Miracle-dz/UniFAR-FTUs huggingface dataset:
An epidemic model for economical impact predicting and spatiotemporal spreading of COVID-19

Since the emergence of a new strain of coronavirus known as SARS-CoV-2, many countries around the world have reported cases of COVID-19 disease caused by this virus. Numerous people's lives have been affected both from a health and an economic point of view. The long tradition of using mathematical models to generate insights about the transmission of a disease, as well as new computer techniques such as Artificial Intelligence, have opened the door to diverse investigations providing relevant information about the evolution of COVID-19. In this research, we seek to advance the existing epidemiological models based on microscopic Markov chains to predict the impact of the pandemic at medical and economic levels. For this purpose, we have made use of the Spanish population movements based on mobile-phone geographically-located information to determine its economic activity using Artificial Intelligence techniques and have developed a novel advanced epidemiological model that combines this information with medical data. With this tool, scenarios can be released with which to determine which restriction policies are optimal and when they have to be applied both to limit the destruction of the economy and to avoid the feared possible upsurge of the disease.
Deep Learning-based Initialization of Iterative Reconstruction for Breast Tomosynthesis

Reconstruction of digital breast tomosynthesis is a challenging problem due to the limited angle data available in such systems. Due to memory limitations, deep learning-based methods can help improve these reconstructions, but can not (yet) attain sufficiently high resolution. In addition to this practical issue, questions remain on the possibility of such models introducing 'ghost' information from the training data that is not compatible with the projection data. To take advantage of some of the benefits of deep learning-based reconstructions while avoiding these limitations, we propose to use the low resolution deep learning-based reconstruction as an initialization of a regular high resolution iterative method. The network was trained using digital phantoms, some based on a mathematical model and some derived from patient dedicated breast CT scans. The output of this network was then used as initialization for 10 000 iterations of MLTR for nine patient based phantoms that were not included in the training. The same nine cases were also reconstructed without any initialization for comparison. The reconstructions including initialization were found to reach a lower mean squared error than those without, and visual inspection found much improved retrieval of the breast outline and depiction of the skin, confirming that adding the deep learning-based initialization adds valuable information to the reconstruction.
Action and Perception as Divergence Minimization

We introduce a unified objective for action and perception of intelligent agents. Extending representation learning and control, we minimize the joint divergence between the combined system of agent and environment and a target distribution. Intuitively, such agents use perception to align their beliefs with the world, and use actions to align the world with their beliefs. Minimizing the joint divergence to an expressive target maximizes the mutual information between the agent's representations and inputs, thus inferring representations that are informative of past inputs and exploring future inputs that are informative of the representations. This lets us explain intrinsic objectives, such as representation learning, information gain, empowerment, and skill discovery from minimal assumptions. Moreover, interpreting the target distribution as a latent variable model suggests powerful world models as a path toward highly adaptive agents that seek large niches in their environments, rendering task rewards optional. The framework provides a common language for comparing a wide range of objectives, advances the understanding of latent variables for decision making, and offers a recipe for designing novel objectives. We recommend deriving future agent objectives the joint divergence to facilitate comparison, to point out the agent's target distribution, and to identify the intrinsic objective terms needed to reach that distribution.
TopoMap: A 0-dimensional Homology Preserving Projection of High-Dimensional Data

Multidimensional Projection is a fundamental tool for high-dimensional data analytics and visualization. With very few exceptions, projection techniques are designed to map data from a high-dimensional space to a visual space so as to preserve some dissimilarity (similarity) measure, such as the Euclidean distance for example. In fact, although adopting distinct mathematical formulations designed to favor different aspects of the data, most multidimensional projection methods strive to preserve dissimilarity measures that encapsulate geometric properties such as distances or the proximity relation between data objects. However, geometric relations are not the only interesting property to be preserved in a projection. For instance, the analysis of particular structures such as clusters and outliers could be more reliably performed if the mapping process gives some guarantee as to topological invariants such as connected components and loops. This paper introduces TopoMap, a novel projection technique which provides topological guarantees during the mapping process. In particular, the proposed method performs the mapping from a high-dimensional space to a visual space, while preserving the 0-dimensional persistence diagram of the Rips filtration of the high-dimensional data, ensuring that the filtrations generate the same connected components when applied to the original as well as projected data. The presented case studies show that the topological guarantee provided by TopoMap not only brings confidence to the visual analytic process but also can be used to assist in the assessment of other projection methods.
A Software-Defined Networking Solution for Transparent Session and Service Continuity in Dynamic Multi-Access Edge Computing

Multi-Access Edge Computing (MEC) will allow implementing low-latency services that have been unfeasible so far. The European Telecommunications Standards Institute (ETSI) and the 3rd Generation Partnership Project (3GPP) are working towards the standardization of MEC in 5G networks and the corresponding solutions for routing user traffic to applications in local area networks. Nevertheless, there are neither practical implementations for dynamically relocating applications from the core to a MEC host nor from one MEC host to another ensuring service continuity. In this article we propose a solution based on Software-Defined Networking (SDN) to create a new instance of the IP anchor point to dynamically redirect User Equipment (UE) traffic to a new physical location (e.g., an edge infrastructure). We also present a novel approach that leverages SDN to replicate the previous context of the connection in the new instance of the IP anchor point, thus guaranteeing Session and Service Continuity (SSC), and compare it with alternative state replication strategies. This approach can be used to implement edge services in 4G or 5G networks.
FairGNN: Eliminating the Discrimination in Graph Neural Networks with Limited Sensitive Attribute Information

Graph neural networks (GNNs) have shown great power in modeling graph structured data. However, similar to other machine learning models, GNNs may make predictions biased on protected sensitive attributes, e.g., skin color, gender, and nationality. Because machine learning algorithms including GNNs are trained to faithfully reflect the distribution of the training data which often contains historical bias towards sensitive attributes. In addition, the discrimination in GNNs can be magnified by graph structures and the message-passing mechanism. As a result, the applications of GNNs in sensitive domains such as crime rate prediction would be largely limited. Though extensive studies of fair classification have been conducted on i.i. d data, methods to address the problem of discrimination on non-i.i. d data are rather limited. Furthermore, the practical scenario of sparse annotations in sensitive attributes is rarely considered in existing works. Therefore, we study the novel and important problem of learning fair GNNs with limited sensitive attribute information. FairGNN is proposed to eliminate the bias of GNNs whilst maintaining high node classification accuracy by leveraging graph structures and limited sensitive information. Our theoretical analysis shows that FairGNN can ensure the fairness of GNNs under mild conditions given limited nodes with known sensitive attributes. Extensive experiments on real-world datasets also demonstrate the effectiveness of FairGNN in debiasing and keeping high accuracy.
Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs

We present an $\tilde{O}(m+n^{1.5})$-time randomized algorithm for maximum cardinality bipartite matching and related problems (e.g. transshipment, negative-weight shortest paths, and optimal transport) on m-edge, n-node graphs. For maximum cardinality bipartite matching on moderately dense graphs, i.e. $m=\Omega(n^{1.5})$, our algorithm runs in time nearly linear in the input size and constitutes the first improvement over the classic $O(m\sqrt{n})$-time [Dinic 1970; Hopcroft-Karp 1971; Karzanov 1973] and $\widetilde{O}(n^{\omega})$-time algorithms [Ibarra-Moran 1981] (where currently $\omega\approx 2.373$). On sparser graphs, i.e. when $m=n^{9/8+\delta}$ for any constant $\delta > 0$, our result improves upon the recent advances of [Madry 2013] and [Liu-Sidford 2020b, 2020a] which achieve an $\widetilde{O}(m^{4/3+o(1)})$ runtime. We obtain these results by combining and advancing recent lines of research in interior point methods (IPMs) and dynamic graph algorithms. First, we simplify and improve the IPM of [v.d. Brand-Lee-Sidford-Song 2020], providing a general primal-dual IPM framework and new sampling-based techniques for handling infeasibility induced by approximate linear system solvers. Second, we provide a simple sublinear-time algorithm for detecting and sampling high-energy edges in electric flows on expanders and show that when combined with recent advances in dynamic expander decompositions, this yields efficient data structures for maintaining the iterates of both [v.d. Brand et al.] and our new IPMs. Combining this general machinery yields a simpler $\widetilde{O}(n\sqrt{m})$ time algorithm for matching based on the logarithmic barrier function, and our state-of-the-art $\widetilde{O}(m+n^{1.5})$ time algorithm for matching based on the [Lee-Sidford 2014] barrier (as regularized in [v.d.Brand et al.]).
Optimization of Plasmid Extraction Method from Lactic Acid Bacteria

By combining the extraction method of plasmid from E.coil and the method of plasmid from lactic acid bacteria reported before to improve the common methods used in plasmid extraction kit,a fast,safe and high efficient method for extraction of plasmid DNA from lactic acid bacteria was developed.The optimal concentration of lysozyme was proved to be 20mg/ml,the optimal processing time was 30min,and the usage toxic ethidium bromide was avoided during the whole process of extraction.Experimental results showed that,the developed method showed an increased extraction efficiency,as well as good reproducibility,and thus provided a good foundation for the following molecular improvement of lactic acid bacteria.
DeformSyncNet

Shape deformation is an important component in any geometry processing toolbox. The goal is to enable intuitive deformations of single or multiple shapes or to transfer example deformations to new shapes while preserving the plausibility of the deformed shape(s). Existing approaches assume access to point-level or part-level correspondence or establish them in a preprocessing phase, thus limiting the scope and generality of such approaches. We propose DeformSyncNet, a new approach that allows consistent and synchronized shape deformations without requiring explicit correspondence information. Technically, we achieve this by encoding deformations into a class-specific idealized latent space while decoding them into an individual, model-specific linear deformation action space, operating directly in 3D. The underlying encoding and decoding are performed by specialized (jointly trained) neural networks. By design, the inductive bias of our networks results in a deformation space with several desirable properties, such as path invariance across different deformation pathways, which are then also approximately preserved in real space. We qualitatively and quantitatively evaluate our framework against multiple alternative approaches and demonstrate improved performance.
Comparing Fair Ranking Metrics

Ranking is a fundamental aspect of recommender systems. However, ranked outputs can be susceptible to various biases; some of these may cause disadvantages to members of protected groups. Several metrics have been proposed to quantify the (un)fairness of rankings, but there has not been to date any direct comparison of these metrics. This complicates deciding what fairness metrics are applicable for specific scenarios, and assessing the extent to which metrics agree or disagree. In this paper, we describe several fair ranking metrics in a common notation, enabling direct comparison of their approaches and assumptions, and empirically compare them on the same experimental setup and data set. Our work provides a direct comparative analysis identifying similarities and differences of fair ranking metrics selected for our work.
Using Household Grants to Benchmark the Cost Effectiveness of a USAID Workforce Readiness Program

Author(s): McIntosh, Craig; Zeitlin, Andrew | Abstract: We use a randomized experiment to compare a workforce training program to cash transfers in Rwanda. Conducted in a sample of poor and underemployed youth, this study measures the impact of the training program not only relative to a control group but relative to the counterfactual of simply disbursing the cost of the program directly to beneficiaries. While the training program was successful in improving a number of core outcomes (productive hours, assets, savings, and subjective well-being), cost-equivalent cash transfers move all these outcomes as well as consumption, income, and wealth. In the head-to-head costing comparison cash proves superior across a number of economic outcomes, while training outperforms cash only in the production of business knowledge. We find little evidence of complementarity between human and physical capital interventions, and no signs of heterogeneity or spillover effects.
Towards Distributed Privacy-Preserving Prediction

In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns. Even the trained model parameters or prediction can pose serious privacy leakage. To address these problems, we demonstrate a generally applicable Distributed Privacy-Preserving Prediction (DPPP) framework, in which instead of sharing more sensitive data or model parameters, an untrusted aggregator combines only multiple models’ predictions under provable privacy guarantee. Our framework integrates two main techniques to guarantee individual privacy. First, we introduce the improved Binomial Mechanism and Discrete Gaussian Mechanism to achieve distributed differential privacy. Second, we utilize homomorphic encryption to ensure that the aggregator learns nothing but the noisy aggregated prediction. Experimental results demonstrate that our framework has comparable performance to the non-private frameworks and delivers better results than the local differentially private framework and standalone framework.
Feasibility of endoscopic suturing to prevent adverse events and hospitalization after endoscopic submucosal dissection

Abstract Background and study aim Endoscopic submucosal dissection (ESD) enables en bloc removal of gastrointestinal epithelial lesions but can leave a large mucosal defect, which can lead to inpatient observation and delayed bleeding or perforation. The aim of this study was to examine the safety and effectiveness of endoscopic suturing in closing ESD defects to prevent adverse events. Patients and methods In this single-center prospective cohort study, endoscopic suturing was performed to close ESD defects in the stomach or rectum. Suturing was performed in the antegrade position starting from the edge most distal to the endoscope insertion site, moving from right to left, left to right manner before ending at the edge most proximal to the endoscope insertion site. Results In total, 31 patients (mean age 65.6, 71 % male) received endoscopic suturing after gastric (58.1 %) or rectal (41.9 %) ESD. Mean lesion size was 27.4 ± 16.2 mm and mean suturing time was 13.4 ± 5.9 min. Complete closure was achieved in all patients. Same-day discharge occurred in 58.1 % of patients; the remainder were hospitalized with mean length of stay of 1 ± 0.6 day. There were no instances of delayed bleeding or delayed perforation (0 %, 95 % CI: 0–11.5 %). No recurrences were found on surveillance endoscopy. Conclusions Based on this small prospective study, endoscopic suturing of post-ESD defects in the stomach and rectum appears to be feasible, safe, and potentially effective in preventing bleeding or perforation. Further larger controlled studies, however, are needed to validate these findings.
Identification of Key Genes and Pathways Associated with Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is the leading cause of severe, permanent vision loss among the elderly in the developed world. The cellular and molecular pathogenesis of initiation and development of AMD remain poorly delineated. The limited resources of the human AMD RPE/choroid tissues impeded the extensive study of the disease. To better understand the molecular and pathway changes in human AMD RPE/choroid tissues, we searched the literature and found three independent studies using high-throughput technology to analyze gene expression in 54 human AMD RPE/choroid tissues and 46 age-matched healthy controls. We downloaded these data, pooled them together, and reanalyzed the difference between molecular and pathways by the Ingenuity Pathway Analysis (IPA) database. Totally, 353 differentially expressed genes (DEGs) were identified, among which 181 genes were downregulated and 172 genes were upregulated in RPE/choroid of AMD patients. Furthermore, several significantly enriched biological processes, including cancer, organismal injury and abnormalities, and ophthalmic disease, were identified associated with these DEGs. By analysis of canonical pathway, the phototransduction pathway and atherosclerosis signaling were the top two significant canonical pathways altered in RPE/choroid tissues in human AMD. As expected, several ophthalmic disease-related molecules, including RHO, PDE6A, 3′,5′-cyclic-GMP phosphodiesterase, and G protein alpha, were in the central nodes of disease network. The bioinformatics technology combined with the existing high-throughput data was applied to evaluate the underlying key genes and pathways in human AMD tissues, which may predict downstream and upstream biological processes and identify potential therapeutic intervention targets in human AMD.
Circular RNA cir-ITCH Is a Potential Therapeutic Target for the Treatment of Castration-Resistant Prostate Cancer

cir-ITCH, a well-known tumor-suppressive circular RNA, plays a critical role in different cancers. However, its expression and functional role in prostate cancer (PCa) are unclear. Herein, we explored the potential mechanism and tumor-inhibiting role of cir-ITCH in PCa. Using reverse transcriptase polymerase chain reaction assay, we analyzed the expression of cir-ITCH in PCa and paired adjacent nontumor tissue samples resected during surgical operation, as well as in two cell lines of human PCa (LNCaP and PC-3) and the immortalized normal prostate epithelial cell line (RWPE-1). Cell viability and migration of PCa cell lines were evaluated using CCK-8 and wound-healing assays. Expression of key proteins of the Wnt/β-catenin and PI3K/AKT/mTOR pathways was detected using western blotting. We found that cir-ITCH expression was typically downregulated in the tissues and cell lines of PCa compared to that in the peritumoral tissue and in RWPE-1 cells, respectively. The results showed that cir-ITCH overexpression significantly inhibits the proliferation, migration, and invasion of human PCa cells and that reciprocal inhibition of expression occurred between cir-ITCH and miR-17. Proteins in the Wnt/β-catenin and PI3K/AKT/mTOR pathways were downregulated by overexpression of cir-ITCH both in androgen receptor-positive LNCaP cells and androgen receptor-negative PC-3 cells. Taken together, these data demonstrated that cir-ITCH plays a tumor-suppressive role in human PCa cells, partly through the Wnt/β-catenin and PI3K/AKT/mTOR pathways. Thus, cir-ITCH may serve as a novel therapeutic target for the treatment of PCa, especially castration-resistant prostate cancer.
Nonlinear multispectral imaging for tumor delineation

Abstract. Significance: In breast-preserving tumor surgery, the inspection of the excised tissue boundaries for tumor residue is too slow to provide feedback during the surgery. The discovery of positive margins requires a new surgery which is difficult and associated with low success. If the re-excision could be done immediately this is believed to improve the success rate considerably. Aim: Our aim is for a fast microscopic analysis that can be done directly on the excised tissue in or near the operating theatre. Approach: We demonstrate the combination of three nonlinear imaging techniques at selected wavelengths to delineate tumor boundaries. We use hyperspectral coherent anti-Stokes Raman scattering (CARS), second harmonic generation (SHG), and two-photon excited fluorescence (TPF) on excised patient tissue. Results: We show the discriminatory power of each of the signals and demonstrate a sensitivity of 0.87 and a specificity of 0.95 using four CARS wavelengths in combination with SHG and TPF. We verify that the information is independent of sample treatment. Conclusions: Nonlinear multispectral imaging can be used to accurately determine tumor boundaries. This demonstration using microscopy in the epi-direction directly on thick tissue slices brings this technology one step closer to clinical implementation.
Study on mutation breeding and fermentation of antimicrobial lipopeptides yielding bacterium with 20keV N~+ ion beam implantation

Bacillus amyloliquefaciens ES-2 was implanted with 20keV N+ ion beam to breed mutants of high yield of antimicrobial lipopeptides. The dose effects of the nitrogen ion implantation on the survival and positive mu- tations rate was studied. The mutant strain designated as B. amyloliquefaciens ES-2-4 showing higher yield of antim- icrobial lipopeptides was obtained. The concentration of the lipopeptides in fermentation liquid increased by 15.2% compared with the original strain. We also studied the fermentation process between the mutant and the original strain. It indicated that the mutant with shorter lag phase, longer stable phase and higher yield of the lipopeptides, which was suitable for large-scale production.