Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Position Bias in Digital Library Recommender Systems

"Position bias" describes the tendency of users to interact with items on top of a list with higher probability than with items at a lower position in the list, regardless of the items' actual relevance. In the domain of recommender systems, particularly recommender systems in digital libraries, position bias has received little attention. We conduct a study in a real-world recommender system that delivered ten million related-article recommendations to the users of the digital library Sowiport, and the reference manager JabRef. Recommendations were randomly chosen to be shuffled or non-shuffled, and we compared click-through rate (CTR) for each rank of the recommendations. According to our analysis, the CTR for the highest rank in the case of Sowiport is 53% higher than expected in a hypothetical non-biased situation (0.189% vs. 0.123%). Similarly, in the case of Jabref the highest rank received a CTR of 1.276%, which is 87% higher than expected (0.683%). A chi-squared test confirms the strong relationship between the rank of the recommendation shown to the user and whether the user decided to click it (p < 0.01 for both Jabref and Sowiport). Our study confirms the findings from other domains, that recommendations in the top positions are more often clicked, regardless of their actual relevance.
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization

We study the estimation of the latent variable Gaussian graphical model (LVGGM), where the precision matrix is the superposition of a sparse matrix and a low-rank matrix. In order to speed up the estimation of the sparse plus low-rank components, we propose a sparsity constrained maximum likelihood estimator based on matrix factorization and an efficient alternating gradient descent algorithm with hard thresholding to solve it. Our algorithm is orders of magnitude faster than the convex relaxation based methods for LVGGM. In addition, we prove that our algorithm is guaranteed to linearly converge to the unknown sparse and low-rank components up to the optimal statistical precision. Experiments on both synthetic and genomic data demonstrate the superiority of our algorithm over the state-of-the-art algorithms and corroborate our theory.
Presence of the metabolic syndrome is associated with shorter time to castration-resistant prostate cancer.

BACKGROUND Metabolic syndrome (MS) is a set of risk factors that includes obesity and insulin resistance and has been implicated in the development of prostate cancer. Its impact on androgen deprivation therapy (ADT) efficacy has not been studied. PATIENTS AND METHODS Retrospective study of prostate cancer patients seen from 1998 to 2005 in a medical oncology clinic. MS, as defined by modified Adult Treatment Panel III criteria, was assessed at the time of initiation of ADT. The study end points were time to prostate-specific antigen (PSA) progression and overall survival (OS) from time of starting ADT. RESULTS Eighty-two patients treated with ADT and data to assess for presence of MS were identified. Median age in men with and without MS was 70 years and 49% of the patients evaluated met criteria for MS. Median time to PSA progression for patients with MS was 16 versus 36 months without MS (P=0.003). The median OS for patients with MS was 36.5 months after commencing ADT compared with 46.7 months for those patients without MS (P=0.061). CONCLUSIONS This preliminary data suggest that MS is a risk factor for earlier development of castration-resistant prostate cancer and support the need for a prospective evaluation of this finding.
The Chikungunya Epidemic on La Réunion Island in 2005–2006: A Cost-of-Illness Study

Background This study was conducted to assess the impact of chikungunya on health costs during the epidemic that occurred on La Réunion in 2005–2006. Methodology/Principal Findings From data collected from health agencies, the additional costs incurred by chikungunya in terms of consultations, drug consumption and absence from work were determined by a comparison with the expected costs outside the epidemic period. The cost of hospitalization was estimated from data provided by the national hospitalization database for short-term care by considering all hospital stays in which the ICD-10 code A92.0 appeared. A cost-of-illness study was conducted from the perspective of the third-party payer. Direct medical costs per outpatient and inpatient case were evaluated. The costs were estimated in Euros at 2006 values. Additional reimbursements for consultations with general practitioners and drugs were estimated as €12.4 million (range: €7.7 million–€17.1 million) and €5 million (€1.9 million–€8.1 million), respectively, while the cost of hospitalization for chikungunya was estimated to be €8.5 million (€5.8 million–€8.7 million). Productivity costs were estimated as €17.4 million (€6 million–€28.9 million). The medical cost of the chikungunya epidemic was estimated as €43.9 million, 60% due to direct medical costs and 40% to indirect costs (€26.5 million and €17.4 million, respectively). The direct medical cost was assessed as €90 for each outpatient and €2,000 for each inpatient. Conclusions/Significance The medical management of chikungunya during the epidemic on La Réunion Island was associated with an important economic burden. The estimated cost of the reported disease can be used to evaluate the cost/efficacy and cost/benefit ratios for prevention and control programmes of emerging arboviruses.
Monitoring, Prediction and Prevention of SLA Violations in Composite Services

We propose the PREvent framework, which is a system that integrates event-based monitoring, prediction of SLA violations using machine learning techniques, and automated runtime prevention of those violations by triggering adaptation actions in service compositions. PREvent improves on related work in that it can be used to prevent violations ex ante, before they have negatively impacted the provider's SLAs. We explain PREvent in detail and show the impact on SLA violations based on a case study.
SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles

Recent advances in single cell transcriptional profiling open up a new avenue in studying the functional role of cell-to-cell variability in physiological processes such as stem cell differentiation. In this work, we developed a novel algorithm called SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS), for the inference of gene regulatory networks (GRNs) from single cell transcriptional expression data. In particular, we focused on time-stamped cross-sectional expression data, a common type of dataset generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers the regulatory (causal) relationships among genes by employing regularized linear regression, particularly ridge regression, using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using simulated time-stamped in silico single cell expression data and single transcriptional profiling of THP-1 monocytic human leukemia cell differentiation. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Meanwhile, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality.
ACQUISITION OF DUTCH AS A SECOND LANGUAGE

This study reports on the impact of 11 West European first languages on the acquisition of Dutch. Using data from nearly 6,000 second-language learners, it was found that the mother tongue had a rather large impact on two language skills—namely, oral and written proficiency—as measured by the scores received by these learners on the State Examination of Dutch as a Second Language. Multilevel analyses showed that the effect of the mother tongue can adequately be modeled by means of the cognate linguistic distance measure, adopted from McMahon and McMahon (2005). The explanative power of the genetic linguistic distance measure (Cavalli-Sforza, Menozzi, & Piazza, 1994), on the other hand, was rather poor. Additionally, learner characteristics (age of arrival, length of residence, hours studying Dutch, education, and gender) and context characteristics (quality of schooling in the country of origin and multilingual country of origin) explained part of the variation in Dutch speaking and writing skills.
Multi-Task Learning For Parsing The Alexa Meaning Representation Language

The Alexa Meaning Representation Language (AMRL) is a compositional graph-based semantic representation that includes fine-grained types, properties, actions, and roles and can represent a wide variety of spoken language. AMRL increases the ability of virtual assistants to represent more complex requests, including logical and conditional statements as well as ones with nested clauses. Due to this representational capacity, the acquisition of large scale data resources is challenging, which limits the accuracy of resulting models. This paper has two primary contributions. First, we develop a linearization of AMRL graphs along with a deep multi-task model that predicts fine-grained types, properties, and intents. Second, we show how to jointly train a model that predicts an existing representation for spoken language understanding (SLU) along with the linearized AMRL parse. The resulting model, which leverages learned embeddings from both tasks, is able to predict the AMRL representation more accurately than other approaches, decreasing the error rates in the full parse by 3.56% absolute and reducing the amount of natively annotated data needed to train accurate parsing models.
A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high arctic plant productivity

Efforts to estimate plant productivity using satellite data can be frustrated by the presence of cloud cover. We developed a new method to overcome this problem, focussing on the high-arctic archipelago of Svalbard where extensive cloud cover during the growing season can prevent plant productivity from being estimated over large areas. We used a field-based time-series (2000−2009) of live aboveground vascular plant biomass data and a recently processed cloud-free MODIS-Normalised Difference Vegetation Index (NDVI) data set (2000−2014) to estimate, on a pixel-by-pixel basis, the onset of plant growth. We then summed NDVI values from onset of spring to the average time of peak NDVI to give an estimate of annual plant productivity. This remotely sensed productivity measure was then compared, at two different spatial scales, with the peak plant biomass field data. At both the local scale, surrounding the field data site, and the larger regional scale, our NDVI measure was found to predict plant biomass (adjusted R2 = 0.51 and 0.44, respectively). The commonly used ‘maximum NDVI’ plant productivity index showed no relationship with plant biomass, likely due to some years having very few cloud-free images available during the peak plant growing season. Thus, we propose this new summed NDVI from onset of spring to time of peak NDVI as a proxy of large-scale plant productivity for regions such as the Arctic where climatic conditions restrict the availability of cloud-free images.
Pollen morphology of extant Winteraceae: a study allowing SEM-based affiliation of its fossil representatives

Abstract When applying high-resolution microscopy, the pollen morphology of extant taxa can be used to classify fossil pollen, that is, to address the latter in the established systematic-phylogenetic framework. Here we investigate tetrads and pollen features of 20 different Winteraceae species, most of them belonging to the early-diverging generic lineages Tasmannia, Drimys and Pseudowintera. The tetrads and pollen are grouped into eleven pollen types based on diagnostic features observed by both light and scanning electron microscopy. The high-resolution scanning electron micrographs of recent material allow detailed comparison with fossil material, resulting in a more accurate affiliation of fossil tetrads/pollen to extant lineages. As a case study, early Miocene Winteraceae tetrads from South Africa are re-examined and formally described. The systematic placement of the African fossils is discussed in light of the pollen types presented here.
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis

Collaborative Filtering(CF)-based recommendation algorithms, such as Latent Factor Models (LFM), work well in terms of prediction accuracy. However, the latent features make it difficulty to explain the recommendation results to the users. Fortunately, with the continuous growth of online user reviews, the information available for training a recommender system is no longer limited to just numerical star ratings or user/item features. By extracting explicit user opinions about various aspects of a product from the reviews, it is possible to learn more details about what aspects a user cares, which further sheds light on the possibility to make explainable recommendations. In this work, we propose the Explicit Factor Model (EFM) to generate explainable recommendations, meanwhile keep a high prediction accuracy. We first extract explicit product features (i.e. aspects) and user opinions by phrase-level sentiment analysis on user reviews, then generate both recommendations and disrecommendations according to the specific product features to the user's interests and the hidden features learned. Besides, intuitional feature-level explanations about why an item is or is not recommended are generated from the model. Offline experimental results on several real-world datasets demonstrate the advantages of our framework over competitive baseline algorithms on both rating prediction and top-K recommendation tasks. Online experiments show that the detailed explanations make the recommendations and disrecommendations more influential on user's purchasing behavior.
The Sentimental Value of Chinese Sub-Character Components

Sub-character components of Chinese characters carry important semantic information, and recent studies have shown that utilizing this information can improve performance on core semantic tasks. In this paper, we hypothesize that in addition to semantic information, sub-character components may also carry emotional information, and that utilizing it should improve performance on sentiment analysis tasks. We conduct a series of experiments on four Chinese sentiment data sets and show that we can significantly improve the performance in various tasks over that of a character-level embeddings baseline. We then focus on qualitatively assessing multiple examples and trying to explain how the sub-character components affect the results in each case.
ApriISAM: Real-Time Smoothing and Mapping

For online robots, incremental SLAM algorithms offer huge potential computational savings over batch algorithms. The dominant incremental algorithms are iSAM and iSAM2 which offer radically different approaches to computing incremental updates, balancing issues like 1) the need to re-linearize, 2) changes in the desirable variable marginalization order, and 3) the underlying conceptual approach (i.e. the “matrix” story versus the “factor graph” story). In this paper, we propose a new incremental algorithm that computes solutions with lower absolute error and generally provides lower error solutions for a fixed computational budget than either iSAM or iSAM2. Key to AprilSAM's performance are a new dynamic variable reordering algorithm for fast incremental Cholesky factorizations, a method for reducing the work involved in backsubstitutions, and a new algorithm for deciding between incremental and batch updates.
Online model estimation of ultra-wideband TDOA measurements for mobile robot localization

Ultra-wideband (UWB) localization is a recent technology that promises to outperform many indoor localization methods currently available. Yet, non-line-of-sight (NLOS) positioning scenarios can create large biases in the time-difference-of-arrival (TDOA) measurements, and must be addressed with accurate measurement models in order to avoid significant localization errors. In this work, we first develop an efficient, closed-form TDOA error model and analyze its estimation characteristics by calculating the Cramér-Rao lower bound (CRLB). We subsequently detail how an online Expectation Maximization (EM) algorithm is adopted to find an elegant formalism for the maximum likelihood estimate of the model parameters. We perform real experiments on a mobile robot equipped with an UWB emitter, and show that the online estimation algorithm leads to excellent localization performance due to its ability to adapt to the varying NLOS path conditions over time.
Self-Signaling and the Costs and Benefits of Temptation in Consumer Choice

The literature on self-control emphasizes that temptation is costly. The authors propose that temptation entails not only costs but also benefits for consumers. These arise from self-signaling effects of how consumers handle tempting choice options. Succumbing to temptation is a (costly) self-signal of weak willpower, whereas resisting temptation is a (beneficial) self-signal of strong willpower. Five experiments demonstrate that these self-signaling costs and benefits of temptation depend not only on the chosen item but also on the temptation from the nonchosen options. The authors discuss theoretical implications of their findings for research on impulsive choice and self-control and on self-signaling and managerial implications for pricing and assortment strategies.
Problematic variation in local institutional review of a multicenter genetic epidemiology study.

CONTEXT Sequencing of the human genome provides an immense resource for studies correlating DNA variation and epidemiology. However, appropriately powered genetic epidemiology studies often require recruitment from multiple sites. OBJECTIVES To document the burden imposed by review of multicenter studies and to determine the variability among local institutional review boards (IRBs) in the approval of a multicenter genetic epidemiology study. DESIGN A PubMed search was performed to determine the frequency of citations of multicenter studies by 5-year intervals from 1974 through 2002. A 7-question survey was sent to all participating study centers to obtain information on frequency of IRB meetings, dates for submission and approval, use/nonuse of a specific consent form, type of review performed, types of consent forms required, preparation time, and number of changes requested by the IRB at each center. Centers also provided a copy of all consent forms they generated and IRB correspondence regarding the study. SETTING AND PARTICIPANTS Thirty-one of 42 cystic fibrosis care centers in this single US multicenter genetic epidemiology study of cystic fibrosis replied, yielding a 74% response rate. MAIN OUTCOME MEASURES Frequency of published research studies and consistency among IRBs. RESULTS The number of all published single-center studies has increased 1.3-fold since 1985, while the number of published epidemiology and genetic epidemiology multicenter studies increased by 8- and 9-fold, respectively, during this same period. Evaluation of the risk of the same genetic epidemiology study by 31 IRBs ranged from minimal to high, resulting in 7 expedited reviews (23%) and 24 full reviews (77%). The number of consents required by the IRBs ranged from 1 to 4; 15 IRBs (48%) required 2 or more consents, while 10 (32%) did not require assent for children. The most common concern (52%) of IRBs pertained to the genetic aspects of the study. CONCLUSIONS Review of a protocol for a multicenter genetic epidemiology study by local IRBs was highly variable. Lack of uniformity in the review process creates uneven human subjects protection and incurs considerable inefficiency. The need for reform, such as the proposed centralized review, is underscored by the ever increasing rate of genetic discoveries facilitated by the Human Genome Project and the unprecedented opportunity to assess the relevance of genetic variation to public health.
DeepLoco

Learning physics-based locomotion skills is a difficult problem, leading to solutions that typically exploit prior knowledge of various forms. In this paper we aim to learn a variety of environment-aware locomotion skills with a limited amount of prior knowledge. We adopt a two-level hierarchical control framework. First, low-level controllers are learned that operate at a fine timescale and which achieve robust walking gaits that satisfy stepping-target and style objectives. Second, high-level controllers are then learned which plan at the timescale of steps by invoking desired step targets for the low-level controller. The high-level controller makes decisions directly based on high-dimensional inputs, including terrain maps or other suitable representations of the surroundings. Both levels of the control policy are trained using deep reinforcement learning. Results are demonstrated on a simulated 3D biped. Low-level controllers are learned for a variety of motion styles and demonstrate robustness with respect to force-based disturbances, terrain variations, and style interpolation. High-level controllers are demonstrated that are capable of following trails through terrains, dribbling a soccer ball towards a target location, and navigating through static or dynamic obstacles.
Delay Dependent Anti-windup Synthesis for Time-varying Delay Systems with Saturating Actuators

The synthesis of an anti-windup compensator for systems with time-varying delays is solved in this paper. More precisely, LMI conditions are proposed that guarantee the stability of the closedloop system, with a enlarged domain of attraction. An ellipsoid and a polyhedral set are used to bound this domain of attraction, making possible to derive a new sector condition. In addition, an algorithm is provided to optimize the anti-windup gain, reducing the conservatism. Numerical examples illustrate the effectiveness of our methodology, which improves previous works.
Functional Elastic Knits Made of Bamboo Charcoal and Quick-Dry Yarns: Manufacturing Techniques and Property Evaluations

Conventional sportswear fabrics are functional textiles that can mitigate the impaired muscles caused by exercises for the wearers, but they can also cause discomfort and skin allergy. This study proposes combining two yarns to form functional composite yarns, by using a twisting or wrapping process. Moreover, a different twist number is used in order to adjust the performance of functional composite yarns. A crochet machine is used to make the functional composite yarns into functional elastic knits that are suitable for use in sportswear. The test results show that, in comparison to the non-processed yarns, using the twisted or wrapped yarns can considerably decrease the water vapor transmission rate of functional elastic knits by 38%, while also improving their far infrared emissivity by 13%, water absorption rate by 39%, and air permeability by 136%. In particular, the functional elastic knits that are made of B-wrapped yarns (bamboo charcoal- wrapped yarns), composed of 20 twists per inch, have the optimal diverse functions.