Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-echelon Supply Chains with Uncertain Seasonal Demands and Lead Times Using Deep Reinforcement Learning

We address the problem of production planning and distribution in multi-echelon supply chains. We consider uncertain demands and lead times which makes the problem stochastic and non-linear. A Markov Decision Process formulation and a Non-linear Programming model are presented. As a sequential decision-making problem, Deep Reinforcement Learning (RL) is a possible solution approach. This type of technique has gained a lot of attention from Artificial Intelligence and Optimization communities in recent years. Considering the good results obtained with Deep RL approaches in different areas there is a growing interest in applying them in problems from the Operations Research field. We have used a Deep RL technique, namely Proximal Policy Optimization (PPO2), to solve the problem considering uncertain, regular and seasonal demands and constant or stochastic lead times. Experiments are carried out in different scenarios to better assess the suitability of the algorithm. An agent based on a linearized model is used as a baseline. Experimental results indicate that PPO2 is a competitive and adequate tool for this type of problem. PPO2 agent is better than baseline in all scenarios with stochastic lead times (7.3-11.2%), regardless of whether demands are seasonal or not. In scenarios with constant lead times, the PPO2 agent is better when uncertain demands are non-seasonal (2.2-4.7%). The results show that the greater the uncertainty of the scenario, the greater the viability of this type of approach.
Multi-granularity Association Learning Framework for on-the-fly Fine-Grained Sketch-based Image Retrieval

Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo in a given query sketch. However, its widespread applicability is limited by the fact that it is difficult to draw a complete sketch for most people, and the drawing process often takes time. In this study, we aim to retrieve the target photo with the least number of strokes possible (incomplete sketch), named on-the-fly FG-SBIR (Bhunia et al. 2020), which starts retrieving at each stroke as soon as the drawing begins. We consider that there is a significant correlation among these incomplete sketches in the sketch drawing episode of each photo. To learn more efficient joint embedding space shared between the photo and its incomplete sketches, we propose a multigranularity association learning framework that further optimizes the embedding space of all incomplete sketches. Specifically, based on the integrity of the sketch, we can divide a complete sketch episode into several stages, each of which corresponds to a simple linear mapping layer. Moreover, our framework guides the vector space representation of the current sketch to approximate that of its later sketches to realize the retrieval performance of the sketch with fewer strokes to approach that of the sketch with more strokes. In the experiments, we proposed more realistic challenges, and our method achieved superior early retrieval efficiency over the stateof-the-art methods and alternative baselines on two publicly available fine-grained sketch retrieval datasets.
SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps

Real-time graphics applications require high-quality textured materials to convey realism in virtual environments. Generating these textures is challenging as they need to be visually realistic, seamlessly tileable, and have a small impact on the memory consumption of the application. For this reason, they are often created manually by skilled artists. In this work, we present SeamlessGAN, a method capable of automatically generating tileable texture maps from a single input exemplar. In contrast to most existing methods, focused solely on solving the synthesis problem, our work tackles both problems, synthesis and tileability, simultaneously. Our key idea is to realize that tiling a latent space within a generative network trained using adversarial expansion techniques produces outputs with continuity at the seam intersection that can then be turned into tileable images by cropping the central area. Since not every value of the latent space is valid to produce high-quality outputs, we leverage the discriminator as a perceptual error metric capable of identifying artifact-free textures during a sampling process. Further, in contrast to previous work on deep texture synthesis, our model is designed and optimized to work with multi-layered texture representations, enabling textures composed of multiple maps such as albedo, normals, etc. We extensively test our design choices for the network architecture, loss function, and sampling parameters. We show qualitatively and quantitatively that our approach outperforms previous methods and works for textures of different types.
Convolutional dual graph Laplacian sparse coding

In recent years, graph signal processing (GSP) technology has become popular in various fields, and graph Laplacian regularizers have also been introduced into convolutional sparse representation. This paper proposes a convolutional sparse representation model based on the dual graph Laplacian regularizer to ensure effective application of a dual graph signal smoothing prior on the rows and columns of input images.The graph Laplacian matrix contains the gradient information of the image and the similarity information between pixels, and can also describe the degree of change of the graph, so the image can be smoothed. Compared with the single graph smoothing prior, the dual graph has a simple structure, relaxes the conditions, and is more conducive to image restoration using the image signal prior. In this paper, this paper formulated the corresponding minimization problem using the proposed model, and subsequently used the alternating direction method of multiplication (ADMM) algorithm to solve it in the Fourier domain.Finally, using random Gaussian white noise for the denoising experiments. Compared with the single graph smoothing prior,the denoising results of the model with dual graph smoothing prior proposed in this paper has fewer noise points and clearer texture.
Roadside Lidar Vehicle Detection and Tracking Using Range And Intensity Background Subtraction

In this study, we developed the solution of roadside LiDAR object detection using a combination of two unsupervised learning algorithms. The 3D point clouds are ﬁrstly converted into spherical coordinates and ﬁlled into the elevation-azimuth matrix using a hash function. After that, the raw LiDAR data were rearranged into the new data structure to store the information of range, azimuth, and intensity. Then, the dynamic mode decomposition method is applied to decompose the LiDAR data into low-rank backgrounds and sparse foregrounds based on intensity channel pattern recognition. The coarse-ﬁne triangle algorithm (CFTA) automatically ﬁnds the dividing value to separate the moving targets from static background according to range information. After intensity and range background subtraction, the foreground moving objects will be detected using a density-based detector and encoded into the state-space model for tracking. The output of the proposed solution includes vehicle trajectories that can enable many mobility and safety applications. The method was validated at both path and point levels and outperformed the state of the art. In contrast to the previous methods that process directly on the scattered and discrete point clouds, the dynamic classiﬁcation method can establish the less sophisticated linear relationship of the 3D measurement data, which captures the spatial-temporal structure that we often desire.
Non-Stationary Representation Learning in Sequential Linear Bandits

In this paper, we study representation learning for multi-task decision-making in non-stationary environments. We consider the framework of sequential linear bandits, where the agent performs a series of tasks drawn from different environments. The embeddings of tasks in each environment share a low-dimensional feature extractor called representation, and representations are different across environments. We propose an online algorithm that facilitates efficient decision-making by learning and transferring non-stationary representations in an adaptive fashion. We prove that our algorithm significantly outperforms the existing ones that treat tasks independently. We also conduct experiments using both synthetic and real data to validate our theoretical insights and demonstrate the efficacy of our algorithm.
Automated Reinforcement Learning: An Overview

Reinforcement Learning and recently Deep Reinforcement Learning are popular methods for solving sequential decision making problems modeled as Markov Decision Processes. RL modeling of a problem and selecting algorithms and hyper-parameters require careful considerations as different configurations may entail completely different performances. These considerations are mainly the task of RL experts; however, RL is progressively becoming popular in other fields where the researchers and system designers are not RL experts. Besides, many modeling decisions, such as defining state and action space, size of batches and frequency of batch updating, and number of timesteps are typically made manually. For these reasons, automating different components of RL framework is of great importance and it has attracted much attention in recent years. Automated RL provides a framework in which different components of RL including MDP modeling, algorithm selection and hyper-parameter optimization are modeled and defined automatically. In this article, we explore the literature and present recent work that can be used in automated RL. Moreover, we discuss the challenges, open questions and research directions in AutoRL.
Demographic History and Natural Selection Shape Patterns of Deleterious Mutation Load and Barriers to Introgression across Populus Genome

Abstract Hybridization and resulting introgression are important processes shaping the tree of life and appear to be far more common than previously thought. However, how the genome evolution was shaped by various genetic and evolutionary forces after hybridization remains unresolved. Here we used whole-genome resequencing data of 227 individuals from multiple widespread Populus species to characterize their contemporary patterns of hybridization and to quantify genomic signatures of past introgression. We observe a high frequency of contemporary hybridization and confirm that multiple previously ambiguous species are in fact F1 hybrids. Seven species were identified, which experienced different demographic histories that resulted in strikingly varied efficacy of selection and burdens of deleterious mutations. Frequent past introgression has been found to be a pervasive feature throughout the speciation of these Populus species. The retained introgressed regions, more generally, tend to contain reduced genetic load and to be located in regions of high recombination. We also find that in pairs of species with substantial differences in effective population size, introgressed regions are inferred to have undergone selective sweeps at greater than expected frequencies in the species with lower effective population size, suggesting that introgression likely have higher potential to provide beneficial variation for species with small populations. Our results, therefore, illustrate that demography and recombination have interplayed with both positive and negative selection in determining the genomic evolution after hybridization.
Construction of a Model for Predicting the Severity of Diverticular Bleeding in an Elderly Population

Objective To identify the risk factors for severe diverticular bleeding in an elderly population. Methods Using a comprehensive computerized hospital database, severe and non-severe diverticular bleeding cases were compared for 19 factors: the age, sex, body mass index, comorbid conditions (hypertension, cardiovascular disease, cerebrovascular disease, and chronic renal failure, including those undergoing dialysis), history of diverticular bleeding, use of low-dose aspirin, use of antiplatelet agent besides aspirin, use of anticoagulant agent, use of prednisolone, use of non-steroidal anti-inflammatory drugs, use of cyclooxygenase-2 selective inhibitors, changes in vital signs, hypoalbuminemia, bilateral diverticula, identification of bleeding lesion, and rebleeding. Severe bleeding was defined as the need for blood transfusion, emergency surgery, or vascular embolization. Patients A total of 258 patients were admitted for lower gastrointestinal bleeding between August 2010 and July 2020, among whom 120 patients over 65 years old diagnosed with diverticular bleeding were included in this study. Results Fifty-one patients (43%) had severe diverticular bleeding. Independent risk factors for severe diverticular bleeding were as follows: change in vital signs [odds ratio (OR), 5.23; 95% confidence interval (CI), 1.9-14.4; p=0.0014], hypoalbuminemia (OR, 12.3; 95% CI, 1.97-77.3; p=0.0073), bilateral diverticula (OR, 3.47; 95% CI, 1.33-9.02; p=0.011), and rebleeding (OR, 5.92; 95% CI, 2.21-15.8; p<0.001). The area under the receiver operating characteristic curve was 0.79 after cross validation. Conclusion Severe diverticular bleeding in elderly population may be predicted by changes in their vital signs, hypoalbuminemia, bilateral diverticula, and rebleeding.
Features of patients who developed hepatocellular carcinoma after direct-acting antiviral treatment for hepatitis C Virus

Background The features of hepatitis C virus patients with a sustained virologic response (SVR) who developed hepatocellular carcinoma (HCC) after direct-acting antiviral (DAA) therapy are unclear. Methods The study population included 1494 DAA-SVR patients without a history of HCC. The cumulative carcinogenesis rate after the end of treatment (EOT) and factors related to HCC were analyzed. Results Sixty (4.0%) patients developed HCC during a median observation period of 47.6 months. At four years, the cumulative carcinogenesis rate was 4.7%. A Cox proportional hazards analysis showed that age ≥73 years (hazard ratio [HR]: 2.148), male sex (HR: 3.060), hyaluronic acid (HA) ≥75 ng/mL (HR: 3.996), alpha-fetoprotein at EOT (EOT-AFP) ≥5.3 ng/mL (HR: 4.773), and albumin at EOT (EOT-Alb) <3.9 g/dL (HR: 2.305) were associated with HCC development. Especially, EOT-AFP ≥5.3 ng/mL was associated with HCC development after 3 years from EOT (HR: 6.237). Among patients who developed HCC, AFP did not increase in patients with EOT-AFP <5.3 ng/mL at the onset of HCC. Of these 5 factors, EOT-AFP ≥5.3 ng/mL was scored as 2 points; the others were scored as 1 point. The 4-year cumulative carcinogenesis rate for patients with total scores of 0–2, 3–4, and 5–6 points were 0.6%, 11.9%, and 27.1%, respectively (p<0.001). Conclusions EOT-AFP ≥5.3 ng/mL is useful for predicting HCC development after an SVR. However, AFP does not increase in patients with EOT-AFP <5.3 ng/mL at the onset of HCC. The combination of EOT-AFP, age, sex, HA, and EOT-Alb is important for predicting carcinogenesis.
‘I told him I’m gonna get it’: intimate partner birth control communication among homeless young women

Abstract Homeless youth have disproportionately high rates of unintended pregnancy and STIs. Enhancing communication between sexual partners can improve sexual health outcomes, yet little is known about this topic among homeless youth; therefore, this study aimed to examine how homeless youth communicate with their partners about birth control. In-depth semi-structured interviews regarding intimate partner birth control communication were conducted with 10 homeless young women aged 14–22 years following their completion of a comprehensive sexual health program (Wahine Talk). We transcribed the interviews verbatim and used a structured, inductive analytic approach to identify themes. Analysis identified three themes: Getting the Conversation Started, Conversation Content, and Impact of Conversation. Birth control conversations were prompted by programme participation, birth control side effects, and family members’ interest in homeless youth becoming pregnant. Barriers to communication included fear and mistimed conversations (e.g. during the initiation of sex). Homeless young people shared simultaneous desires to delay pregnancy and concerns about side effects of birth control use. Discussions about birth control with their partners may demonstrably improve homeless youth’s intimate relationships and family planning efforts. Providers can support homeless young women by helping them plan conversation timing and addressing fear, including the risk of violence.
The effect of a high-polyphenol Mediterranean diet (Green-MED) combined with physical activity on age-related brain atrophy: the Dietary Intervention Randomized Controlled Trial Polyphenols Unprocessed Study (DIRECT PLUS)

ABSTRACT Background The effect of diet on age-related brain atrophy is largely unproven. Objectives We aimed to explore the effect of a Mediterranean diet (MED) higher in polyphenols and lower in red/processed meat (Green-MED diet) on age-related brain atrophy. Methods This 18-mo clinical trial longitudinally measured brain structure volumes by MRI using hippocampal occupancy score (HOC) and lateral ventricle volume (LVV) expansion score as neurodegeneration markers. Abdominally obese/dyslipidemic participants were randomly assigned to follow 1) healthy dietary guidelines (HDG), 2) MED, or 3) Green-MED diet. All subjects received free gym memberships and physical activity guidance. Both MED groups consumed 28 g walnuts/d (+440 mg/d polyphenols). The Green-MED group consumed green tea (3–4 cups/d) and Mankai (Wolffia-globosa strain, 100 g frozen cubes/d) green shake (+800 mg/d polyphenols). Results Among 284 participants (88% men; mean age: 51 y; BMI: 31.2 kg/m2; APOE‐ε4 genotype = 15.7%), 224 (79%) completed the trial with eligible whole-brain MRIs. The pallidum (−4.2%), third ventricle (+3.9%), and LVV (+2.2%) disclosed the largest volume changes. Compared with younger participants, atrophy was accelerated among those ≥50 y old (HOC change: −1.0% ± 1.4% compared with −0.06% ± 1.1%; 95% CI: 0.6%, 1.3%; P < 0.001; LVV change: 3.2% ± 4.5% compared with 1.3% ± 4.1%; 95% CI: −3.1%, −0.8%; P = 0.001). In subjects ≥ 50 y old, HOC decline and LVV expansion were attenuated in both MED groups, with the best outcomes among Green-MED diet participants, as compared with HDG (HOC: −0.8% ± 1.6% compared with −1.3% ± 1.4%; 95% CI: −1.5%, −0.02%; P = 0.042; LVV: 2.3% ± 4.7% compared with 4.3% ± 4.5%; 95% CI: 0.3%, 5.2%; P = 0.021). Similar patterns were observed among younger subjects. Improved insulin sensitivity over the trial was the parameter most strongly associated with brain atrophy attenuation (P < 0.05). Greater Mankai, green tea, and walnut intake and less red and processed meat were significantly and independently associated with reduced HOC decline (P < 0.05). Elevated urinary concentrations of the polyphenols urolithin-A (r = 0.24; P = 0.013) and tyrosol (r = 0.26; P = 0.007) were significantly associated with lower HOC decline. Conclusions A Green-MED (high-polyphenol) diet, rich in Mankai, green tea, and walnuts and low in red/processed meat, is potentially neuroprotective for age-related brain atrophy. This trial was registered at clinicaltrials.gov as NCT03020186.
Chromosome-level genome assembly of the shuttles hoppfish, Periophthalmus modestus

Abstract Background The shuttles hoppfish (mudskipper), Periophthalmus modestus, is one of the mudskippers, which are the largest group of amphibious teleost fishes, which are uniquely adapted to live on mudflats. Because mudskippers can survive on land for extended periods by breathing through their skin and through the lining of the mouth and throat, they were evaluated as a model for the evolutionary sea-land transition of Devonian protoamphibians, ancestors of all present tetrapods. Results A total of 39.6, 80.2, 52.9, and 33.3 Gb of Illumina, Pacific Biosciences, 10X linked, and Hi-C data, respectively, was assembled into 1,419 scaffolds with an N50 length of 33 Mb and BUSCO score of 96.6%. The assembly covered 117% of the estimated genome size (729 Mb) and included 23 pseudo-chromosomes anchored by a Hi-C contact map, which corresponded to the top 23 longest scaffolds above 20 Mb and close to the estimated one. Of the genome, 43.8% were various repetitive elements such as DNAs, tandem repeats, long interspersed nuclear elements, and simple repeats. Ab initio and homology-based gene prediction identified 30,505 genes, of which 94% had homology to the 14 Actinopterygii transcriptomes and 89% and 85% to Pfam familes and InterPro domains, respectively. Comparative genomics with 15 Actinopterygii species identified 59,448 gene families of which 12% were only in P. modestus. Conclusions We present the high quality of the first genome assembly and gene annotation of the shuttles hoppfish. It will provide a valuable resource for further studies on sea-land transition, bimodal respiration, nitrogen excretion, osmoregulation, thermoregulation, vision, and mechanoreception.
Analysis of microRNA expression profiles during the differentiation of chicken embryonic stem cells into male germ cells

Abstract The differentiation of embryonic stem cells (ESCs) into germ cells in vitro could have very promising applications for infertility treatment and could provide an excellent model for uncovering the molecular mechanisms of germline generation. This study aimed to investigate the differentially expressed miRNAs (DEMs) during the differentiation of chicken ESCs (cESCs) into male germ cells and to establish a profile of the DEMs. Cells before and after induction were subjected to miRNA sequencing (miRNA-seq). A total of 113 DEMs were obtained, including 61 upregulated and 52 downregulated DEMs. GO and KEGG enrichment analyses showed that the target genes were enriched mainly in the MAPK signaling pathway, HTLV infection signaling pathway, cell adhesion molecule (CAM)-related pathways, viral myocarditis, Wnt signaling pathway, ABC transporters, TGF-β signaling pathways, Notch signaling pathways and insulin signaling pathway. The target genes of the miRNAs were related to cell binding, cell parts and biological regulatory processes. Six DEMs, let-7k-5p, miR-132c-5p, miR-193a-5p, miR-202-5p, miR-383-5p and miR-6553-3p, were assessed by qRT-PCR, and the results were consistent with the results of miRNA-seq. Based on qRT-PCR and western blot verification, miR-383-5p and its putative target gene STRN3 were selected to construct an STRN3 3'-UTR dual-luciferase gene reporter vector and its mutant vector. The double luciferase reporter activity of the cotransfected STRN3-WT + miR-383-5p mimics group was significantly lower (by approximately 46%) than that of the other five groups (p < 0.01). There was no significant difference in luciferase activity among the other 5 groups. This study establishes a DEM profile during the process of cESC differentiation into male germ cells; illustrates the mechanisms by which miRNAs regulate target genes; provides a theoretical basis for further research on the mechanisms of the formation and regulation of male germ cells; and provides an important strategy for gene editing, animal genetic resource protection and transgenic animal production.