Miracle-dz/UniFAR-FTUs huggingface dataset:
Geometric Digital Twinning of Industrial Facilities: Retrieval of Industrial Shapes

This paper devises, implements and benchmarks a novel shape retrieval method that can accurately match individual labelled point clusters (instances) of existing industrial facilities with their respective CAD models. It employs a combination of image and point cloud deep learning networks to classify and match instances to their geometrically similar CAD model. It extends our previous research on geometric digital twin generation from point cloud data, which currently is a tedious, manual process. Experiments with our joint network reveal that it can reliably retrieve CAD models at 85.2% accuracy. The proposed research is a fundamental framework to enable the geometric Digital Twin (gDT) pipeline and incorporate the real geometric configuration into the Digital Twin.
Smoothed Online Learning is as Easy as Statistical Learning

Much of modern learning theory has been split between two regimes: the classical ofﬂine setting, where data arrive independently, and the online setting, where data arrive adversarially. While the former model is often both computationally and statistically tractable, the latter requires no distributional assumptions. In an attempt to achieve the best of both worlds, previous work proposed the smooth online setting where each sample is drawn from an adversarially chosen distribution, which is smooth , i.e., it has a bounded density with respect to a ﬁxed dom-inating measure. Existing results for the smooth setting were known only for binary-valued function classes and were computationally expensive in general; in this paper, we ﬁll these lacunae. In particular, we provide tight bounds on the minimax regret of learning a nonparametric function class, with nearly optimal dependence on both the horizon and smoothness parameters. Furthermore, we provide the ﬁrst oracle-efﬁcient, no-regret algorithms in this setting. In particular, we propose an oracle-efﬁcient improper algorithm whose regret achieves optimal dependence on the horizon and a proper algorithm requiring only a single oracle call per round whose regret has the optimal horizon dependence in the classiﬁcation setting and is sublinear in general. Both algorithms have exponentially worse dependence on the smoothness parameter of the adversary than the minimax rate. We then prove a lower bound on the oracle complexity of any proper learning algorithm, which matches the oracle-efﬁcient upper bounds up to a polynomial factor, thus demonstrating the existence of a statistical-computational gap in smooth online learning. Finally, we apply our results to the contextual bandit setting to show that if a function class is learnable in the classical setting, then there is an oracle-efﬁcient, no-regret algorithm for contextual bandits in the case that contexts arrive in a smooth manner. in the theorem statements). We take L = 1 and suppress logarithmic factors where possible.
Albanian Apparel Industry and Its Characteristics of Development

For many years the phenomenon of offshore outsourcing has softened the economic problems of developing countries. The main industries that have developed thanks to outsourcing are labor-intensive industries and services. We recognize achievement indicators for developing countries like China, India, Philippines, etc. The reasons vary as the country's size, costs, level of education, the policies of the host country and many others. The set of these factors not only affects to the promotion of foreign investments, but also to the growth rate of an industry compared with others or international trade level for a particular category of goods / services. Albania has positive figures of development of offshore outsourcing particularly on apparel industry. The industry has been able to survive over two decades even though the economic and political changes. The modest data show little information about the characteristics of this industry, in terms of sector size, number of firms and their size, type of products, etc. The growth of the industry is focused only on positive net exports. The purpose of this paper is presenting a different picture of the industry, focusing on factors directly related to the development of apparel firms. Some of them are the functions (processes) they do, products, their experience, location, etc. This paper will help being familiar with the main characteristics of this industry as well as providing some important conclusions for its development in the future. DOI: 10.5901/ajis.2015. v4n3s1p585
METAL: The Metal Evolution, Transport, and Abundance in the Large Magellanic Cloud Hubble Program. III. Interstellar Depletions, Dust-to-Metal, and Dust-to-Gas Ratios versus Metallicity

The metallicity and gas density dependence of interstellar depletions, the dust-to-gas (D/G), and dust-to-metal (D/M) ratios have important implications for how accurately we can trace the chemical enrichment of the universe, either by using FIR dust emission as a tracer of the ISM or by using spectroscopy of damped Lyα systems to measure chemical abundances over a wide range of redshifts. We collect and compare large samples of depletion measurements in the Milky Way (MW), Large Magellanic Cloud (LMC) (Z = 0.5 Z ⊙), and Small Magellanic Cloud (SMC) (Z = 0.2 Z ⊙). The relations between the depletions of different elements do not strongly vary between the three galaxies, implying that abundance ratios should trace depletions accurately down to 20% solar metallicity. From the depletions, we derive D/G and D/M. The D/G increases with density, consistent with the more efficient accretion of gas-phase metals onto dust grains in the denser ISM. For log N(H) > 21 cm−2, the depletion of metallicity tracers (S, Zn) exceeds −0.5 dex, even at 20% solar metallicity. The gas fraction of metals increases from the MW to the LMC (factor 3) and SMC (factor 6), compensating for the reduction in total heavy element abundances and resulting in those three galaxies having the same neutral gas-phase metallicities. The D/G derived from depletions are respective factors of 2 (LMC) and 5 (SMC) higher than the D/G derived from FIR, 21 cm, and CO emission, likely due to the combined uncertainties on the dust FIR opacity and on the depletion of carbon and oxygen.
Deep learning for drug repurposing: Methods, databases, and applications

Drug development is time‐consuming and expensive. Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs, specifically for Coronavirus Disease 2019 (COVID‐19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). However, comprehensively obtaining and productively integrating available knowledge and big biomedical data to effectively advance deep learning models is still challenging for drug repurposing in other complex diseases. In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing. We first summarized the commonly used bioinformatics and pharmacogenomics databases for drug repurposing. Next, we discuss recently developed sequence‐based and graph‐based representation approaches as well as state‐of‐the‐art deep learning‐based methods. Finally, we present applications of drug repurposing to fight the COVID‐19 pandemic and outline its future challenges.
Characterizing, Detecting, and Predicting Online Ban Evasion

Moderators and automated methods enforce bans on malicious users who engage in disruptive behavior. However, malicious users can easily create a new account to evade such bans. Previous research has focused on other forms of online deception, like the simultaneous operation of multiple accounts by the same entities (sockpuppetry), impersonation of other individuals, and studying the effects of de-platforming individuals and communities. Here we conduct the first data-driven study of ban evasion, i.e., the act of circumventing bans on an online platform, leading to temporally disjoint operation of accounts by the same user. We curate a novel dataset of 8,551 ban evasion pairs (parent, child) identified on Wikipedia and contrast their behavior with benign users and non-evading malicious users. We find that evasion child accounts demonstrate similarities with respect to their banned parent accounts on several behavioral axes — from similarity in usernames and edited pages to similarity in content added to the platform and its psycholinguistic attributes. We reveal key behavioral attributes of accounts that are likely to evade bans. Based on the insights from the analyses, we train logistic regression classifiers to detect and predict ban evasion at three different points in the ban evasion lifecycle. Results demonstrate the effectiveness of our methods in predicting future evaders (AUC = 0.78), early detection of ban evasion (AUC = 0.85), and matching child accounts with parent accounts (MRR = 0.97). Our work can aid moderators by reducing their workload and identifying evasion pairs faster and more efficiently than current manual and heuristic-based approaches.
Discovering Concepts in Learned Representations using Statistical Inference and Interactive Visualization

Concept discovery is one of the open problems in the interpretability literature that is important for bridging the gap between non-deep learning experts and model end-users. Among current formulations, concepts defines them by as a direction in a learned representation space. This definition makes it possible to evaluate whether a particular concept significantly influences classification decisions for classes of interest. However, finding relevant concepts is tedious, as representation spaces are high-dimensional and hard to navigate. Current approaches include hand-crafting concept datasets and then converting them to latent space directions; alternatively, the process can be automated by clustering the latent space. In this study, we offer another two approaches to guide user discovery of meaningful concepts, one based on multiple hypothesis testing, and another on interactive visualization. We explore the potential value and limitations of these approaches through simulation experiments and an demo visual interface to real data. Overall, we find that these techniques offer a promising strategy for discovering relevant concepts in settings where users do not have predefined descriptions of them, but without completely automating the process.
Families of Prym varieties of abelian coverings and Shimura varieties

Under the condition that the Prym map is injective in characteristic p, we prove that the special subvarieties in the moduli space of abelian varieties of dimension l and polarization type D, Al,D , arising from families of abelian covers of P 1 are of a very restrictive nature. In other words, if the family is one-dimensional or if it contains an eigenspace of certain type for the group action on the cohomology of fibers, then the Shimura varieties arising from such families can only be constructed by the group action of the family.
Providing Real-time Assistance for Repairing Runtime Exceptions using Stack Overflow Posts

Runtime Exceptions (REs) are an important class of bugs that occur frequently during code development. Traditional Automatic Program Repair (APR) tools are of limited use in this “in-development” use case, since they require a test-suite to be available as a patching oracle. Thus, developers typically tend to manually resolve their in-development REs, often by referring to technical forums, such as Stack Overflow (SO). To automate this manual process we extend our previous work, MaesTro, to provide real-time assistance to developers for repairing Java REs by recommending a relevant patch-suggesting SO post and synthesizing a repair patch from this post to fix the RE in the developer's code. Maestro exploits a library of Runtime Exception Patterns (REPs) semi-automatically mined from SO posts, through a relatively inexpensive, one-time, incremental process. An REP is an abstracted sequence of statements that triggers a given RE. REPs are used to index SO posts, retrieve a post most relevant to the RE instance exhibited by a developer's code and then mediate the process of extracting a concrete repair from the SO post, abstracting out post-specific details, and concretizing the repair to the developer's buggy code. We evaluate MaesTro on a published RE benchmark comprised of 78 instances. Maestro is able to generate a correct repair patch at the top position in 27% of the cases, within the top-3 in 40% of the cases and overall return a useful artifact in 81% of the cases. Further, the use of REPs proves instrumental to all aspects of Maestro's performance, from ranking and searching of SO posts to synthesizing patches from a given post. In particular, 45% of correct patches generated by MaesTro could not be produced by a baseline technique not using REPs, even when provided with Maestro's SO-post ranking. Maestro is also fast, needing around 1 second, on average, to generate its output. Overall, these results indicate that Maestro can provide effective real-time assistance to developers in repairing REs.
Spin accumulation without spin current

The spin Hall (SH) effect is a phenomenon in which the spin current flows perpendicular to an applied electric field and causes the spin accumulation at the boundaries. However, in the presence of spin-orbit couplings, the spin current is not well defined. Here, we calculate the spin response to an electric-field gradient, which naturally appears at the boundaries. We derive a generic formula using the Bloch wave functions and the phenomenological relaxation time. We also calculate the response for the uniform Rashba model with $\delta$-function nonmagnetic disorder within the first-order Born approximation and corresponding vertex corrections. We find the nonzero spin accumulation, although the SH conductivity exactly vanishes.
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression

We study a fundamental transfer learning process from source to target linear regression tasks, including overparameterized settings where there are more learned parameters than data samples. The target task learning is addressed by using its training data together with the parameters previously computed for the source task. We define a transfer learning approach to the target task as a linear regression optimization with a regularization on the distance between the to-be-learned target parameters and the already-learned source parameters. We analytically characterize the generalization performance of our transfer learning approach and demonstrate its ability to resolve the peak in generalization errors in double descent phenomena of the minimum L2-norm solution to linear regression. Moreover, we show that for sufficiently related tasks, the optimally tuned transfer learning approach can outperform the optimally tuned ridge regression method, even when the true parameter vector conforms to an isotropic Gaussian prior distribution. Namely, we demonstrate that transfer learning can beat the minimum mean square error (MMSE) solution of the independent target task. Our results emphasize the ability of transfer learning to extend the solution space to the target task and, by that, to have an improved MMSE solution. We formulate the linear MMSE solution to our transfer learning setting and point out its key differences from the common design philosophy to transfer learning.
Cheating Robot Games: A model for insider information

Combinatorial games are two-player games of pure strategy where the players, usually called Left and Right, move alternately. In this paper, we introduce Cheating Robot games. These arise from simultaneous-play combinatorial games where one player has insider information (‘cheats’). Play occurs in rounds. At the beginning of a round, both players know the moves that are available to them. Left chooses a move. Knowing Left’s move, Right then chooses a move. Right’s move is not constrained by Left’s choice. The round is not completed until both players have made a choice. A game is finished only when one or both players do not have a move at the beginning of a round. Right choosing a move, knowing Left’s, makes the games deterministic, distinguishing them from simultaneous games. Also, the ending condition distinguishes this class of games from combinatorial games, since the outcomes are now Left-win, Right-win and draw. The basic theory and properties are developed, including showing that there is an equivalence relation and partial order on the games. Whilst there are no inverses in the class of all games, we show that there is a sub-class, simple hot games, in which the integers have inverses. In this sub-class, the optimal strategies are obtained by the solutions to a minimum-weight matching problem on a graph whose number of vertices equals the number of summands in the disjunctive sum.
Associations Between Total Sleep Duration and Cognitive Function Among Middle-Aged and Older Chinese Adults: Does Midday Napping Have an Effect on It?

Aim The effect of sleep duration on cognitive function has been reported. However, the studies about the combined effects of total sleep duration and midday napping on cognition in elders were limited and inconclusive. We aimed to investigate the associations between total sleep duration, midday napping and cognitive function among middle-aged and older Chinese adults. Methods Based on the 3rd wave of the China Health and Retirement Longitudinal Study (CHARLS) in 2015, a total of 9218 participants aged ≥45 years with completed cognition measurements were included. Cognitive functions were assessed by a combined global cognition score of episodic memory and mental status. Information about sleep-related variables, demographic characteristics, and health status were collected by validated questionnaires. Multivariate linear regression models were performed to evaluate the associations between total sleep duration, midday napping, and cognitive function. Stratified analyses were used to explore the potential effect modifier. Results Overall, the global cognition score was 10.38 ± 4.30 among the participants (mean age: 61.5 ± 8.7 years). For sleep duration, both short sleep and long sleep duration were significantly associated with the increased risk of cognitive impairment after controlling for demographics and other confounders. Compared with sleeping for 7h per day, the adjusted β-coefficient and 95% CI of the risk of cognitive impairment was −0.967 (95% CIs: −1.191, −0.742) for ≤5h, −0.257 (−0.498, −0.016) for 6h, −0.424 (−0.650, −0.198) for 8h and −0.664 (−0.876, −0.452) for ≥9h. The combined effect analysis indicated that subjects with extended or without naps had a significantly higher risk of cognitive impairment in the ≤5h sleep time group (ref: 7h) and in extended nappers or without naps group (ref: short nappers). Subjects with extended naps or without naps might increase the risk of cognitive impairment, especially those having ≤5h total sleep time. Stratified analysis showed that participants aged ≥60 years without social activity increased the risk of cognitive decline. Conclusion An inverted U-shaped association was observed between total sleep duration and cognitive function in Chinese elders, especially in those aged ≥60 years or without social activity. Short midday naps could mitigate the deleterious effects of poor sleep quality and shorter sleep duration on cognitive function. The findings could help us identify the vulnerable population and decrease the burden of cognitive impairment.
Examining new measure of asnaf muslimpreneur success model: a Maqasid perspective

Purpose This study aims to investigate mustahiq business success using a newly developed index called asnaf muslimpreneur success index (AMSi). Design/methodology/approach Following Abu Zaharah theory of al-Maqasid al-Shariah, the authors developed the AMSi through literature analyses and expert judges involving three Shariah scholars for improved extensions and applications. Subsequently, the authors conducted the actual survey, which included 17 mustahiq who had been assisted by the zakat institution in running their small and halal businesses in Sabah, Malaysia. Findings Using the AMSi, the success of asnaf businesses was in the moderate index, implying the success meets the index score of more than 50%. All indexes capturing asnafs’ business experience, asnafs’ business justice, asnafs’ well-being and asnafs’ financial wisdom were greater than the threshold value of 0.5 or 50%, implying that the interaction of the battery items involved in those variables resulted in satisfactory results, indicating that they are relevant and essential in gauging the asnafs’ success level in the development business programme. Research limitations/implications The results obtained were at the exploratory level, and for that, more studies related are expected to be conducted in the future, where the coverage of the respondents used is larger to extend the findings, at least. Practical implications The results obtained can become a yardstick to gauge the success of asnaf muslimpreneurs in Sabah, East Malaysia. Originality/value This study introduces new measures of asnaf muslimpreneur success model, where mustahiq business is brought into play.
Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework

Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents with fixed routes and schedules, which leads to latent travel regularity. However, human mobility patterns, specifically the complex relationships between bus passengers, are deeply hidden in this fixed mobility mode. Although many models exist to predict traffic flow, human mobility patterns have not been well explored in this regard. To address this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN). Firstly, we construct a novel sharing-stop network to model relationships between passengers based on bus record data. Then, we employ GCN to extract features from the graph by learning useful topology information and introduce a deep clustering method to recognize mobility patterns hidden in bus passengers. Furthermore, to fully utilize spatio-temporal information, we propose GCN2Flow to predict passenger flow based on various mobility patterns. To the best of our knowledge, this paper is the first work to adopt a multi-pattern approach to predict the bus passenger flow by taking advantage of graph learning. We design a case study for optimizing routes. Extensive experiments upon a real-world bus dataset demonstrate that MPGCN has potential efficacy in passenger flow prediction and route optimization.