Miracle-dz/UniFAR-FTUs huggingface dataset:
Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

Recommender Systems have shown to be an effective way to alleviate the over-choice problem and provide accurate and tailored recommendations. However, the impressive number of proposed recommendation algorithms, splitting strategies, evaluation protocols, metrics, and tasks, has made rigorous experimental evaluation particularly challenging. Puzzled and frustrated by the continuous recreation of appropriate evaluation benchmarks, experimental pipelines, hyperparameter optimization, and evaluation procedures, we have developed an exhaustive framework to address such needs. Elliot is a comprehensive recommendation framework that aims to run and reproduce an entire experimental pipeline by processing a simple configuration file. The framework loads, filters, and splits the data considering a vast set of strategies (13 splitting methods and 8 filtering approaches, from temporal training-test splitting to nested K-folds Cross-Validation). Elliot(https://github.com/sisinflab/elliot) optimizes hyperparameters (51 strategies) for several recommendation algorithms (50), selects the best models, compares them with the baselines providing intra-model statistics, computes metrics (36) spanning from accuracy to beyond-accuracy, bias, and fairness, and conducts statistical analysis (Wilcoxon and Paired t-test).
The topological origin of the Peierls–Nabarro barrier

Crystals and other condensed matter systems described by density waves often exhibit dislocations. Here we show, by considering the topology of the ground state manifolds (GSMs) of such systems, that dislocations in the density phase field always split into disclinations, and that the disclinations themselves are constrained to sit at particular phase values in the GSM. This constraint on the location of the disclinations results in an energy barrier to dislocation glide. Consequently, the topology of the GSM alone gives rise to a Peierls–Nabarro barrier.
Land Cover Mapping in Limited Labels Scenario: A Survey

Land cover mapping is essential for monitoring global environmental change and managing natural resources. Unfortunately, traditional classification models are plagued by limited training data available in existing land cover products and data heterogeneity over space and time. In this survey, we provide a structured and comprehensive overview of challenges in land cover mapping and machine learning methods used to address these problems. We also discuss the gaps and opportunities that exist for advancing research in this promising direction.
Superdiffusion in spin chains

This review summarizes recent advances in our understanding of anomalous transport in spin chains, viewed through the lens of integrability. Numerical advances, based on tensor-network methods, have shown that transport in many canonical integrable spin chains—most famously the Heisenberg model—is anomalous. Concurrently, the framework of generalized hydrodynamics has been extended to explain some of the mechanisms underlying anomalous transport. We present what is currently understood about these mechanisms, and discuss how they resemble (and differ from) the mechanisms for anomalous transport in other contexts. We also briefly review potential transport anomalies in systems where integrability is an emergent or approximate property. We survey instances of anomalous transport and dynamics that remain to be understood.
DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization

Deployment of facial action unit recognition models has been impeded due to their limited generalization to unseen people and demographics. This work conducts an in-depth generalization analysis across several sources of variance: individuals (40 subjects), genders (male and female), skin types (darker and lighter), and databases (BP4D and DISFA). To help suppress the variance in data, we propose using self-supervised denoising autoencoders to transfer facial expressions of different people onto a common facial template which is then used to train and evaluate each of the models. We show that person-independent models yielded significantly lower performance (55% average F1 and accuracy across 40 subjects) than person-dependent models (60.3 %), leading to a generalization gap of 5.3%. However, normalizing the data with the proposed method significantly increased the performance of person-independent models (59.6%). Similarly, the proposed method was able to significantly reduce the generalization gap when considering gender (2.4%), skin type (5.3%), and dataset (9.4%). These findings represent an important step towards the creation of more generalizable facial action unit recognition systems.
Uniqueness of convex ancient solutions to hypersurface flows

Abstract We show that every convex ancient solution of mean curvature flow with Type I curvature growth is either spherical, cylindrical, or planar. We then prove the corresponding statement for flows by a natural class of curvature functions which are convex or concave in the second fundamental form. Neither of these results assumes interior noncollapsing.
Sensing Population Distribution from Satellite Imagery Via Deep Learning:Model Selection, Neighboring Effects, and Systematic Biases

The rapid development of remote sensing techniques provides rich, large-coverage, and high-temporal information of the ground, which can be coupled with the emerging deep learning approaches that enable latent features and hidden geographical patterns to be extracted. This article marks the first attempt to cross-compare performances of popular state-of-the-art deep learning models in estimating population distribution from remote sensing images, investigate the contribution of neighboring effect, and explore the potential systematic population estimation biases. We conduct an end-to-end training of four popular deep learning architectures, i.e., VGG, ResNet, Xception, and DenseNet, by establishing a mapping between Sentinel-2 image patches and their corresponding population count from the LandScan population grid. The results reveal that DenseNet outperforms the other three models, while VGG has the worst performances in all evaluating metrics under all selected neighboring scenarios. As for the neighboring effect, contradicting existing studies, our results suggest that the increase of neighboring sizes leads to reduced population estimation performance, which is found universal for all four selected models in all evaluating metrics. In addition, there exists a notable, universal bias that all selected deep learning models tend to overestimate sparsely populated image patches and underestimate densely populated image patches, regardless of neighboring sizes. The methodological, experimental, and contextual knowledge this article provides is expected to benefit a wide range of future studies that estimate population distribution via remote sensing imagery.
Regularizing towards Causal Invariance: Linear Models with Proxies

We propose a method for learning linear models whose predictive performance is robust to causal interventions on unobserved variables, when noisy proxies of those variables are available. Our approach takes the form of a regularization term that trades off between in-distribution performance and robustness to interventions. Under the assumption of a linear structural causal model, we show that a single proxy can be used to create estimators that are prediction optimal under interventions of bounded strength. This strength depends on the magnitude of the measurement noise in the proxy, which is, in general, not identifiable. In the case of two proxy variables, we propose a modified estimator that is prediction optimal under interventions up to a known strength. We further show how to extend these estimators to scenarios where additional information about the "test time" intervention is available during training. We evaluate our theoretical findings in synthetic experiments and using real data of hourly pollution levels across several cities in China.
PoNW: A Secure and Scalable Proof-of-Notarized-Work Based Consensus Mechanism

The original consensus algorithm - Proof of Work (PoW) has been widely utilized in the blockchain systems and is been adopted by many cryptocurrencies, such as Bitcoin and Ethereum, among many others. Nevertheless, the concept has received criticisms over its high energy consumption. This is induced by the necessity for all nodes in the network to communicate synchronously for consensus over the ledger state to be reached. Additionally, the concept has also shown clear limitations regarding performance and throughput. In trying to rectify this issue, the paper proposes the introduction of a new hybrid consensus protocol known as the Proof of Notarized Work (PoNW). The PoNW concept reduces the number of nodes that need to achieve consensus, thereby reducing the overall energy consumption in the current PoW. In addition, we propose using a decentralized random beacon to select nodes to participate in the mining process randomly. Therefore, our algorithm promises to achieve higher scalability and consistency levels without conceding its decentralization. When this is paired with a Byzantine Fault Tolerance (PBFT) verification, the system gains the ability to replace the probabilistic finality in current PoW with absolute finality in a matter of seconds, solving the issue of scalability. Finally, the study will look into the proposed algorithm's security and provides threats model to insure an acceptable failure probability. Results from the security analysis have shown that our consensus algorithm ensures forks cannot occur, and it remains secure and consistent even amid numerous attacks.
Multimodal Biometric Method Based on the Score-level Fusion of Forearm Vein and Forearm Geometry

Biometric authentication based on the forearm is less exploited in previous studies than those based on the finger or the hand. Therefore, we propose a multimodal forearm biometric method that combines veins and geometry of a human forearm, using two veins features: SURF and line-based feature, and one geometry feature: forearm shape feature. The proposed method introduces a score-level fusion approach based on the Cumulative Match Characteristics (CMC) curve. Weighted SUM rule and z-score normalization are applied to obtain the final fusion score, and the optimal weights are calculated by analyzing the CMC(1) point and the CMC=100% point on CMC curves. Two comparison experiments show that the proposed multimodal biometric method outperforms the single-modal methods and other score-level fusion methods.
Virtual Indian Sign Language Interpreter

We present a complete system to automatically translate English content to Indian Sign Language, using animated avatars to perform the output. This is the first work of this scale in ISL, including our own animation and translation system. Our work is further intended to be completely open sourced to foster collaboration from the general public and interested HI members, and also serve as a starting point for future research in the field. Sign language is the predominant means of communication of the significant population of Hearing Impaired (HI) in India, who are very disadvantaged with regards to access to media and education due to lack of resources. Our work seeks to bridge this gap by providing a translation system that can be used by the HI. The pipeline of the system consists of a rule based English-to-ISL gloss Machine Translation framework followed by an animation module using hand-crafted animations playing the output sequentially. The process is aided by a English-ISL bilingual translation corpus and a video corpus of ISL performance at both sentence and individual sign levels.
Dose Optimization in 18 F-FDG PET Based on Noise-Equivalent Count Rate Measurement and Image Quality Assessment

The present study aimed to optimize the injected dose of 18 F-FDG in whole-body PET/CT scans and assess its effect on noise-equivalent count rate (NECR) and visual image quality (IQ). Methods: Patients scheduled to undergo 18 F-FDG PET/ CT were prospectively recruited in the study from January to December 2019, regardless of the indication or underlying dis-ease. Patients were divided into 4 groups and injected with different amounts of 18 F-FDG radioactivity per kilogram of body weight (1.85, 3.7, 5.5, and 7.4 MBq/kg). All patients underwent 18 F-FDG PET/CT studies, and NECR local was calculated by not-ing the trues rate, total prompts, and randoms rate for each bed position. Whole-body NECR global was calculated as the average NECR for all bed positions. IQ was qualitatively assessed for each bed position (IQ local ) and for whole-body PET (IQ global ) by 2 readers using 5-point scores based on prevalence of noise, contrast, and lesion detectability. NECR and IQ were compared among all 4 activity groups. Patients were also subdivided into 4 body-mass-index groups (group I, 15 – 20 kg/m 2 ; group II, 20.1 – 25 kg/m 2 ; group III, 25.1 – 30 kg/m 2 ; and group IV, 30.1 – 35 kg/m 2 ) for comparison. A P value of less than 0.05 was considered signiﬁcant. Results: In total, 109 patients underwent 18 F-FDG PET/CT studies after injection of different amounts of 18 F-FDG radioactivity and a mean uptake time of 62.32 min. The mean NECR global and IQ global for each group were signiﬁcantly different from other groups ( P , 0.05), with NECR and IQ being higher in high-activity groups than in low-activity groups. The overall IQ was acceptable in all patients, even in the lowest-activity group (1.84 MBq/kg). The mean NECR global and IQ global were signiﬁcantly different in all 4 body-mass-index groups ( P , 0.05), except between groups II and III ( P . 0.05). NECR local and IQ local correlated moderately ( r 5 0.64). Conclusion: Optimization of injected 18 F-FDG radioactivity from 7.4 MBq/kg (200 μ Ci/kg) to 1.85 MBq/kg (50 μ Ci/kg) resulted in acceptable IQ, despite a reduction in NECR.
Tumor Cell Microenvironment and Microvessel Density Analysis in MALT Type Lymphoma

Background/Aim: MALT type lymphoma belongs to marginal zone lymphoma (MZL). MALT lymphomas’ inflammatory microenvironment contributes to the pathogenesis of this cancer. In this study, we analyzed and quantified the tumor inflammatory microenvironment in MALT lymphoma samples and in healthy controls, and the microvessel content by immunohistochemistry and morphometric estimation. Patients and Methods: Biopsy specimens from MALT type lymphoma patients and from non-metastatic axillary lymph nodes (CTRL) were analyzed by immunochemistry in order to quantify CD3, CD4, CD8, CD68, CD163, tryptase, CD34, and Ki67 positive cells. Results: A substantial increase in the number of cells that were positive for the above markers and microvascular density (MVD) were observed in the MALT group. We also found a positive correlation between microvessels and CD8+ cells and between CD8+ cells and M2 type macrophages, while tryptase+ mast cells correlated with CD4+ cells. The mitotic proliferation index Ki67 was higher in MALT samples. Conclusion: The interactions between inflammatory cells in the tumor microenvironment and their correlation with angiogenesis is a useful tool in the development of new immunotherapy strategies.
APOE4 disrupts intracellular lipid homeostasis in human iPSC-derived glia

APOE4-disrupted intracellular lipid homeostasis in human and yeast cells was reversed by promoting phospholipid synthesis through choline supplementation. APOE4 disrupts lipid homeostasis in glia APOE4 is a strong genetic risk factor for many diseases, most notably, late-onset Alzheimer’s disease. Sienski et al. now show that cultured human glia with an APOE4 genotype accumulated unsaturated triglycerides leading to a lipid imbalance. Using genetic screens in yeast, the authors discovered that promoting phospholipid synthesis through choline supplementation of culture medium restored a normal lipid state in APOE4-expressing yeast cells. The authors then demonstrated that choline supplementation also restored lipid homeostasis in human APOE4 astrocytes. These findings suggest that modulating glial metabolism could help to reduce APOE4-associated disease risk. The E4 allele of the apolipoprotein E gene (APOE) has been established as a genetic risk factor for many diseases including cardiovascular diseases and Alzheimer’s disease (AD), yet its mechanism of action remains poorly understood. APOE is a lipid transport protein, and the dysregulation of lipids has recently emerged as a key feature of several neurodegenerative diseases including AD. However, it is unclear how APOE4 perturbs the intracellular lipid state. Here, we report that APOE4, but not APOE3, disrupted the cellular lipidomes of human induced pluripotent stem cell (iPSC)–derived astrocytes generated from fibroblasts of APOE4 or APOE3 carriers, and of yeast expressing human APOE isoforms. We combined lipidomics and unbiased genome-wide screens in yeast with functional and genetic characterization to demonstrate that human APOE4 induced altered lipid homeostasis. These changes resulted in increased unsaturation of fatty acids and accumulation of intracellular lipid droplets both in yeast and in APOE4-expressing human iPSC-derived astrocytes. We then identified genetic and chemical modulators of this lipid disruption. We showed that supplementation of the culture medium with choline (a soluble phospholipid precursor) restored the cellular lipidome to its basal state in APOE4-expressing human iPSC-derived astrocytes and in yeast expressing human APOE4. Our study illuminates key molecular disruptions in lipid metabolism that may contribute to the disease risk linked to the APOE4 genotype. Our study suggests that manipulating lipid metabolism could be a therapeutic approach to help alleviate the consequences of carrying the APOE4 allele.
Software smell detection techniques: A systematic literature review

Software smells indicate design or code issues that might degrade the evolution and maintenance of software systems. Detecting and identifying these issues are challenging tasks. This paper explores, identifies, and analyzes the existing software smell detection techniques at design and code levels. We carried out a systematic literature review (SLR) to identify and collect 145 primary studies related to smell detection in software design and code. Based on these studies, we address several questions related to the analysis of the existing smell detection techniques in terms of abstraction level (design or code), targeted smells, used metrics, implementation, and validation. Our analysis identified several detection techniques categories. We observed that 57% of the studies did not use any performance measures, 41% of them omitted details on the targeted programing language, and the detection techniques were not validated in 14% of these studies. With respect to the abstraction level, only 18% of the studies addressed bad smell detection at the design level. This low coverage urges for more focus on bad smell detection at the design level to handle them at early stages. Finally, our SLR brings to the attention of the research community several opportunities for future research.
Science and Religion Shift in the First Three Months of the Covid-19 Pandemic

Abstract The goal of this pilot study is to investigate expressions of the collective disquiet of people in the first months of Covid-19 pandemic, and to try to understand how they manage covert risk, especially with religion and magic. Four co-authors living in early hot spots of the pandemic speculate on the roles of science, religion, and magic, in the latest global catastrophe. They delve into the consolidation that should be occurring worldwide because of a common, viral enemy, but find little evidence for it. They draw parallels to biblical works, finding evidence of a connection between plague and “social strife. ” They explore changes in the purviews of science, religion, and magic, and how and why they have changed, as three systems of covert risk management. They speculate on the coming wave of grief when the world populations finally decide that too many people have died, and they envision cultural changes on the other side of the pandemic, to lifestyles, travel, reverse urbanization, and living and working in smaller communities. Using an unusual approach named “crowd-sourced ethnography”, they conduct un-traditional ethnography and speculate on management of covert risk in their native countries.
Writing a compelling integrated discussion: a guide for integrated discussions in article-based theses and dissertations

Abstract Article-based theses and dissertations are increasingly being used in nursing and the health sciences as an alternate format to the traditional five-chapter monograph. A unique chapter in the article-based thesis is the integrated discussion, which differs in breadth and depth as compared to the discussion for a traditional thesis monograph or journal article. For many students and faculty, the integrated discussion is a challenging chapter to write, with minimal or no published guidance available. In this article, we offer a four-step approach with templates for planning and writing an integrated discussion. We also share several lessons learned with examples from published theses and dissertations. Writing an integrated discussion can be facilitated and written more efficiently by developing a clear and detailed outline of the chapter and broad discussion points prior to drafting the text, to achieve a higher-level synthesis, analysis, and interpretation of the overall significance of the thesis findings.
Inhibition of lncRNA H19/miR-370-3p pathway mitigates neuronal apoptosis in an in vitro model of spinal cord injury (SCI)

Abstract Background Spinal cord injury (SCI) is the most serious complication of spinal injury, often leading to severe dysfunction of the limbs below the injured segment. Conventional therapy approaches are becoming less and less effective, and gene therapy is a new research direction by now. Methods The Sprague-Dawley rats were haphazardly assigned to two groups, namely sham group and SCI model group, and lncRNA H19 and miR-370-3p levels were investigated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Correlation between lncRNA H19 and miR-370-3p was ascertained by luciferase report assay and RT-qPCR. After transfection with si-H19, miR-370-3p inhibitor, negative controls (NC), or both, primary spinal neurons were subjected to the simulation of lipopolysaccharide (LPS) for inducing in vitro model of SCI. Cell viability, apoptotic rate, caspase-3 activity, Bax and Bcl-2 protein, ROS generation, TNF-α, IL-1β, and IL-6 protein, as well as IκBα and p65 phosphorylation ratio were evaluated adopting 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), apoptosis, caspase-3 activity, ROS generation, and western blot assays, thereby searching for the specific action mechanism on LPS-induced spinal never injury. Results SCI resulted in lncRNA H19 higher expression and miR-370-3p lower expression. LPS simulation raised a series of cellular biological changes, such as decreased viability, promoted apoptosis, generated ROS, and released inflammatory factors. lncRNA H19 inhibition reversed above LPS-induced changes. Besides, as the downstream target of lncRNA H19, miR-370-3p was oppositely regulated by lncRNA H19. The above biological changes induced by lncRNA H19 inhibition were reversed by miR-370-3p upregulation. Moreover, lncRNA H19 inhibition could block NF-κB pathway through miR-370-3p upregulation. Conclusion Inhibition of lncRNA H19/miR-370-3p mitigated spinal neuron apoptosis in an in vitro model of SCI. This provided the possibility for clinical use of gene therapy.