Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning

Ranking is a fundamental and widely studied problem in scenarios such as search, advertising, and recommendation. However, joint optimization for multi-scenario ranking, which aims to improve the overall performance of several ranking strategies in different scenarios, is rather untouched. Separately optimizing each individual strategy has two limitations. The first one is lack of collaboration between scenarios meaning that each strategy maximizes its own objective but ignores the goals of other strategies, leading to a sub-optimal overall performance. The second limitation is the inability of modeling the correlation between scenarios meaning that independent optimization in one scenario only uses its own user data but ignores the context in other scenarios. In this paper, we formulate multi-scenario ranking as a fully cooperative, partially observable, multi-agent sequential decision problem. We propose a novel model named Multi-Agent Recurrent Deterministic Policy Gradient (MA-RDPG) which has a communication component for passing messages, several private actors (agents) for making actions for ranking, and a centralized critic for evaluating the overall performance of the co-working actors. Each scenario is treated as an agent (actor). Agents collaborate with each other by sharing a global action-value function (the critic) and passing messages that encodes historical information across scenarios. The model is evaluated with online settings on a large E-commerce platform. Results show that the proposed model exhibits significant improvements against baselines in terms of the overall performance.
Multi-command SSVEP-based BCI system via single flickering frequency half-field stimulation pattern

This paper proposes a half-field steady state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system to enhance the number of limited commands obtained from the existing SSVEP-based BCI methods. With the theory of vision perception and the concept of the existing half-field SSVEP-based BCI system, we propose the new stimulation pattern that, by using only one frequency, four commands can be generated with the average classification accuracy of approximately 77%. By using only one frequency, eye fatigue can be reduced. Furthermore, this method can be efficiently used to further increase the number of commands for the existing SSVEP-based BCI system.
A Feature-Oriented Sentiment Rating for Mobile App Reviews

In this paper, we propose a general framework that allows developers to filter, summarize and analyze user reviews written about applications on App Stores. Our framework extracts automatically relevant features from reviews of apps (e.g., information about functionalities, bugs, requirements, etc) and analyzes the sentiment associated with each of them. Our framework has three main building blocks, namely, (i) topic modeling, (ii) sentiment analysis and (iii) summarization interface. The topic modeling block aims at finding semantic topics from textual comments, extracting the target features based on the most relevant words of each discovered topic. The sentiment analysis block detects the sentiment associated with each discovered feature. The summarization interface provides to developers an intuitive visualization of the features (i.e., topics) and their associated sentiment, providing richer information than a 'star rating' strategy. Our evaluation shows that the topic modeling block is able to organize information provided by users in subcategories that facilitate the understanding of which features more positively/negatively impact the overall evaluation of the application. Regarding user satisfaction, we can observe that, in spite of the star rating being a good measure of evaluation, the Sentiment Analysis technique is more accurate in capturing the sentiment transmitted by the user by means of a comment.
Cross-Domain Adversarial Auto-Encoder

In this paper, we propose the Cross-Domain Adversarial Auto-Encoder (CDAAE) to address the problem of cross-domain image inference, generation and transformation. We make the assumption that images from different domains share the same latent code space for content, while having separate latent code space for style. The proposed framework can map cross-domain data to a latent code vector consisting of a content part and a style part. The latent code vector is matched with a prior distribution so that we can generate meaningful samples from any part of the prior space. Consequently, given a sample of one domain, our framework can generate various samples of the other domain with the same content of the input. This makes the proposed framework different from the current work of cross-domain transformation. Besides, the proposed framework can be trained with both labeled and unlabeled data, which makes it also suitable for domain adaptation. Experimental results on data sets SVHN, MNIST and CASIA show the proposed framework achieved visually appealing performance for image generation task. Besides, we also demonstrate the proposed method achieved superior results for domain adaptation. Code of our experiments is available in this https URL.
Differences in the Electrophysiological Properties of Mouse Somatosensory Layer 2/3 Neurons In Vivo and Slice Stem from Intrinsic Sources Rather than a Network-Generated High Conductance State

Abstract Synaptic activity in vivo can potentially alter the integration properties of neurons. Using recordings in awake mice, we targeted somatosensory layer 2/3 pyramidal neurons and compared neuronal properties with those from slices. Pyramidal cells in vivo had lower resistance and gain values, as well as broader spikes and increased spike frequency adaptation compared to the same cells in slices. Increasing conductance in neurons using dynamic clamp to levels observed in vivo, however, did not lessen the differences between in vivo and slice conditions. Further, local application of tetrodotoxin (TTX) in vivo blocked synaptic-mediated membrane voltage fluctuations but had little impact on pyramidal cell membrane input resistance and time constant values. Differences in electrophysiological properties of layer 2/3 neurons in mouse somatosensory cortex, therefore, stem from intrinsic sources separate from synaptic-mediated membrane voltage fluctuations.
Natural Antisense Transcripts: Molecular Mechanisms and Implications in Breast Cancers

Natural antisense transcripts are RNA sequences that can be transcribed from both DNA strands at the same locus but in the opposite direction from the gene transcript. Because strand-specific high-throughput sequencing of the antisense transcriptome has only been available for less than a decade, many natural antisense transcripts were first described as long non-coding RNAs. Although the precise biological roles of natural antisense transcripts are not known yet, an increasing number of studies report their implication in gene expression regulation. Their expression levels are altered in many physiological and pathological conditions, including breast cancers. Among the potential clinical utilities of the natural antisense transcripts, the non-coding|coding transcript pairs are of high interest for treatment. Indeed, these pairs can be targeted by antisense oligonucleotides to specifically tune the expression of the coding-gene. Here, we describe the current knowledge about natural antisense transcripts, their varying molecular mechanisms as gene expression regulators, and their potential as prognostic or predictive biomarkers in breast cancers.
LinguaKit: uma ferramenta multilingue para a análise linguística e a extração de informação

Este artigo apresenta LinguaKit, uma suite multilingue de ferramentas de analise, extracao, anotacao e correcao linguisticas. LinguaKit permite realizar tarefas tao diversas como a lematizacao, a etiquetagem morfossintatica ou a analise sintatica (entre outras), incluindo tambem aplicacoes para a analise de sentimentos (ou minaria de opinioes), a extracao de termos multipalavra, ou a anotacao concetual e ligacao a recursos enciclopedicos tais como a DBpedia. A maior parte dos modulos funcionam para quatro variedades linguisticas: portugues, espanhol, ingles e galego. A linguagem de programacao de LinguaKit e Perl, e o codigo esta disponivel sob a licenca livre GPLv3.
Otic drug delivery systems: formulation principles and recent developments

Abstract Disorders of the ear severely impact the quality of life of millions of people, but the treatment of these disorders is an ongoing, but often overlooked challenge particularly in terms of formulation design and product development. The prevalence of ear disorders has spurred significant efforts to develop new therapeutic agents, but perhaps less innovation has been applied to new drug delivery systems to improve the efficacy of ear disease treatments. This review provides a brief overview of physiology, major diseases, and current therapies used via the otic route of administration. The primary focuses are on the various administration routes and their formulation principles. The article also presents recent advances in otic drug deliveries as well as potential limitations. Otic drug delivery technology will likely evolve in the next decade and more efficient or specific treatments for ear disease will arise from the development of less invasive drug delivery methods, safe and highly controlled drug delivery systems, and biotechnology targeting therapies.
A hybrid tool for reaching and grasping rehabilitation: The ArmeoFES

Many research groups are currently working with robotic devices for hand grasp rehabilitation and restoration. A common problem in this area is the fact that existing and commercially available robotic exoskeletons are able to provide gravity compensation of the shoulder and elbow but do not provide any support for the grasping and releasing movements of the hand. The lack of a flexible support technology for the hand reduces the possible ways in which clinicians can deal with the issue of a personalized, effective rehabilitation. This paper presents new software that allows FES assisted grasping to integrate with the ArmeoSpring (Hocoma AG). The system uses a Man-In-The-Loop control approach, whereby surface EMG signals from proximal muscles are used to trigger and modulate multichannel FES applied to distal muscles, thus allowing patient induced and strength adapted movement control of the hand. Combining volitionally controlled FES with arm-weight-compensation allows early adoption of FES assisted therapy for patients, augmenting their functionalities and extending training capabilities with the ArmeoSpring.
Monitoring and Control in Language Production

Language research has provided insight into how speakers translate a thought into a sequence of sounds that ultimately becomes words, phrases, and sentences. Despite the complex stages involved in this process, relatively little is known about how we avoid and handle production and comprehension errors that would otherwise impede communication. We review current research on the mechanisms underlying monitoring and control of the language system, especially production, with particular emphasis on whether such monitoring is issued by domain-general or domain-specific procedures.
Cannabinol delays symptom onset in SOD1 (G93A) transgenic mice without affecting survival

Therapeutic options for amyotrophic lateral sclerosis (ALS), the most common adult‐onset motor neuron disorder, remain limited. Emerging evidence from clinical studies and transgenic mouse models of ALS suggests that cannabinoids, the bioactive ingredients of marijuana (Cannabis sativa) might have some therapeutic benefit in this disease. However, Δ9‐tetrahydrocannabinol (Δ9‐THC), the predominant cannabinoid in marijuana, induces mind‐altering effects and is partially addictive, compromising its clinical usefulness. We therefore tested whether cannabinol (CBN), a non‐psychotropic cannabinoid, influences disease progression and survival in the SOD1 (G93A) mouse model of ALS. CBN was delivered via subcutaneously implanted osmotic mini‐pumps (5 mg/kg/day) over a period of up to 12 weeks. We found that this treatment significantly delays disease onset by more than two weeks while survival was not affected. Further research is necessary to determine whether non‐psychotropic cannabinoids might be useful in ameliorating symptoms in ALS.
Adaptive MapReduce Similarity Joins

Similarity joins are a fundamental database operation. Given data sets S and R, the goal of a similarity join is to find all points x ∈ S and y ∈ R with distance at most r. Recent research has investigated how locality-sensitive hashing (LSH) can be used for similarity join, and in particular two recent lines of work have made exciting progress on LSH-based join performance. Hu, Tao, and Yi (PODS 17) investigated joins in a massively parallel setting, showing strong results that adapt to the size of the output. Meanwhile, Ahle, Aumüller, and Pagh (SODA 17) showed a sequential algorithm that adapts to the structure of the data, matching classic bounds in the worst case but improving them significantly on more structured data. We show that this adaptive strategy can be adapted to the parallel setting, combining the advantages of these approaches. In particular, we show that a simple modification to Hu et al.'s algorithm achieves bounds that depend on the density of points in the dataset as well as the total outsize of the output. Our algorithm uses no extra parameters over other LSH approaches (in particular, its execution does not depend on the structure of the dataset), and is likely to be efficient in practice.
Translating OpenACC to LLVM IR with SPIR kernels

In general, highly parallelized programs executed on heterogeneous multiprocessor platforms may get better performance than homogeneous ones. OpenCL is one of the standards for parallel programming of heterogeneous multiprocessor platforms and SPIR (Standard Portable Intermediate Representation) is a portable binary format for representing OpenCL kernel code. However, the programming of these programs is usually complex and error-prone for most programmers. Therefore, some standards have been proposed to simplify the programming on heterogeneous multiprocessor platforms, for example, OpenACC (a directive-based parallel programming model). In this paper, we implement our framework on Clang, the C front-end of LLVM, to automatically translate OpenACC to LLVM IR with SPIR kernels. After that, it is optional to optimize the IR code by LLVM optimizer and execute the host LLVM IR by LLVM JIT-compiler. According to the experiment results, our translated programs have significant performance enhancement for some programs while comparing with their corresponding sequential version of programs and have comparable performance while comparing with their manual OpenCL version. Therefore, our design may reduce the difficulty of writing the programs in heterogeneous multiprocessor platform and the translated OpenCL programs are portable and have good performance as that of the manual OpenCL programs written by experienced programmers.
African rice (Oryza glaberrima): History and future potential

The African species of rice (Oryza glaberrima) was cultivated long before Europeans arrived in the continent. At present, O. glaberrima is being replaced by the introduced Asian species of rice, Oryza sativa. Some West African farmers, including the Jola of southern Senegal, still grow African rice for use in ritual contexts. The two species of rice have recently been crossed, producing a promising hybrid.
Robot Personality Design for an Appropriate Response to the Human Partner

—This paper discusses the importance of modeling personality for social robots. While human-liked features (such as voice, gestures, and postures) are well-studied in social robotics, developing robots with personality traits is still very much in its infancy. In this paper, we show and argue the importance of embodying personality in the robot’s behavior so as to provide a more natural interaction and a more appropriate feedback to the human partner.
The Imaging of Inflammatory Bowel Disease: Current Concepts and Future Directions

Radiologic techniques play an integral role in the diagnosis and management of patients with inflammatory bowel disease (IBD). Imaging has long been utilized to evaluate seg‐ ments of the digestive tract that are inaccessible to conventional endoscopy. While endos‐ copy offers unparalleled visualization of the large bowel lumen and biopsy capabilities, the small bowel remains essentially wholly inaccessible by conventional endoscopic techniques [1]. Thus, one important role that imaging plays in the care of patients with IBD is the evalu‐ ation of the small bowel.
Enhanced Retention Performance Modeling for Intelligent Tutoring System

The aim of Personalized Adaptive Scheduling System is not only to maximize learning, but also to minimize the tendency to forget. Empirical studies from psychology and cognitive theory of memory indicate that repetition with gradually increasing spacing interval is necessary to promote optimal long-term retention. While all individuals differ in their capacity to learn and retain information, the existing personalized systems attempt to model student retention performance based on learning capabilities only. The present study aims to investigate the extent to which individual differences in learning and forgetfulness help in predicting retention performance, which is essential for designing personalized retrieval practice schedules for a student. This study was conducted using data from Personalized Adaptive Scheduling System in ASSISTments, an adaptive mathematical tutor. The results illustrate the importance of student learning and forgetfulness features in predicting retention performance of a new skill, the proposed prediction model showed a significant improvement from an R of 0.2975 with an existing baseline model to an R value of 0.3989. In addition the newly identified features are used to predict the retention interval of a student, since ideal personalized retention schedules should be roughly equal to the retention interval of the student. The predictive accuracy of the linear regression model thus obtained was found to be statistically significant with an R value of 0.413.
Social Network Monitoring for Bursty Cascade Detection

Social network services have become important and efficient platforms for users to share all kinds of information. The capability to monitor user-generated information and detect bursts from information diffusions in these social networks brings value to a wide range of real-life applications, such as viral marketing. However, in reality, as a third party, there is always a cost for gathering information from each user or so-called social network sensor. The question then arises how to select a budgeted set of social network sensors to form the data stream for burst detection without compromising the detection performance. In this article, we present a general sensor selection solution for different burst detection approaches. We formulate this problem as a constraint satisfaction problem that has high computational complexity. To reduce the computational cost, we first reduce most of the constraints by making use of the fact that bursty cascades are rare among the whole population. We then transform the problem into an Linear Programming (LP) problem. Furthermore, we use the sub-gradient method instead of the standard simplex method or interior-point method to solve the LP problem, which makes it possible for our solution to scale up to large social networks. Evaluating our solution on millions of real information cascades, we demonstrate both the effectiveness and efficiency of our approach.
A Review on Noise Reduction of Echo Cardiographic Images based on Temporal Information

imaging is inserting a significant role in diagnosis the diseases and in image guided surgery. There are varied imaging modalities for various applications giving the anatomical and physiological conditions of the patient. Noise suppression of echocardiography images may be a difficult issue for correct and effective human interpretation and computer-assisted analysis. In spite of comprehensive speckle reduction ways, up to now there are few studies of de-noising echocardiography sequences supported temporal data. During this article, a quick and correct filter supported temporal data has been projected that permits the reduction of noise in echocardiography images. The projected methodology consists of smoothing intensity variation time curves (IVTC) assessed in every picture element. By filtering high-frequency elements of every temporal signal and so substitution the smooth signals in their positions, all pixels of all frames are often reconstructed during a parallel manner. The presentation of the projected methodology is evaluated and compared with seven alternative speckle-reduction filters. Judgment of the filters is predicated on a series of computer-simulated and real clinical images, and additionally on visual assessment by specialists. The experimental results show that the projected algorithmic rule is quick, less computationally demanding than alternative filters, and correct, additionally to preserving the edges of the images.
Elevated levels of the circulatory ischemia-modified albumin in patients with polycystic ovary syndrome: a meta-analysis

Abstract Oxidative stress (OS) has been reported to be associated with the pathogenesis of polycystic ovary syndrome (PCOS). Ischemia-modified albumin (IMA) levels in the circulation have been recently studied as a novel marker of OS. The studies in the literature on IMA levels in PCOS are inconsistent. This meta-analysis was conducted to compare circulatory IMA levels between PCOS patients and non-PCOS controls. Relevant studies were retrieved by online database and manual searching. The standardized mean differences (SMDs) with 95% confidence intervals (CIs) were obtained by a random-effects meta-analysis. The funnel plot analysis with Begg’s and Egger’s tests was used for publication bias. A total of nine studies were included in this meta-analysis. The results indicated that the serum IMA levels were significantly elevated in PCOS patients as compared to non-PCOS controls (SMD = 0.49, 95% CI = 0.23–0.75, Z = 3.75, p = .0002). A one-study leave-out sensitivity analysis indicated that no single study had a significant influence on the overall outcome, suggesting the good validity and stability of these meta-analytic results. There was no evidence of publication bias as evidenced by the Egger (p = .28) and Begg’s tests (p = .21). The present meta-analysis suggests that IMA might be considered as a reliable and novel marker reflecting increased OS in PCOS.