Miracle-dz/UniFAR-FTUs huggingface dataset:
EMI: Exploration with Mutual Information

Reinforcement learning algorithms struggle when the reward signal is very sparse. In these cases, naive random exploration methods essentially rely on a random walk to stumble onto a rewarding state. Recent works utilize intrinsic motivation to guide the exploration via generative models, predictive forward models, or discriminative modeling of novelty. We propose EMI, which is an exploration method that constructs embedding representation of states and actions that does not rely on generative decoding of the full observation but extracts predictive signals that can be used to guide exploration based on forward prediction in the representation space. Our experiments show competitive results on challenging locomotion tasks with continuous control and on image-based exploration tasks with discrete actions on Atari. The source code is available at this https URL .
Transfer-Learning Oriented Class Imbalance Learning for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) aims to predict defects of projects lacking training data by using prediction models trained on historical defect data from other projects. However, since the distribution differences between datasets from different projects, it is still a challenge to build high-quality CPDP models. Unfortunately, class imbalanced nature of software defect datasets further increases the difficulty. In this paper, we propose a transferlearning oriented minority over-sampling technique (TOMO) based feature weighting transfer naive Bayes (FWTNB) approach (TOMOFWTNB) for CPDP by considering both classimbalance and feature importance problems. Differing from traditional over-sampling techniques, TOMO not only can balance the data but reduce the distribution difference. And then FWTNB is used to further increase the similarity of two distributions. Experiments are performed on 11 public defect datasets. The experimental results show that (1) TOMO improves the average G-Measure by 23.7\%$\sim$41.8\%, and the average MCC by 54.2\%$\sim$77.8\%. (2) feature weighting (FW) strategy improves the average G-Measure by 11\%, and the average MCC by 29.2\%. (3) TOMOFWTNB improves the average G-Measure value by at least 27.8\%, and the average MCC value by at least 71.5\%, compared with existing state-of-theart CPDP approaches. It can be concluded that (1) TOMO is very effective for addressing class-imbalance problem in CPDP scenario; (2) our FW strategy is helpful for CPDP; (3) TOMOFWTNB outperforms previous state-of-the-art CPDP approaches.
Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples

State-of-the-art neural networks are vulnerable to adversarial examples; they can easily misclassify inputs that are imperceptibly different than their training and test data. In this work, we establish that the use of cross-entropy loss function and the low-rank features of the training data have responsibility for the existence of these inputs. Based on this observation, we suggest that addressing adversarial examples requires rethinking the use of cross-entropy loss function and looking for an alternative that is more suited for minimization with low-rank features. In this direction, we present a training scheme called differential training, which uses a loss function defined on the differences between the features of points from opposite classes. We show that differential training can ensure a large margin between the decision boundary of the neural network and the points in the training dataset. This larger margin increases the amount of perturbation needed to flip the prediction of the classifier and makes it harder to find an adversarial example with small perturbations. We test differential training on a binary classification task with CIFAR-10 dataset and demonstrate that it radically reduces the ratio of images for which an adversarial example could be found -- not only in the training dataset, but in the test dataset as well.
Decoding multimodal behavior using time differences of MEG events

Multimodal behavior involves multiple processing stations distributed across distant brain regions, but our understanding of how such distributed processing is coordinated in the brain is limited. Here we take a decoding approach to this problem, aiming to quantify how temporal aspects of brain-wide neural activity may be used to infer specific multimodal behaviors. Using high temporal resolution measurements by MEG, we detect bursts of activity from hundreds of locations across the surface of the brain at millisecond resolution. We then compare decoding using three characteristics of neural activity bursts, decoding with event counts, with latencies and with time differences between pairs of events. Training decoders in this regime is particularly challenging because the number of samples is smaller by orders of magnitude than the input dimensionality. We develop a new decoding approach for this regime that combines non-parametric modelling with aggressive feature selection. Surprisingly, we find that decoding using time-differences, based on thousands of region pairs, is significantly more accurate than using other activity characteristics, reaching 90% accuracy consistently across subjects. These results suggest that relevant information about multimodal brain function is provided by subtle time differences across remote brain areas.
Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back

A graph is a powerful concept for representation of relations between pairs of entities. Data with underlying graph structure can be found across many disciplines and there is a natural desire for understanding such data better. Deep learning (DL) has achieved significant breakthroughs in a variety of machine learning tasks in recent years, especially where data is structured on a grid, such as in text, speech, or image understanding. However, surprisingly little has been done to explore the applicability of DL on arbitrary graph-structured data directly. The goal of this thesis is to investigate architectures for DL on graphs and study how to transfer, adapt or generalize concepts that work well on sequential and image data to this domain. We concentrate on two important primitives: embedding graphs or their nodes into a continuous vector space representation (encoding) and, conversely, generating graphs from such vectors back (decoding). To that end, we make the following contributions. First, we introduce Edge-Conditioned Convolutions (ECC), a convolution-like operation on graphs performed in the spatial domain where filters are dynamically generated based on edge attributes. The method is used to encode graphs with arbitrary and varying structure. Second, we propose SuperPoint Graph, an intermediate point cloud representation with rich edge attributes encoding the contextual relationship between object parts. Based on this representation, ECC is employed to segment large-scale point clouds without major sacrifice in fine details. Third, we present GraphVAE, a graph generator allowing us to decode graphs with variable but upper-bounded number of nodes making use of approximate graph matching for aligning the predictions of an autoencoder with its inputs. The method is applied to the task of molecule generation.
DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed Caching

Load balancing is critical for distributed storage to meet strict service-level objectives (SLOs). It has been shown that a fast cache can guarantee load balancing for a clustered storage system. However, when the system scales out to multiple clusters, the fast cache itself would become the bottleneck. Traditional mechanisms like cache partition and cache replication either result in load imbalance between cache nodes or have high overhead for cache coherence. We present DistCache, a new distributed caching mechanism that provides provable load balancing for large-scale storage systems. DistCache co-designs cache allocation with cache topology and query routing. The key idea is to partition the hot objects with independent hash functions between cache nodes in different layers, and to adaptively route queries with the power-of-two-choices. We prove that DistCache enables the cache throughput to increase linearly with the number of cache nodes, by unifying techniques from expander graphs, network flows, and queuing theory. DistCache is a general solution that can be applied to many storage systems. We demonstrate the benefits of DistCache by providing the design, implementation, and evaluation of the use case for emerging switch-based caching.
Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph

We study the decentralized machine learning scenario where many users collaborate to learn personalized models based on (i) their local datasets and (ii) a similarity graph over the users' learning tasks. Our approach trains nonlinear classifiers in a multi-task boosting manner without exchanging personal data and with low communication costs. When background knowledge about task similarities is not available, we propose to jointly learn the personalized models and a sparse collaboration graph through an alternating optimization procedure. We analyze the convergence rate, memory consumption and communication complexity of our decentralized algorithms, and demonstrate the benefits of our approach compared to competing techniques on synthetic and real datasets.
Quantitative Job Insecurity and Well-Being: Testing the Mediating Role of Hindrance and Challenge Appraisals

The aim of this study is to determine the extent to which cognitive appraisals of job insecurity may mediate the link between job insecurity and well-being among employees. According to cognitive appraisal theory, the two cognitive appraisals of job insecurity, hindrance vs. challenge appraisals, were integrated into a conceptual model and examined as the mediators of job insecurity-wellbeing association. Well-being related outcomes were job satisfaction and emotional exhaustion. Hypotheses were tested using a total sample of 306 hospital staff1. Respondents from diverse departments of this hospital were recruited and completed scales on quantitative job insecurity, hindrance vs. challenge appraisals of job insecurity, job satisfaction, and emotional exhaustion. Results indicated that hindrance appraisals of job insecurity mediated the association between job insecurity and emotional exhaustion. Challenge appraisals did not mediate the job insecurity-well-being association. In all, only one out of four mediation paths was found significant. As a result, employees hindered by job insecurity are more likely to be emotionally exhausted.
Gene Expression Program Underlying Tail Resorption During Thyroid Hormone-Dependent Metamorphosis of the Ornamented Pygmy Frog Microhyla fissipes

Thyroid hormone (T3) is essential for vertebrate development, especially during the so-called postembryonic development, a period around birth in mammals when plasma T3 level peaks and many organs mature into their adult form. Compared to embryogenesis, postembryonic development is poorly studied in mammals largely because of the difficulty to manipulate the uterus-enclosed embryos and neonates. Amphibian metamorphosis is independent of maternal influence and can be easily manipulated for molecular and genetic studies, making it a valuable model to study postembryonic development in vertebrates. Studies on amphibian metamorphosis have been largely focused on the two highly related species Xenopus laevis and Xenopus tropicalis. However, adult X. laevis and X. tropicalis animals remain aquatic. This makes important to study metamorphosis in a species in which postmetamorphic frogs live on land. In this regard, the anuran Microhyla fissipes represents an alternative model for developmental and genetic studies. Here we have made use of the advances in sequencing technologies to investigate the gene expression profiles underlying the tail resorption program during metamorphosis in M. fissipes. We first used single molecule real-time sequencing to obtain 67, 939 expressed transcripts in M. fissipes. We next identified 4,555 differentially expressed transcripts during tail resorption by using Illumina sequencing on RNA samples from tails at different metamorphic stages. Bioinformatics analyses revealed that 11 up-regulated KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways and 88 Gene Ontology (GO) terms as well as 21 down-regulated KEGG pathways and 499 GO terms were associated with tail resorption. Our findings suggest that tail resorption in M. fissipes and X. laevis shares many programs. Future investigations on function and regulation of these genes and pathways should help to reveal the mechanisms governing amphibian tail resorption and adaptive evolution from aquatic to terrestrial life. Furthermore, analysis of the M. fissipes model, especially, on the changes in other organs associated with the transition from aquatic to terrestrial living, should help to reveal important mechanistic insights governing mammalian postembryonic developments.
Thought Control Ability Moderates the Effect of Mind Wandering on Positive Affect via the Frontoparietal Control Network

Mind wandering is a phenomenon that involves thoughts shifting away from a primary task to the process of dealing with other personal goals. A large number of studies have found that mind wandering can predict negative emotions, but researchers have seldom focused on the positive role of mind wandering. The current study aimed to explore the relationships among mind wandering, emotions and thought control ability, which is the ability to inhibit one’s own unpleasant or unwanted intrusive thoughts. Here, we collected resting-state functional magnetic resonance imaging (rsfMRI) data from 368 participants who completed a set of questionnaires involving mind wandering, thought control ability and positive or negative emotions. The results revealed that (1) rsfMRI connectivity features related to thought control ability and mind wandering could divide individuals into two groups: HMW (high mind-wandering) group and LMW (low mind-wandering) group. The HMW group scored lower in thought control ability (TCA), higher in negative emotion (NE) and lower in positive emotion (PE) than the LMW group. (2) TCA moderated the association between MW and positive affect (PA). (3) Two groups exhibited different segregation within key nodes (SWKN) of the frontoparietal control network (FPCN), and the subsequent analysis showed that the SWKN of the FPCN was negatively correlated with PA. These findings indicate that TCA moderates the effect of mind wandering on affect via the FPCN, which may have important implications for our understanding of the positive role of mind wandering.
Resting State Functional Connectivity of Dorsal Raphe Nucleus and Ventral Tegmental Area in Medication-Free Young Adults With Major Depression

Background: This study has, for the first time, investigated the dorsal raphe nucleus (DRN) and ventral tegmental area (VTA) resting state whole-brain functional connectivity in medication-free young adults with major depression (MDD), at baseline and in relationship to treatment response. Method: A total of 119 subjects: 78 MDD (24 ± 4 years.) and 41 Healthy Controls (HC) (24 ± 3 years) were included in the analysis. DRN and VTA ROIs anatomical templates were used to extract resting state fluctuations and used to derive whole-brain functional connectivity. Differences between MDD and HCs were examined, as well as the correlation of baseline Hamilton Depression and Anxiety scale scores to the baseline DRN and VTA connectivity. The relationship to treatment response was examined by investigating the correlation of the percentage decrease in depression and anxiety scale scores with baseline connectivity measures. Results: There was a significant decrease (p = 0.05; cluster-wise corrected) in DRN connectivity with the prefrontal and mid-cingulate cortex in the MDD group, compared with the HC group. DRN connectivity with temporal areas, including the hippocampus and amygdala, positively correlated with baseline depression scores (p = 0.05; cluster-wise corrected). VTA connectivity with the cuneus-occipital areas correlated with a change in depression scores (p = 0.05; cluster-wise corrected). Conclusion: Our results indicate the presence of DRN-prefrontal and DRN-cingulate cortex connectivity abnormalities in young medication-free depressed subjects when compared to HCs and that the severity of depressive symptoms correlates with DRN-amygdala/hippocampus connectivity. VTA connectivity with the parietal and occipital areas is related to antidepressant treatment associated with a decrease in depressive symptoms. Future studies need to be carried out in larger and different age group populations to confirm the findings of the study.
Fine-Scale Tracking of Ambient Temperature and Movement Reveals Shuttling Behavior of Elephants to Water

Movement strategies of animals have been well studied as a function of ecological drivers (e.g., forage selection and avoiding predation) rather than physiological requirements (e.g., thermoregulation). Thermal stress is a major concern for large mammals, especially for savanna elephants (Loxodonta africana), which have amongst the greatest challenge for heat dissipation in hot and arid environments. Therefore, elephants must make decisions about where and how fast to move to reduce thermal stress. We tracked 14 herds of elephant in Kruger National Park (KNP), South Africa, for 2 years, using GPS collars with inbuilt temperature sensors to examine the influence of temperature on movement strategies, particularly when accessing water. We first confirmed that collar-mounted temperature loggers captured hourly variation in relative ambient temperatures across the landscape, and thus, could be used to predict elephant movement strategies at fine spatio-temporal scales. We found that elephants moved slower in more densely wooded areas, but unexpectedly, moved faster at higher temperatures, especially in the wet season compared to the dry season. Notably, this speed of movement was highest when elephants were approaching and leaving water sources. Visits to water showed a periodic shuttling pattern, with a peak return rate of 10-30 hours, wherein elephants were closest to water during the hotter times of the day, and spent longer at water sources in the dry season compared to the wet season. When elephants left water, they showed low fidelity to the same water source, and travelled farther in the dry season than in the wet season. In KNP, where water is easily accessible, and the risk of poaching is low, we found that elephants use short, high-speed bursts of movement to get to water at hotter times of day. This strategy not only provides the benefit of predation risk avoidance, but also allows them to use water to thermoregulate. We demonstrate that ambient temperature is an important predictor of movement and water use across the landscape, with elephants responding facultatively to a “landscape of thermal stress”.
Spanish Version of the Flourishing Scale (FS) on the Parents of Children With Cancer: A Validation Through Rasch Analysis

The interest in the study of flourishing is due to the fact that it has been proven that it contributes to a good adjustment to the demands of the environment, relating to indicators of health and well-being. There are many researches that have tried to find out what dimensions make it up. The goal of this study is to validate the Spanish Version of The Flourishing Scale (FS), being the first time it is applied to Spanish parents of children with cancer. A total of 138 parents of children with cancer participated in a semi-structured interview. Through IBM SPSS and Winsteps descriptive analyzes and the internal consistency of the FS were calculated. Rasch analysis was used to study the dimensionality of the scale, the adjustment of each one of the items, the reliability values for the items and for the people, the validity of the construct, the functioning of the response categories, and the differential item functioning (DIF). The external construct validity of the FS was examined with associated measures. Results found that parents indicated a flourishing attitude (M = 3.85; sd = 0.63). We confirm that the FS maintains an adequate internal consistency and a unidimensional structure. We observed a good alignment between the question and the person's abilities as soon as a high reliability for the items. Although the sample is large enough to corroborate the difficulty hierarchy of the items, the construct validity could be improved by introducing items of moderate and high difficulty. On the other hand, the answer category 3 overlaps with the 2 and 4, so we propose possible solutions. Regarding a possible DIF, this exists in relation to “gender,” “level of education” and “by the situation with respect to treatment. ” Finally, the convergent validity of the FS is demonstrated, there being a significant correlation with well-being, satisfaction with social support and coping. In general, the results show adequate psychometric properties of the Spanish version of the FS, so we recommend integrating it in psychosocial interventions aimed at parents of children with cancer, in order to provide them with resources to deal with the disease.
An Exploratory Study of Police Officers: Low Compassion Satisfaction and Compassion Fatigue

Background: Compassion fatigue, or the physical, mental, and emotional state experienced by professionals that assist others in distress, has been well documented in several caring professions such as nurses, firefighters, and emergency medical technicians. Until the current study, it has only rarely been examined in police samples despite their high rates of stress and suicide which is a likely result of a depletion of compassion satisfaction, or the pleasure an officer gets from relating to and helping others. Aim: This study documents findings from an ongoing study of compassion fatigue amongst a sample of US urban police officers which suggests the possibility of a future risk for high burnout. Conclusion: Very low levels of compassion fatigue were found in the sampled police officers in comparison to what would be expected from the general population. Where compassion fatigue was found in the sampled police, it was significantly correlated to the level of compassion satisfaction. A potential cause for concern is that the incidence of levels of reported compassion satisfaction were also low in the sample (in the bottom quartile compared to the general population). This suggests a possibility of higher numbers of burnout in the future given the role of compassion satisfaction as a buffer against compassion fatigue in policing.