derives a representation that comprises information of the whole sentence and whole thread; whereas, CNN captures most informative features with respect to context from sentence and thread. Attention mechanism is applied on top of CNN to further highlight high-level representations that carry important information contributing to a desirable summary. Extensive performance evaluation has been conducted on three datasets, two of which are real-life online forums and one is news dataset. The results reveal that the proposed model outperforms several competitive baselines.
Semi-Supervised Learning Combining Backpropagation and STDP: STDP Enhances Learning by Backpropagation with a Small Amount of Labeled Data in a Spiking Neural Network

A semi-supervised learning method for spiking neural networks is proposed. The proposed method consists of supervised learning by backpropagation and subsequent unsupervised learning by spike-timing-dependent plasticity (STDP), which is a biologically plausible learning rule. Numerical experiments show that the proposed method improves the accuracy without additional labeling when a small amount of labeled data is used. This feature has not been achieved by existing semi-supervised learning methods of discriminative models. It is possible to implement the proposed learning method for event-driven systems. Hence, it would be highly efficient in real-time problems if it were implemented on neuromorphic hardware. The results suggest that STDP plays an important role other than self-organization when applied after supervised learning, which differs from the previous method of using STDP as pre-training interpreted as self-organization.
Antecedents of Intention to Adopt Mobile Health (mHealth) Application and Its Impact on Intention to Recommend: An Evidence from Indonesian Customers

Introduction Mobile health (mHealth) applications gain popularity due to the increasing number of mobile phone usage and internet penetration, which might help some of Indonesia's medical issues. However, the uptake of mHealth applications is still low in Indonesia. This study is aimed at understanding the factors that drive individuals to adopt mHealth applications and their impact on the intention to recommend. Methods We applied a new model that combines three different theories with some other constructs: an extended unified theory of acceptance and use of technology, diffusion of innovation, and the internet customer trust model. The study used a cross-sectional study design with partial least squares causal modeling approach. Results There are 787 respondents in our study, with the majority of them being female, young adults. Our model could explain 53.2% of the variance of intention to adopt while explaining 48.3% of the variance of intention to recommend. Initial trust in mHealth platform (β = 0.373, p = <0.001), facilitating conditions (β = 0.131, p = <0.01), and performance expectancy (β = 0.099, p = <0.05) are the top three most important drivers of intention to adopt mHealth applications. Lastly, importance-performance map analysis (IPMA) showed that the mHealth application's initial trust is the most important construct with a high-performance score. Discussion. Mobile health developers and managers need to improve initial trust in the mHealth platform, facilitating conditions, and performance expectancy when developing the applications. With a medium Q2predict, these factors can be applied out of the research context with medium predictive power.
Topical Treatment for Scarring and Non-Scarring Alopecia: An Overview of the Current Evidence

Abstract Alopecia is a clinical condition related to hair loss that can significantly affect both male and female adults’ quality of life. Despite the high market demand, only few drugs are currently approved for alopecia treatment. Topical formulations still bring drawbacks, such as scalp irritation with frequent use, and low drug absorption to the site of action, which limits the efficacy. The most recent research points out that different formulation technology could circumvent the aforementioned flaws. Such technology includes incorporation of drugs in rigid or deformable nanoparticles, strategies involving physical, energetical and mechanical techniques, such as iontophoresis, sonophoresis, microneedling, and the use of solid effervescent granules to be hydrated at the moment of application in the scalp. In this paper, the progress of current research on topical formulations dedicated to the treatment of alopecia is reviewed and discussed.
LOXL1 gene polymorphism candidates for exfoliation glaucoma are also associated with a risk for primary open-angle glaucoma in a Caucasian population from central Russia

Purpose This study was aimed to replicate the previously reported associations of the three LOXL1 gene polymorphisms with exfoliation glaucoma (XFG) and to analyze these genetic variants for their possible contribution to primary open-angle glaucoma (POAG) in Caucasians from central Russia. Methods In total, 932 participants were recruited for the study, including 328 patients with XFG, 208 patients with POAG, and 396 controls. The participants were of Russian ethnicity (self-reported) and born in Central Russia. They were genotyped at three single nucleotide polymorphisms (SNPs) of the LOXL1 gene (rs2165241, rs4886776, and rs893818). The association was analyzed using logistic regression. Results Allele C of rs2165241 was associated with a decreased risk of XFG (odds ratio [OR] =0.27–0.45, pperm ≤5*10−6) and POAG (OR=0.35–0.47, рperm≤0.001), and allele A of rs4886776 and rs893818 were associated with a lower risk of XFG (OR=0.53–0.57, рperm≤0.001). Haplotype TGG of loci rs2165241-rs4886776-rs893818 was associated with an elevated risk of XFG (OR=2.23, рperm=0.001) and POAG (OR=2.01, рperm=0.001), haplotype CGG was also associated with a decreased risk of XFG (OR=0.45, рperm=0.001) and POAG (OR=0.35, рperm=0.001). Haplotype CAA was associated with a decreased risk of XFG only (OR=0.50, рperm=0.001). Conclusions Polymorphisms rs2165241, rs4886776, and rs893818 of the LOXL1 gene showed association with XFG and POAG in a Caucasian sample from central Russia.
Prevalence and viral load determination of BK polyomavirus among Iranian patients with brain tumors

Background: Due to persistent infections of human central nervous system (CNS), polyomaviruses have been identified as one of the risk factors for brain tumor development. Human BK virus is of signiﬁcant interest due to its experimental neuro-oncogenic potential and the possible association with CNS neoplasms. However, the results of different studies are discrepant. In the present study, we aimed to investigate the prevalence of BK virus genome and quantify BK viral load in Iranian patients with primary and metastatic brain malignancies. Methods: To assess the prevalence of BK virus sequences, a total of 58 fresh brain tumors were examined by quantitative real-time PCR. The BK viral load was determined as viral copy number per cell. Results: Of the 58 brain tumor samples BK tumor antigen (TAg) sequences were detected in 26 (44.8%) of cases. In primary brain tumors, BK virus sequences were recognized more frequently in schwannomas (15.5%) and meningiomas (12.1%). The mean BK virus TAg copy number in positive cases was 0.20×10-3±0.27×10-3 (range 0.01×10-3- 0.8×10-3) copies per cell. Conclusion: Taken together, in the present study low copy numbers of BK virus TAg gene was detected in brain tumor cells, which can indicate that BK virus may contribute to tumor induction by indirect mechanisms or neuro-persistence of this virus without any pathological consequences.
Postresection prognosis of combined hepatocellular carcinoma-cholangiocarcinoma according to the 2010 World Health Organization classification: single-center experience of 168 patients

Purpose Combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CC) has wide histologic diversity. This study investigated the effects of cHCC-CC histology, according to the 2010 World Health Organization (WHO) classification, on patient prognosis. Methods The medical records of patients who underwent surgical resection for cHCC-CC at our institution between July 2012 and June 2019 were retrospectively evaluated. Results During the study period, 168 patients, 122 males (72.6%) and 46 females (27.4%), underwent surgical resection for cHCC-CC, including 159 patients (94.6%) who underwent R0 resection. Mean tumor diameter was 4.4 ± 2.8 cm, and 161 patients (95.8%) had solitary tumors. Histologically, 86 patients (51.2%) had classical type, and 82 (48.8%) had tumors with stem cell (SC) features, including 33 (19.6%) with intermediate-cell and 23 (13.7%) each with typical SC and cholangiolocellular features; 3 tumors (1.8%) were unclassifiable. At 1, 3, and 5 years, tumor recurrence rates were 31.9%, 49.6%, and 58.1%, respectively, and patient survival rates were 91.0%, 70.2%, and 60.3%, respectively. Univariate analysis showed that tumor size of >5 cm, microscopic and macroscopic vascular invasion, lymph node metastasis, 8th edition of the American Joint Committee on Cancer (AJCC) tumor stage, and 2010 WHO classification were significantly prognostic. Multivariate analysis showed that the 8th AJCC tumor stage and 2010 WHO histologic classification were independently prognostic for tumor recurrence and patient survival. There were no significant prognostic differences among the 3 SC subtypes. Conclusion Postresection outcomes are better in patients with SC-type than with classical-type cHCC-CC.
Impact of self-esteem on the relationship between orthodontic treatment and the oral health-related quality of life in patients after orthodontic treatment – a systematic review

Objective The treatment protocol in the modern health care paradigm has shifted considerably towards enhancing the quality of life in the last decade. This is particularly important in cosmetic and elective treatments, and hence the interest in oral health-related quality of life (OHRQoL) also has increased. OHRQoL always been measured by endogenous, functional, social, or psychological determinants. Self-esteem (SE) is one of the internal factors that affect the perception of malocclusion and hence OHRQoL before and after treatment. The purpose of this review is to assess whether there exists any correlation between the Oral Health-Related Quality of Life, Self-esteem (SE) in patients following orthodontic treatment. Methods A literature search was confined to the English language using Medical Subject Heading terms (MeSH) in PubMed, Cochrane Library, and Ovid® covering the period from January 1, 1951 to May 15, 2020. Search in Google Scholar, grey literature, and hand search on cross-references was performed to find additional data. The studies found to be suitable were selected based on the predefined inclusion and exclusion criteria. The quality of assessment and risk of bias for the included studies were evaluated independently by two invigilators utilizing “The Cochrane Collaboration’s tool for assessing the risk of bias” and “Modified version of the Newcastle Ottawa scale” for Randomized Controlled Trials (RCTs) and non-randomized trials respectively. Results A total of 7688 studies were retrieved from all the sources. After screening all the titles and excluding the duplicates, 28 studies were finally included for text review, and all of them were fit for quality appraisal. The design of the final studies included comprised of 3 RCTs, 14 cohort studies, 9 cross-sectional studies, and 2 case-control studies. Conclusion There is moderate evidence to show that fixed orthodontic treatment improves OHRQoL and SE in children. OHRQoL also increased in adolescents and adults. However, there is a weak correlation between SE and OHRQoL. More evidence-based studies are needed to analyze the relationship.