Miracle-dz/UniFAR-FTUs huggingface dataset:
Decomposable Formulation of Transmission Constraints for Decentralized Power Systems Optimization

One of the most complicating factors in decentralized solution methods for a broad range of power system optimization problems is the modeling of power flow equations. Existing formulations for direct current power flows either have limited scalability or are very dense and unstructured, making them unsuitable for large-scale decentralized studies. In this work, we present a novel sparsified variant of the injection shift factors formulation, which has a decomposable block-diagonal structure and scales well for large systems. We also propose a decentralized solution method, based on the alternating direction multiplier method, that efficiently handles transmission line outages in N-1 security requirements. Benchmarks on multizonal security-constrained unit commitment problems show that the proposed formulation and algorithm can reliably and efficiently solve interconnection-level test systems with up to 6,515 buses with no convergence or numerical issues. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was partially supported by Laboratory Directed Research and Development funding from Argonne National Laboratory provided by the Director, Office of Science, of the U.S. Department of Energy [Grant DE-AC02-06CH11357]. This work was also partially supported by the U.S. Department of Energy Advanced Grid Modeling Program [Grant DE-OE0000875]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0326 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0326 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github. io/ .
New approaches to compare Police practice in Europe: an occupational health perspective.

Police work is a particular stressful occupation. Exposure to stressors everyday impairs physical and psychological health and can impact the welfare of citizens. It seems crucial to further understand stress among police officers in order to have proactive and healthy European police force. Despite this need, previous research on Police stress has been impaired by several problems. These include relying largely on self-report measures, retrospective biases, and cross sectional designs, failing to address within-person variations. Police forces in most European countries face nowadays extremely challenging times due to a combination of factors, including open borders, drug trafficking, terrorism, multicultural diversity and the overall use of new technologies in contemporary life (Benyon, 1994; Oakley, 2001). Hence, there is an urgent need to conduct comparative occupational health studies among police forces in Europe, allowing the development of knowledge in this area and the dissemination of best practices to tackle police stress and foster police health and well-being. Despite this need, little work has been conducted, mainly due to a variety of reasons discussed in this paper that are likely to emerge when conducting European comparative studies on police. In an attempt to overcome some of these challenges, recommendations are provided and particularly an innovative interdisciplinary research method developed in the SCOPE project is described. Findings of this research will impact theoretical and applied knowledge in the area of police occupational health. Finally, this seems to be a promising research method to use in future comparative occupational health studies among police forces in Europe.
On Solving Cooperative MARL Problems with a Few Good Experiences

Cooperative Multi-agent Reinforcement Learning (MARL) is crucial for cooperative decentralized decision learning in many domains such as search and rescue, drone surveillance, package delivery and fire fighting problems. In these domains, a key challenge is learning with a few good experiences, i.e., positive reinforcements are obtained only in a few situations (e.g., on extinguishing a fire or tracking a crime or delivering a package) and in most other situations there is zero or negative reinforcement. Learning decisions with a few good experiences is extremely challenging in cooperative MARL problems due to three reasons. First, compared to the single agent case, exploration is harder as multiple agents have to be coordinated to receive a good experience. Second, environment is not stationary as all the agents are learning at the same time (and hence change policies). Third, scale of problem increases significantly with every additional agent. Relevant existing work is extensive and has focussed on dealing with a few good experiences in single-agent RL problems or on scalable approaches for handling non-stationarity in MARL problems. Unfortunately, neither of these approaches (or their extensions) are able to address the problem of sparse good experiences effectively. Therefore, we provide a novel fictitious self imitation approach that is able to simultaneously handle non-stationarity and sparse good experiences in a scalable manner. Finally, we provide a thorough comparison (experimental or descriptive) against relevant cooperative MARL algorithms to demonstrate the utility of our approach.
Optimizing Generative Adversarial Networks for Image Super Resolution via Latent Space Regularization

Natural images can be regarded as residing in a manifold that is embedded in a higher dimensional Euclidean space. Generative Adversarial Networks (GANs) try to learn the distribution of the real images in the manifold to generate samples that look real. But the results of existing methods still exhibit many unpleasant artifacts and distortions even for the cases where the desired ground truth target images are available for supervised learning such as in single image super resolution (SISR). We probe for ways to alleviate these problems for supervised GANs in this paper. We explicitly apply the Lipschitz Continuity Condition (LCC) to regularize the GAN. An encoding network that maps the image space to a new optimal latent space is derived from the LCC, and it is used to augment the GAN as a coupling component. The LCC is also converted to new regularization terms in the generator loss function to enforce local invariance. The GAN is optimized together with the encoding network in an attempt to make the generator converge to a more ideal and disentangled mapping that can generate samples more faithful to the target images. When the proposed models are applied to the single image super resolution problem, the results outperform the state of the art.
Planning an Efficient and Robust Base Sequence for a Mobile Manipulator Performing Multiple Pick-and-place Tasks

In this paper, we address efficiently and robustly collecting objects stored in different trays using a mobile manipulator. A resolution complete method, based on precomputed reachability database, is proposed to explore collision-free inverse kinematics (IK) solutions and then a resolution complete set of feasible base positions can be determined. This method approximates a set of representative IK solutions that are especially helpful when solving IK and checking collision are treated separately. For real world applications, we take into account the base positioning uncertainty and plan a sequence of base positions that reduce the number of necessary base movements for collecting the target objects, the base sequence is robust in that the mobile manipulator is able to complete the part-supply task even there is certain deviation from the planned base positions. Our experiments demonstrate both the efficiency compared to regular base sequence and the feasibility in real world applications.
Unsupervised Representation Disentanglement Using Cross Domain Features and Adversarial Learning in Variational Autoencoder Based Voice Conversion

An effective approach for voice conversion (VC) is to disentangle linguistic content from other components in the speech signal. The effectiveness of variational autoencoder (VAE) based VC (VAE-VC), for instance, strongly relies on this principle. In our prior work, we proposed a cross-domain VAE-VC (CDVAE-VC) framework, which utilized acoustic features of different properties, to improve the performance of VAE-VC. We believed that the success came from more disentangled latent representations. In this article, we extend the CDVAE-VC framework by incorporating the concept of adversarial learning, in order to further increase the degree of disentanglement, thereby improving the quality and similarity of converted speech. More specifically, we first investigate the effectiveness of incorporating the generative adversarial networks (GANs) with CDVAE-VC. Then, we consider the concept of domain adversarial training and add an explicit constraint to the latent representation, realized by a speaker classifier, to explicitly eliminate the speaker information that resides in the latent code. Experimental results confirm that the degree of disentanglement of the learned latent representation can be enhanced by both GANs and the speaker classifier. Meanwhile, subjective evaluation results in terms of quality and similarity scores demonstrate the effectiveness of our proposed methods.
Deleting object selective units in a fully-connected layer of deep convolutional networks improves classification performance

Neurons in the primate visual cortices show a wide range of stimulus selectivity. Some neurons respond to only a small fraction of stimulus images, whereas others respond to many stimulus images in a non-selective manner. It is unclear how stimulus selective and non-selective neurons contribute to visual object recognition. Herein, we examined the relationship between stimulus selectivity and the effect of deletion of units on task performance using fully a connected layer of two types of deep convolutional neural networks (DCNNs). Deleting a stimulus selective unit caused slight improvements of task performance, whereas deleting stimulus non-selective units caused a significant decrease in task performance. However, these findings do not imply that stimulus selective units have no use for the task. Indeed, better performance was obtained when the networks consisted of both stimulus selective and non-selective units.
Unsupervised Domain Adaptation for Neural Machine Translation with Iterative Back Translation

State-of-the-art neural machine translation (NMT) systems are data-hungry and perform poorly on domains with little supervised data. As data collection is expensive and infeasible in many cases, unsupervised domain adaptation methods are needed. We apply an Iterative Back Translation (IBT) training scheme on in-domain monolingual data, which repeatedly uses a Transformer-based NMT model to create in-domain pseudo-parallel sentence pairs in one translation direction on the fly and then use them to train the model in the other direction. Evaluated on three domains of German-to-English translation task with no supervised data, this simple technique alone (without any out-of-domain parallel data) can already surpass all previous domain adaptation methods---up to +9.48 BLEU over the strongest previous method, and up to +27.77 BLEU over the unadapted baseline. Moreover, given available supervised out-of-domain data on German-to-English and Romanian-to-English language pairs, we can further enhance the performance and obtain up to +19.31 BLEU improvement over the strongest baseline, and +47.69 BLEU increment against the unadapted model.
Influence of the high-order terms on density of condensate of a dilute Bose gas in improved Hartree-Fock approximation

Nguyen Van Thu 2 Institute for Research and Development, Duy Tan University, Da Nang, Vietnam Department of Physics, Hanoi Pedagogical University 2, Hanoi, Vietnam Abstract By means of Cornwal-Jackiw-Tomboulis effective action approach, the density of condensate of a dilute Bose gas confined between two planar walls is investigated within the framework of improved Hartree-Fock, in which the higher-order terms in the momentum integrals are taken into account. Our results show that the quantum fluctuations significantly belittle the density of condensate compared with square of the expectation value of the field operator.
Lesion-Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale

The acquisition of large-scale medical image data, necessary for training machine learning algorithms, is hampered by associated expert-driven annotation costs. Mining hospital archives can address this problem, but labels often incomplete or noisy, e.g., 50% of the lesions in DeepLesion are left unlabeled. Thus, effective label harvesting methods are critical. This is the goal of our work, where we introduce Lesion-Harvester—a powerful system to harvest missing annotations from lesion datasets at high precision. Accepting the need for some degree of expert labor, we use a small fully-labeled image subset to intelligently mine annotations from the remainder. To do this, we chain together a highly sensitive lesion proposal generator (LPG) and a very selective lesion proposal classifier (LPC). Using a new hard negative suppression loss, the resulting harvested and hard-negative proposals are then employed to iteratively finetune our LPG. While our framework is generic, we optimize our performance by proposing a new 3D contextual LPG and by using a global-local multi-view LPC. Experiments on DeepLesion demonstrate that Lesion-Harvester can discover an additional 9,805 lesions at a precision of 90%. We publicly release the harvested lesions, along with a new test set of completely annotated DeepLesion volumes. We also present a pseudo 3D IoU evaluation metric that corresponds much better to the real 3D IoU than current DeepLesion evaluation metrics. To quantify the downstream benefits of Lesion-Harvester we show that augmenting the DeepLesion annotations with our harvested lesions allows state-of-the-art detectors to boost their average precision by 7 to 10%.
Postpartum Contraceptive Use and Its Determinants in Ethiopia: A Systematic Review and Meta-analysis

Background Postpartum contraceptive use is defined as the avoidance of short spaced pregnancies and unintended pregnancy through the first 12 months after delivery. In Ethiopia, different studies have been conducted to assess the prevalence of postpartum contraceptive use and associated factors. The findings of these studies were inconsistent and characterized by great variability. Therefore, the aim of this systematic review and meta-analysis was to estimate the pooled prevalence of postpartum contraceptive use and determinants in Ethiopia using the accessible studies. Methods The articles were identified through electronic search of reputable databases (MEDLINE through PubMed, EMBASE, HINARI, Science Direct, and Cochrane Library) and the hand search of reference listed in previous prevalence studies to retrieve more. 18 articles are included based on a comprehensive list of inclusion and exclusion criteria. Two authors independently extracted all necessary data using a standardized data extraction format. STATA 14 statistical software was used to analyze the data. The Cochrane Q and I2 test were used to assess the heterogeneity between the studies. A random effects model was calculated to estimate the pooled prevalence of postpartum contraceptive use. Moreover, the determinants for family planning use were reviewed. Results The pooled prevalence of family planning use among mothers during the postpartum period in Ethiopia was 48.11% (95% CI: 36.96, 59.27). Besides, subgroup analysis revealed that the highest family planning use prevalence among postpartum mothers was observed in Addis Ababa, 65.41 (95% CI: 48.71, 82.11). Resumed sexual activity: 7.91 (95% CI: 4.62, 13.55), antenatal care: 4.98 (95% CI: 2.34, 10.21), secondary school and above level of maternal education: 3.53 (95% CI: 1.67, 7.45), postnatal care: 3.16 (95% CI: 1.7, 5.88), menses resumption: 3.12 (95% CI: 1.52, 6.39), and ≥6 months of postpartum period: 2.78 (95% CI: 1.97, 3.93) have shown a positive association with the use of family planning among mothers in the postpartum epoch. Conclusions In this study, family planning use among mothers of the postpartum period in Ethiopia was significantly low compared to the existing global commendation on postpartum contraceptive use. Resumed sexual activity, antenatal care, secondary and above level of maternal education, postnatal care, menses resumption, and postpartum period ≥ 6 months were found to be significantly associated with postpartum contraceptive use.
Assessment of the Burden of Multiple Sclerosis Patients' Caregivers in Saudi Arabia

Introduction Multiple sclerosis (MS) is a disease that constitutes a high burden on both patients and caregivers. Although many studies have assessed the burden of MS caregivers worldwide, no such studies have been conducted in Saudi Arabia. In this study, we aim to assess the burden of MS patients' caregivers in Saudi Arabia. Methods This cross-sectional study included caregivers of MS patients (for >1 year) who visited the neurology clinic at King Abdulaziz Medical City, Jeddah, Saudi Arabia, between July 2017 and December 2018. The study utilized the Zarit Burden Interview (ZBI) to assess the burden on MS patients' caregivers. In addition, the caregivers' demographic profile and certain information regarding the patient were also collected through an oral interview. Results There were a total of 219 respondents, of which 117 (53.4%) male caregivers. For ZBI, the majority of caregivers reported little or no burden (57.1%), followed by a mild to moderate burden (30.1%), then a moderate to severe burden (10.5%), and only five (2.3%) reported a severe burden. Conclusion Our results showed a limited burden of MS on the life caregivers of MS patients. We stress the importance of assessing the burden in MS patients and caregivers as routine practice with the other important measures such as quality of life and medication compliance. The finding of this study will help in encouraging medical centers to establish more specialized MS clinics that put into consideration the psychological factors, burden of the disease, multidisciplinary approach, and support groups, which are currently few in number.
The efficacy of new oral contrast mixture for computed tomography enterography

Purpose To show the reliabilities of the new mixture (composed of water, methylcellulose, lactulose, locust bean gum, and sorbitol) and to compare the luminal distension and radiological confidence scores of this solution with water-lactulose mixture. Material and methods Computed tomography enterography (CTE) images in a three-year period were included randomly in our institutional review board-approved and retrospective study. Ninety-one patients drank a lactulose and water mixture (Group 1), and 54 patients drank the new mixture (Group 2). Patients who drank the oral contrast agent were taken to a 64-detector row multiple detector computed tomography machine. Coronal and sagittal reformatted images were also formed. The gastrointestinal tract was divided into 11 segments for scoring. Each segment was graded for distensional and radiological confidence. CTE images were evaluated by two radiologists. Results Inter- and intra-reader reliabilities were good or excellent for all gastrointestinal segments in both groups (p < 0.001). The best κ values were obtained in sigmoid colon assessments. Lower agreement values were detected in duodenum and jejunum scores. The new mixture group (Group 2) showed better results than Group 1 for ileum and colonic segments according to distension and confidence scorings. Conclusions Inter- and intra-reader reliabilities of the new mixture were good or excellent for CTE. The new mixture seems to be more efficient and reliable for ileum and colon. The new mixture can increase bowel distention, radiological confidence, and quality in CTE evaluations.
BMP4 induces asymmetric cell division in human glioma stem-like cells

Glioblastoma (GBM) is a malignant tumor with a high recurrence rate and has very poor prognosis in humans. The median survival is still <2 years. Therefore, a new treatment strategy should be established. Recently, this cancer has been thought to be heterogeneous, consisting of cancer stem cells (CSCs) that are self-renewable, multipotent, and treatment resistant. So various strategies targeting glioma stem-like cells (GSCs) have been investigated. This study focused on strategies targeting GSCs through the induction of differentiation using bone morphogenetic protein 4 (BMP4). The expression of CD133, a cancer stem cell marker, under BMP4 treatment in GSCs was examined using flow cytometry, western blotting, and quantitative PCR. Immunofluorescent staining of GSCs was also performed to examine the type of cell division: asymmetric cell division (ACD) or symmetric cell division (SCD). We obtained the following results. The BMP4 treatment caused downregulation of CD133 expression. Moreover, it induced ACD in GSCs. While the ACD ratio was 23% without BMP4 treatment, it was 38% with BMP4 treatment (P=0.004). Furthermore, the tumor sphere assay demonstrated that BMP4 suppresses self-renewal ability. In conclusion, these findings may provide a new perspective on how BMP4 treatment reduces the tumorigenicity of GSCs.
Genes Positively Selected in Domesticated Mammals Are Significantly Dysregulated in the Blood of Individuals with Autism Spectrum Disorders

Human self-domestication (i.e., the presence of traits in our species that are commonly found in domesticated animals) has been hypothesized to have contributed to the emergence of many human-specific features, including aspects of our cognition and behavior. Signs of self-domestication have been claimed to be attenuated in individuals with autism spectrum disorders (ASD), this conceivably accounting for facets of their distinctive cognitive and behavioral profile, although this possibility needs to be properly tested. In this study, we have found that candidate genes for mammal domestication, but not for neural crest development and function, are significantly dysregulated in the blood of subjects with ASD. The set of differentially expressed genes (DEGs) is enriched in biological and molecular processes, as well as in pathological phenotypes, of relevance for the etiology of ASD, like lipid metabolism, cell apoptosis, the activity of the insulin-like growth factor, gene expression regulation, skin/hair anomalies, musculoskeletal abnormalities, and hearing impairment. Moreover, among the DEGs, there are known candidates for ASD and/or genes involved in biological processes known to be affected in ASD. Our findings give support to the view that one important aspect of the etiopathogenesis of ASD is the abnormal manifestation of features of human self-domestication.