Miracle-dz/UniFAR-FTUs huggingface dataset:
Inventory Balancing with Online Learning

We study a general problem of allocating limited resources to heterogeneous customers over time under model uncertainty. Each type of customer can be serviced using different actions, each of which stochastically consumes some combination of resources and returns different rewards for the resources consumed. We consider a general model in which the resource consumption distribution associated with each customer type–action combination is not known but is consistent and can be learned over time. In addition, the sequence of customer types to arrive over time is arbitrary and completely unknown. We overcome both the challenges of model uncertainty and customer heterogeneity by judiciously synthesizing two algorithmic frameworks from the literature: inventory balancing, which “reserves” a portion of each resource for high-reward customer types that could later arrive based on competitive ratio analysis, and online learning, which “explores” the resource consumption distributions for each customer type under different actions based on regret analysis. We define an auxiliary problem, which allows for existing competitive ratio and regret bounds to be seamlessly integrated. Furthermore, we propose a new variant of upper confidence bound (UCB), dubbed lazyUCB, which conducts less exploration in a bid to focus on “exploitation” in view of the resource scarcity. Finally, we construct an information-theoretic family of counterexamples to show that our integrated framework achieves the best possible performance guarantee. We demonstrate the efficacy of our algorithms on both synthetic instances generated for the online matching with stochastic rewards problem under unknown probabilities and a publicly available hotel data set. Our framework is highly practical in that it requires no historical data (no fitted customer choice models or forecasting of customer arrival patterns) and can be used to initialize allocation strategies in fast-changing environments. This paper was accepted by J. George Shanthikumar, special issue on data-driven prescriptive analytics.
Ameliorating Episodic Memory Deficits in a Young Adult With Developmental (Congenital) Amnesia

Abstract Objectives: Although the spacing effect has been investigated extensively in a variety of populations, few studies have focused on individuals with hippocampal amnesia and none, to our knowledge, have investigated differences in performance as a function of spacing schedule in these cases. In the current study, we investigated the benefit of expanding and equal-interval, compared to massed, spacing schedules in a developmental amnesic person, H.C. , who shows congenitally based abnormal development of the hippocampal memory system. Methods: Given the possibility of plasticity and reorganization in the developing brain, we investigated whether H.C. would benefit more from an expanding versus equal-interval schedule using a continuous recognition paradigm, even though this task has been shown to recruit structures within the medial temporal lobe, including the hippocampus. Results: H.C. and matched controls both showed a clear spacing effect, although neither group benefited more from an equal-interval or expanding spacing schedule. Conclusions: The results of the current study show that the spacing effect is an effective and clinically meaningful memory intervention technique that may be applied to clinical conditions known to affect hippocampal function and episodic memory early in life. (JINS, 2018, 24, 1003–1012)
Integrated B Cell, Toll-like, and BAFF Receptor Signals Promote Autoantibody Production by Transitional B Cells

The B cell survival cytokine BAFF has been linked with the pathogenesis of systemic lupus erythematosus (SLE). BAFF binds distinct BAFF-family surface receptors, including the BAFF-R and transmembrane activator and CAML interactor (TACI). Although originally characterized as a negative regulator of B cell activation, TACI signals are critical for class-switched autoantibody (autoAb) production in BAFF transgenic mice. Consistent with this finding, a subset of transitional splenic B cells upregulate surface TACI expression and contribute to BAFF-driven autoAb. In the current study, we interrogated the B cell signals required for transitional B cell TACI expression and Ab production. Surprisingly, despite established roles for dual BCR and TLR signals in autoAb production in SLE, signals downstream of these receptors exerted distinct impacts on transitional B cell TACI expression and autoAb titers. Whereas loss of BCR signals prevented transitional B cell TACI expression and resulted in loss of serum autoAb across all Ig isotypes, lack of TLR signals exerted a more limited impact restricted to autoAb class-switch recombination without altering transitional B cell TACI expression. Finally, in parallel with the protective effect of TACI deletion, loss of BAFF-R signaling also protected against BAFF-driven autoimmunity. Together, these findings highlight how multiple signaling pathways integrate to promote class-switched autoAb production by transitional B cells, events that likely impact the pathogenesis of SLE and other BAFF-dependent autoimmune diseases.
The Use of Frequency Lowering Technology in the Treatment of Severe-to-Profound Hearing Loss: A Review of the Literature and Candidacy Considerations for Clinical Application

Abstract This article provides a review of the current literature on the topic of frequency lowering hearing aid technology specific to the treatment of severe and profound levels of hearing impairment in child and adult listeners. Factors to consider when assessing listener candidacy for frequency lowering technology are discussed. These include factors related to audiometric assessment, the listener, the type of hearing aid technology, and the verification and validation procedures that can assist in determining candidacy for frequency lowering technology. An individualized candidacy assessment including the use of real-ear verification measures and carefully chosen validation tools are recommended for listeners requiring greater audibility of high-frequency sounds, when compared with amplification via conventional hearing aid technology.
A Randomized Controlled Trial of Adherence to a 24-Month Home-Based Physical Activity Program and the Health Benefits for Older Adults at Risk of Alzheimer’s Disease: The AIBL Active-Study

Background: Previous studies have demonstrated that physical activity (PA) interventions can improve physical and cognitive outcomes in older adults, but most have been relatively short in duration (<1 year) with a few having specifically targeting individuals at risk of developing Alzheimer’s disease. Objective: To examine adherence and physical health outcomes in a 24-month home-based PA intervention in older adults at risk of Alzheimer’s disease. Methods: Participants 60 years and older with mild cognitive impairment (MCI) or subjective memory complaints (SMC) with at least 1 cerebrovascular risk factor recruited from The Australian Imaging Biomarkers and Lifestyle Flagship Study of Aging (AIBL) were randomized to a PA or control group (n = 106). The control group continued with their usual lifestyle. The PA group received a 24-month home-based program with a target of 150 minutes/week of moderate PA and a behavioral intervention. Retention (participants remaining) and PA adherence (PA group only, percent PA completed to the PA prescribed) were determined at 6, 12, 18, and 24 months. Assessments at baseline, 6, 12, and 24 months included, PA; fitness; body composition and fat distribution. Key outcome measures were PA adherence and PA. Results: The 24-month retention rate (97.2%) and the median PA adherence 91.67% (Q1–Q3, 81.96, 100.00) were excellent. In the long-term the intervention group achieved significantly better improvements in PA levels, leg strength, fat mass and fat distribution compared to the control. Conclusion: This study demonstrates that in this target group, long-term PA adherence is achievable and has physical health benefits.
Spatial-temporal clustering analysis of yaws on Lihir Island, Papua New Guinea to enhance planning and implementation of eradication programs

Background In the global program for the eradication of yaws, assessments of the prevalence of the disease are used to decide where to initiate mass treatment. However, the smallest administrative unit which should be used as the basis for making decisions is not clear. We investigated spatial and temporal clustering of yaws to help inform the choice of implementation unit. Methodology/Principal findings We analyzed 11 years of passive surveillance data on incident yaws cases (n = 1448) from Lihir Island, Papua New Guinea. After adjusting for age, sex, and trends in health-seeking, we detected three non-overlapping spatiotemporal clusters (p < 1 × 10−17, p = 1.4 × 10−14, p = 1.4 × 10−8). These lasted from 28 to 47 months in duration and each encompassed between 4 and 6 villages. We also assessed spatial clustering of prevalent yaws cases (n = 532) that had been detected in 7 biannual active case finding surveys beginning in 2013. We identified 1 statistically significant cluster in each survey. We considered the possibility that schools that serve multiple villages might be loci of transmission, but we found no evidence that incident cases of yaws among 8- to 14-year-olds clustered within primary school attendance areas (p = 0.684). Conclusions/Significance These clusters likely reflect transmission of yaws across village boundaries; villages may be epidemiologically linked to a degree such that mass drug administration may be more effectively implemented at a spatial scale larger than the individual village. Author Summary The World Health Organization aims to eradicate yaws using mass drug administration (MDA), which consists of treating everyone in an administrative unit with antibiotics. The administrative unit in a country which is used as the basis for making decisions about implementing MDA is called the implementation unit. Prevalence assessments are used to identify endemic communities for mass treatment programs, but the spatial scale (e.g. village, sub-district, district, or province) at which mass treatment should be implemented is currently unclear. The choice of implementation unit depends on many factors; one of these is the underlying transmission patterns of the disease. Using data from Lihir Island, Papua New Guinea, we found that geographic clusters of yaws often spanned multiple villages. These clusters likely reflect transmission of the infectious disease across village boundaries and suggest that it may be best to implement MDA at a spatial scale larger than the individual village, for example at sub-district level.
SAFE-PDF: Robust Detection of JavaScript PDF Malware Using Abstract Interpretation

The popularity of the PDF format and the rich JavaScript environment that PDF viewers offer make PDF documents an attractive attack vector for malware developers. PDF documents present a serious threat to the security of organizations because most users are unsuspecting of them and thus likely to open documents from untrusted sources. We propose to identify malicious PDFs by using conservative abstract interpretation to statically reason about the behavior of the embedded JavaScript code. Currently, state-of-the-art tools either: (1) statically identify PDF malware based on structural similarity to known malicious samples; or (2) dynamically execute the code to detect malicious behavior. These two approaches are subject to evasion attacks that mimic the structure of benign documents or do not exhibit their malicious behavior when being analyzed dynamically. In contrast, abstract interpretation is oblivious to both types of evasions. A comparison with two state-of-the-art PDF malware detection tools shows that our conservative abstract interpretation approach achieves similar accuracy, while being more resilient to evasion attacks.
Learning to Adaptively Rank Document Retrieval System Configurations

Modern Information Retrieval (IR) systems have become more and more complex, involving a large number of parameters. For example, a system may choose from a set of possible retrieval models (BM25, language model, etc.), or various query expansion parameters, whose values greatly influence the overall retrieval effectiveness. Traditionally, these parameters are set at a system level based on training queries, and the same parameters are then used for different queries. We observe that it may not be easy to set all these parameters separately, since they can be dependent. In addition, a global setting for all queries may not best fit all individual queries with different characteristics. The parameters should be set according to these characteristics. In this article, we propose a novel approach to tackle this problem by dealing with the entire system configurations (i.e., a set of parameters representing an IR system behaviour) instead of selecting a single parameter at a time. The selection of the best configuration is cast as a problem of ranking different possible configurations given a query. We apply learning-to-rank approaches for this task. We exploit both the query features and the system configuration features in the learning-to-rank method so that the selection of configuration is query dependent. The experiments we conducted on four TREC ad hoc collections show that this approach can significantly outperform the traditional method to tune system configuration globally (i.e., grid search) and leads to higher effectiveness than the top performing systems of the TREC tracks. We also perform an ablation analysis on the impact of different features on the model learning capability and show that query expansion features are among the most important for adaptive systems.
The AB loop of oncostatin M (OSM) determines species-specific signaling in humans and mice

The pleiotropic interleukin-6 (IL-6)–type cytokine oncostatin M (OSM) signals in multiple cell types, affecting processes such as cell differentiation, hematopoiesis, and inflammation. In humans, OSM exerts its effects through activation of either of two different heterodimeric receptor complexes, formed by glycoprotein 130 (gp130) and either OSM receptor (OSMR) or leukemia inhibitory factor receptor (LIFR). In contrast, the mouse OSM orthologue acts mainly through dimers containing OSMR and gp130 and shows limited activity through mouse LIFR. Despite their structural similarity, neither human nor mouse OSM signal through the other species' OSMR. The molecular basis for such species-specific signaling, however, remains poorly understood. To identify key molecular features of OSM that determine receptor activation in humans and mice, we generated chimeric mouse-human cytokines. Replacing regions within binding site III of murine OSM with the human equivalents showed that the cytokine's AB loop was critical for receptor selection. Substitutions of individual amino acids within this region demonstrated that residues Asn-37, Thr-40, and Asp-42 of the murine cytokine were responsible for limited LIFR activation and absence of human OSMR/LIFR signaling. In human OSM, Lys-44 appeared to be the main residue preventing mouse OSMR activation. Our data reveal that individual amino acids within the AB loop of OSM determine species-specific activities. These mutations might reflect a key step in the evolutionary process of this cytokine, in which receptor promiscuity gives way to ligand-receptor specialization.
Architectural exploration of heterogeneous memory systems

Heterogeneous systems appear as a viable design alternative for the dark silicon era. In this paradigm, a processor chip includes several different technological alternatives for implementing a certain logical block (e.g., core, on-chip memories) which cannot be used at the same time due to power constraints. The programmer and compiler are then responsible for selecting which of the alternatives should be used for maximizing performance and/or energy efficiency for a given application. This paper presents an initial approach for the exploration of different technological alternatives for the implementation of on-chip memories. It hinges on a linear programming-based model for theoretically comparing the performance offered by the available alternatives, namely SRAM and STT-RAM scratchpads or caches. Experimental results using a cycle-accurate simulation tool confirm that this is a viable model for implementation into production compilers.
Pedestrian Dominance Modeling for Socially-Aware Robot Navigation

We present a Pedestrian Dominance Model (PDM) to identify the dominance characteristics of pedestrians for robot navigation. Through a perception study on a simulated dataset of pedestrians, PDM models the perceived dominance levels of pedestrians with varying motion behaviors corresponding to trajectory, speed, and personal space. At runtime, we use PDM to identify the dominance levels of pedestrians to facilitate socially-aware navigation for the robots. PDM can predict dominance levels from trajectories with $\sim 85$% accuracy. Prior studies in psychology literature indicate that when interacting with humans, people are more comfortable around people that exhibit complementary movement behaviors. Our algorithm leverages this by enabling the robots to exhibit complementing responses to pedestrian dominance. We also present an application of PDM for generating dominance-based collision-avoidance behaviors in the navigation of autonomous vehicles among pedestrians. We demonstrate the benefits of our algorithm for robots navigating among tens of pedestrians in simulated environments.
Subword Semantic Hashing for Intent Classification on Small Datasets

In this paper, we introduce the use of Semantic Hashing as embedding for the task of Intent Classification and achieve state-of-the-art performance on three frequently used benchmarks. Intent Classification on a small dataset is a challenging task for data-hungry state-of-the-art Deep Learning based systems. Semantic Hashing is an attempt to overcome such a challenge and learn robust text classification. Current word embedding based methods [11], [13], [14] are dependent on vocabularies. One of the major drawbacks of such methods is out-of-vocabulary terms, especially when having small training datasets and using a wider vocabulary. This is the case in Intent Classification for chatbots, where typically small datasets are extracted from internet communication. Two problems arise with the use of internet communication. First, such datasets miss a lot of terms in the vocabulary to use word embeddings efficiently. Second, users frequently make spelling errors. Typically, the models for intent classification are not trained with spelling errors and it is difficult to think about ways in which users will make mistakes. Models depending on a word vocabulary will always face such issues. An ideal classifier should handle spelling errors inherently. With Semantic Hashing, we overcome these challenges and achieve state-of-the-art results on three datasets: Chatbot, Ask Ubuntu, and Web Applications [3]. Our benchmarks are available online.1
Big Data Meet Cyber-Physical Systems: A Panoramic Survey

The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world via creating new services and applications in a variety of sectors, such as environmental monitoring, mobile-health systems, intelligent transportation systems, and so on. The information and communication technology sector is experiencing a significant growth in data traffic, driven by the widespread usage of smartphones, tablets, and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. It is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy via providing a broad overview of data collection, storage, access, processing, and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS requires cybersecurity to protect them against malicious attacks and unauthorized intrusion, which become a challenge with the enormous amount of data that are continuously being generated in the network. Thus, we also provide an overview of the different security solutions proposed for CPS big data storage, access, and analytics. We also discuss big data meeting green challenges in the contexts of CPS.
Robust and Efficient CPU-Based RGB-D Scene Reconstruction

3D scene reconstruction is an important topic in computer vision. A complete scene is reconstructed from views acquired along the camera trajectory, each view containing a small part of the scene. Tracking in textureless scenes is well known to be a Gordian knot of camera tracking, and how to obtain accurate 3D models quickly is a major challenge for existing systems. For the application of robotics, we propose a robust CPU-based approach to reconstruct indoor scenes efficiently with a consumer RGB-D camera. The proposed approach bridges feature-based camera tracking and volumetric-based data integration together and has a good reconstruction performance in terms of both robustness and efficiency. The key points in our approach include: (i) a robust and fast camera tracking method combining points and edges, which improves tracking stability in textureless scenes; (ii) an efficient data fusion strategy to select camera views and integrate RGB-D images on multiple scales, which enhances the efficiency of volumetric integration; (iii) a novel RGB-D scene reconstruction system, which can be quickly implemented on a standard CPU. Experimental results demonstrate that our approach reconstructs scenes with higher robustness and efficiency compared to state-of-the-art reconstruction systems.
(Self-Attentive) Autoencoder-based Universal Language Representation for Machine Translation

Universal language representation is the holy grail in machine translation (MT). Thanks to the new neural MT approach, it seems that there are good perspectives towards this goal. In this paper, we propose a new architecture based on combining variational autoencoders with encoder-decoders and introducing an interlingual loss as an additional training objective. By adding and forcing this interlingual loss, we are able to train multiple encoders and decoders for each language, sharing a common universal representation. Since the final objective of this universal representation is producing close results for similar input sentences (in any language), we propose to evaluate it by encoding the same sentence in two different languages, decoding both latent representations into the same language and comparing both outputs. Preliminary results on the WMT 2017 Turkish/English task shows that the proposed architecture is capable of learning a universal language representation and simultaneously training both translation directions with state-of-the-art results.
The Verbal Fluency Decline After Deep Brain Stimulation in Parkinson's Disease: Is There an Influence of Age?

DBS is commonly used to treat Parkinson's disease (PD). DBS is not considered to cause major cognitive side effects, but some research groups have reported that it can cause decreased verbal fluency. The influence of age on DBS cognitive outcome is unclear. We investigated the possible influence of patients' age, level of education, disease duration, disease progression, depression, and levodopa equivalent dose (LED) on verbal fluency performance in patients with PD who underwent DBS of the subthalamic nucleus (STN‐DBS). In this article, we investigated the influence of demographic and clinical parameters, especially age, on cognitive performance post‐DBS in PD patients.