Miracle-dz/UniFAR-FTUs huggingface dataset:
Working Memory Capacity as a Predictor of Cognitive Training Efficacy in the Elderly Population

Aging is associated with a decline in a wide range of cognitive functions and working memory (WM) deterioration is considered a main factor contributing to this. Therefore, any attempt to counteract WM decline seems to have a potential benefit for older adults. However, determination of whether such methods like WM trainings are effective is a subject of a serious debate in the literature. Despite a substantial number of training studies and several meta-analyses, there is no agreement on the matter of their effectiveness. The other important and still not fully explored issue is the impact of the preexisting level of intellectual functioning on the training’s outcome. In our study we investigated the impact of WM training on variety of cognitive tasks performance among older adults and the impact of the initial WM capacity (WMC) on the training efficiency. 85 healthy older adults (55–81 years of age; 55 female, 30 males) received 5 weeks of training on adaptive dual N-back task (experimental group) or memory quiz (active controls). Cognitive performance was assessed before and after intervention with measures of WM, memory updating, inhibition, attention shifting, short-term memory (STM) and reasoning. We found post-intervention group independent improvements across all cognitive tests except for inhibition and STM. With multi-level analysis individual learning curves were modeled, which enabled examining of the intra-individual change in training and inter-individual differences in intra-individual changes. We observed a systematic and positive, but relatively small, learning trend with time. Moderator analyses with demographic characteristics as moderators showed no additional effects on learning curves. Only initial WMC level was a significant moderator of training effectiveness. Older adults with initially lower WMC improved less and reached lower levels of performance, compared to the group with higher WMC. Overall, our findings are in accordance with the research suggesting that post-training gains are within reach of older adults. Our data provide evidence supporting the presence of transfer after N-back training in older adults. More importantly, our findings suggest that it is more important to take into account an initial WMC level, rather than demographic characteristics when evaluating WM training in older adults.
Fast and Flexible Multi-Step Cloth Manipulation Planning Using an Encode-Manipulate-Decode Network (EM*D Net)

We propose a deep neural network architecture, the Encode-Manipulate-Decode (EM*D) net, for rapid manipulation planning on deformable objects. We demonstrate its effectiveness on simulated cloth. The net consists of 3D convolutional encoder and decoder modules that map cloth states to and from latent space, with a “manipulation module” in between that learns a forward model of the cloth's dynamics w.r. t. the manipulation repertoire, in latent space. The manipulation module's architecture is specialized for its role as a forward model, iteratively modifying a state representation by means of residual connections and repeated input at every layer. We train the network to predict the post-manipulation cloth state from a pre-manipulation cloth state and a manipulation input. By training the network end-to-end, we force the encoder and decoder modules to learn a latent state representation that facilitates modification by the manipulation module. We show that the network can achieve good generalization from a training dataset of 6,000 manipulation examples. Comparative experiments without the architectural specializations of the manipulation module show reduced performance, confirming the benefits of our architecture. Manipulation plans are generated by performing error back-propagation w.r. t. the manipulation inputs. Recurrent use of the manipulation network during planning allows for generation of multi-step plans. We show results for plans of up to three manipulations, demonstrating generally good approximation of the goal state. Plan generation takes <2.5 s for a three-step plan and is found to be robust to cloth self-occlusion, supporting the approach' viability for practical application.
SAIN: Self-Attentive Integration Network for Recommendation

With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where user-item interactions are sparse. To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed. Since these models use auxiliary attributes, they are effective in cold start settings. However, most of the proposed models are either unable to capture complex feature interactions or not properly designed to combine user-item feedback information with content information. In this paper, we propose Self-Attentive Integration Network (SAIN) which is a model that effectively combines user-item feedback information and auxiliary information for recommendation task. In SAIN, a self-attention mechanism is used in the feature-level interaction layer to effectively consider interactions between multiple features, while the information integration layer adaptively combines content and feedback information. The experimental results on two public datasets show that our model outperforms the state-of-the-art models by 2.13%
Recommendation from Raw Data with Adaptive Compound Poisson Factorization

Count data are often used in recommender systems: they are widespread (song play counts, product purchases, clicks on web pages) and can reveal user preference without any explicit rating from the user. Such data are known to be sparse, over-dispersed and bursty, which makes their direct use in recommender systems challenging, often leading to pre-processing steps such as binarization. The aim of this paper is to build recommender systems from these raw data, by means of the recently proposed compound Poisson Factorization (cPF). The paper contributions are three-fold: we present a unified framework for discrete data (dcPF), leading to an adaptive and scalable algorithm; we show that our framework achieves a trade-off between Poisson Factorization (PF) applied to raw and binarized data; we study four specific instances that are relevant to recommendation and exhibit new links with combinatorics. Experiments with three different datasets show that dcPF is able to effectively adjust to over-dispersion, leading to better recommendation scores when compared with PF on either raw or binarized data.
Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach

In fashion recommender systems, each product usually consists of multiple semantic attributes (e.g., sleeves, collar, etc). When making cloth decisions, people usually show preferences for different semantic attributes (e.g., the clothes with v-neck collar). Nevertheless, most previous fashion recommendation models comprehend the clothing images with a global content representation and lack detailed understanding of users' semantic preferences, which usually leads to inferior recommendation performance. To bridge this gap, we propose a novel Semantic Attribute Explainable Recommender System (SAERS). Specifically, we first introduce a fine-grained interpretable semantic space. We then develop a Semantic Extraction Network (SEN) and Fine-grained Preferences Attention (FPA) module to project users and items into this space, respectively. With SAERS, we are capable of not only providing cloth recommendations for users, but also explaining the reason why we recommend the cloth through intuitive visual attribute semantic highlights in a personalized manner. Extensive experiments conducted on real-world datasets clearly demonstrate the effectiveness of our approach compared with the state-of-the-art methods.
Genetic outcomes from the translocations of the critically endangered woylie

Translocations are an important conservation strategy for many species. However simply observing demographic growth of a translocated population is not sufficient to infer species recovery. Adequate genetic representation of the source population(s) and their long-term viability should also be considered. The woylie Bettongia penicillata ogilbyi has been subject to more formal translocations for conservation than any other marsupial that, up until recently, has resulted in one of the most successful species recoveries in Australia. We used mitochondrial and nuclear DNA markers to assess the genetic outcomes of translocated woylie populations. These populations have lost genetic variability, differentiated from their source population and the supplementation program on two island populations appears to have failed. We discuss the conservation implications that our results have for managing threatened species, outline some general recommendations for the management of present and future translocations and discuss the appropriate sampling design for the establishment of new populations or captive breeding programs that may mitigate the genetic 'erosion' seen in our study species. This research provides some practical outcomes and a pragmatic understanding of translocation biology. The findings are directly applicable to other translocation programs.
Benevolent and Hostile Sexism in Social Spheres: The Impact of Parents, School and Romance on Belgian Adolescents' Sexist Attitudes

Despite growing public awareness and policy efforts, gender equality has not yet been fully established in Western societies. Previous research has shown that hostile and benevolent sexist attitudes, which are grounded in traditional gender stereotypes, play a key role in the reproduction of gender inequalities. Whereas, hostile and benevolent sexism among adolescents has been previously studied, limited attention has been paid to social characteristics in understanding the support for these attitudes. In this article, we aim to study how the family, the school and romantic partnerships relate to adolescents' benevolent and hostile sexist attitudes. We relied on data gathered in 2013 by the Flemish Youth Research Platform and performed multivariate analyses on 755 parent-child dyads (n♂ = 342; n♀ = 413). Our results indicate that social characteristics especially matter to explain the variation in benevolent sexist attitudes among girls and hostile sexist attitudes among boys. Among girls, being in a romantic relationship and parents' traditional moral beliefs was strongly related to benevolent sexism; while for boys, hostile sexism was strongly related to being enrolled in technical and vocational education. In the conclusion, we elaborate on the implications of our findings.
Future prospects in the treatment of lupus nephritis: proliferative lupus nephritis

Lupus nephritis is a common manifestation of systemic lupus erythematosus, and a predictor of an adverse outcome in this disease. With respect to long-term renal outcome, cyclophosphamide pulses plus steroids are the standard therapy for lupus nephritis. During the last decade, the first international, multicenter, randomized controlled trials were conducted to evaluate new substances for the treatment of lupus nephritis. The testing of other immunosuppressive drugs, anticellular therapies, modulation of cell–cell interactions, (anti)cytokine therapies and lupus erythematosus-specific immunomodulation is currently underway. On the basis of experiences from other indications, mycophenolate and rituximab are currently the most promising drugs, concluding 30 years without any new licensed lupus drugs. Other approaches appear to use more specific interventions, but it may be that the evaluation of anticytokine interventions or lupus erythematosus-specific immunomodulation requires new biomarkers that enable a.
Functional Identification of Corynespora cassiicola-Responsive miRNAs and Their Targets in Cucumber

Target leaf spot (TLS), which is caused by Corynespora cassiicola (C. cassiicola), is one of the most important diseases in cucumber (Cucumis sativus L.). Our previous research identified several C. cassiicola-responsive miRNAs in cucumber by high-throughput sequencing, including two known miRNAs and two novel miRNAs. The target genes of these miRNAs were related to secondary metabolism. In this study, we verified the interaction between these miRNAs and target genes by histochemical staining and fluorescence quantitative assays of GUS. We transiently expressed the candidate miRNAs and target genes in cucumber cotyledons to investigate the resistance to C. cassiicola. Transient expression of miR164d, miR396b, Novel-miR1, and Novel-miR7 in cucumber resulted in decreased resistance to C. cassiicola, while transient expression of NAC (inhibited by miR164d), APE (inhibited by miR396b), 4CL (inhibited by Novel-miR1), and PAL (inhibited by Novel-miR7) led to enhanced resistance to C. cassiicola. In addition, overexpression of 4CL and PAL downregulated lignin synthesis, and overexpression of Novel-miR1 and Novel-miR7 also downregulated lignin synthesis, indicating that the regulation of 4CL and PAL by Novel-miR1 and Novel-miR7 could affect lignin content. The tobacco rattle virus (TRV) induced short tandem target mimic (STTM)-miRNA silencing vector was successfully constructed, and target miRNAs were successfully silenced. The identification of disease resistance and lignin content showed that silencing candidate miRNAs could improve cucumber resistance to C. cassiicola.
Optimizing Expectations via Mobile Apps: A New Approach for Examining and Enhancing Placebo Effects

There is growing interest in interventions that enhance placebo responses in clinical practice, given the possibility that this would lead to better patient health and more effective therapy outcomes. Previous studies suggest that placebo effects can be maximized by optimizing patients’ outcome expectations. However, expectancy interventions are difficult to validate because of methodological challenges, such as reliable blinding of the clinician providing the intervention. Here we propose a novel approach using mobile apps that can provide highly standardized expectancy interventions in a blinded manner, while at the same time assessing data in everyday life using experience sampling methodology (e.g., symptom severity, expectations) and data from smartphone sensors. Methodological advantages include: 1) full standardization; 2) reliable blinding and randomization; 3) disentangling expectation effects from other factors associated with face-to-face interventions; 4) assessing short-term (days), long-term (months), and cumulative effects of expectancy interventions; and 5) investigating possible mechanisms of change. Randomization and expectancy interventions can be realized by the app (e.g., after the clinic/lab visit). As a result, studies can be blinded without the possibility for the clinician to influence study outcomes. Possible app-based expectancy interventions include, for example, verbal suggestions and imagery exercises, although a large number of possible interventions (e.g., hypnosis) could be evaluated using this innovative approach.
On the Effectiveness of Low-rank Approximations for Collaborative Filtering compared to Neural Networks

Even in times of deep learning, low-rank approximations by factorizing a matrix into user and item latent factors continue to be a method of choice for collaborative filtering tasks due to their great performance. While deep learning based approaches excel in hybrid recommender tasks where additional features for items, users or even context are available, their flexibility seems to rather impair the performance compared to low-rank approximations for pure collaborative filtering tasks where no additional features are used. Recent works propose hybrid models combining low-rank approximations and traditional deep neural architectures with promising results but fail to explain why neural networks alone are unsuitable for this task. In this work, we revisit the model and intuition behind low-rank approximation to point out its suitability for collaborative filtering tasks. In several experiments we compare the performance and behavior of models based on a deep neural network and low-rank approximation to examine the reasons for the low effectiveness of traditional deep neural networks. We conclude that the universal approximation capabilities of traditional deep neural networks severely impair the determination of suitable latent vectors, leading to a worse performance compared to low-rank approximations.
Information Source Detection with Limited Time Knowledge

We study the source detection problem using limited timestamps on a given network. Due to the NP-completeness of the maximum likelihood estimator (MLE), we propose an approximation solution called infection-path-based estimator (INF), the essence of which is to identify the most likely infection path that is consistent with observed timestamps. The source node associated with that infection path is viewed as the estimated source û. For the tree network, we transform the INF into integer linear programming and find a reduced search region using BFS, within which the estimated source is provably always on a path termed as candidate path. This notion enables us to analyze the accuracy of the INF in terms of error distance on arbitrary tree. Specifically, on the infinite g-regular tree with uniform sampled timestamps, we get a refined performance guarantee in the sense of a constant bounded d(u*, û). By virtue of time labeled BFS tree, the estimator still performs fairly well when extended to more general graphs. Simulations on both trees and general networks further demonstrate the superior performance of the INF.
An unphysical solution of Maxwell's equations and the failure of Newtonian mechanics

The existence of an unphysical solution of Maxwell's equations for a plane-wave field exemplifies the general result that electromagnetic potentials convey more physical information than the electric and magnetic fields derivable from them. Thus, potential functions are needed to define unambiguously the physical identity of a system. Electromagnetic forces depend only on the fields, which means that Newtonian mechanics, which is based on forces, can produce improper results. Consequences relevant to current research are that a proposed nondipole correction for laser field effects is not physically meaningful; and that the constant crossed-fields configuration in electrodynamics is unrelated to plane-wave fields.
Learning Nonsymmetric Determinantal Point Processes

Determinantal point processes (DPPs) have attracted substantial attention as an elegant probabilistic model that captures the balance between quality and diversity within sets. DPPs are conventionally parameterized by a positive semi-definite kernel matrix, and this symmetric kernel encodes only repulsive interactions between items. These so-called symmetric DPPs have significant expressive power, and have been successfully applied to a variety of machine learning tasks, including recommendation systems, information retrieval, and automatic summarization, among many others. Efficient algorithms for learning symmetric DPPs and sampling from these models have been reasonably well studied. However, relatively little attention has been given to nonsymmetric DPPs, which relax the symmetric constraint on the kernel. Nonsymmetric DPPs allow for both repulsive and attractive item interactions, which can significantly improve modeling power, resulting in a model that may better fit for some applications. We present a method that enables a tractable algorithm, based on maximum likelihood estimation, for learning nonsymmetric DPPs from data composed of observed subsets. Our method imposes a particular decomposition of the nonsymmetric kernel that enables such tractable learning algorithms, which we analyze both theoretically and experimentally. We evaluate our model on synthetic and real-world datasets, demonstrating improved predictive performance compared to symmetric DPPs, which have previously shown strong performance on modeling tasks associated with these datasets.
Algorithmic Detection and Analysis of Vaccine-Denialist Sentiment Clusters in Social Networks

Vaccination rates are decreasing in many areas of the world, and outbreaks of preventable diseases tend to follow in areas with particular low rates. Much research has been devoted to improving our understanding of the motivations behind vaccination decisions and the effects of various types of information offered to skeptics, no large-scale study of the structure of online vaccination discourse have been conducted. Here, we offer an approach to quantitatively study the vaccine discourse in an online system, exemplified by Twitter. We use train a deep neural network to predict tweet vaccine sentiments, surpassing state-of-the-art performance, attaining two-class accuracy of $90.4\%$, and a three-class F1 of $0.762$. We identify profiles which consistently produce strongly anti- and pro-vaccine content. We find that strongly anti-vaccine profiles primarily post links to Youtube, and commercial sites that make money on selling alternative health products, representing a conflict of interest. We also visualize the network of repeated mutual interactions of actors in the vaccine discourse and find that it is highly stratified, with an assortativity coefficient of $r = . 813$.
Experimental model proposed to deduce pharmacological evidence of the beta-cell neogenesis activity of Argyrolobium roseum vis-a-vis an evaluation of its hypoglycemic activity

Background: Argyrolobium roseum is described in texts for its botanical aspects with no mention of its therapeutic uses. Recently, a solvent fraction of this plant has been reported to possess insulin secretagogue activity. Objective: Primary objective of the study was to evaluate the hypoglycemic activity of different fractions of Argyrolobium roseum plant. Secondary objective was to devise a model for detecting its beta-cell neogenesis activity. Materials and Methods: Alcoholic fraction (AR) was studied in fasting, glucose-loaded and streptozotocin (STZ)-treated hyperglycemic rats. For deducing beta-cell neogenesis activity, a sub-lethal dose of STZ, causing delayed death in the animals, was administered. Results: AR in a dose of 200 mg/kg showed hypoglycemic activity in fasting and STZ-treated rats. With sub-lethal dose of STZ, recovery from hyperglycemia was seen. Conclusion: AR of Argyrolobium roseum possesses hypoglycemic and a possible beta-cell neogenesis activity. Further studies are required to confirm its beta-cell neogenesis potential by cytological methods and to establish its role in the treatment of type 2 diabetes.
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator

We study the sample complexity of approximate policy iteration (PI) for the Linear Quadratic Regulator (LQR), building on a recent line of work using LQR as a testbed to understand the limits of reinforcement learning (RL) algorithms on continuous control tasks. Our analysis quantifies the tension between policy improvement and policy evaluation, and suggests that policy evaluation is the dominant factor in terms of sample complexity. Specifically, we show that to obtain a controller that is within $\varepsilon$ of the optimal LQR controller, each step of policy evaluation requires at most $(n+d)^3/\varepsilon^2$ samples, where $n$ is the dimension of the state vector and $d$ is the dimension of the input vector. On the other hand, only $\log(1/\varepsilon)$ policy improvement steps suffice, resulting in an overall sample complexity of $(n+d)^3 \varepsilon^{-2} \log(1/\varepsilon)$. We furthermore build on our analysis and construct a simple adaptive procedure based on $\varepsilon$-greedy exploration which relies on approximate PI as a sub-routine and obtains $T^{2/3}$ regret, improving upon a recent result of Abbasi-Yadkori et al.