Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Fingerprint Pore Detection with a Small FCN

In this work, we investigate if previously proposed CNNs for fingerprint pore detection overestimate the number of required model parameters for this task. We show that this is indeed the case by proposing a fully convolutional neural network that has significantly fewer parameters. We evaluate this model using a rigorous and reproducible protocol, which was, prior to our work, not available to the community. Using our protocol, we show that the proposed model, when combined with post-processing, performs better than previous methods, albeit being much more efficient. All our code is available at this https URL
Granularity and Generalized Inclusion Functions - Their Variants and Contamination

Rough inclusion functions (RIFs) are known by many other names in formal approaches to vagueness, belief, and uncertainty. Their use is often poorly grounded in factual knowledge or involve wild statistical assumptions. The concept of contamination introduced and studied by the present author across a number of her papers, concerns mixing up of information across semantic domains (or domains of discourse). RIFs play a key role in contaminating algorithms and some solutions that seek to replace or avoid them have been proposed and investigated by the present author in some of her earlier papers. The proposals break many algorithms of rough sets in a serious way. In this research, algorithm-friendly granular generalizations of such functions that reduce contamination are proposed and investigated from a mathematically sound perspective. Interesting representation results are proved and a core algebraic strategy for generalizing Skowron-Polkowski style of rough mereology is formulated.
Cybercrime and You: How Criminals Attack and the Human Factors That They Seek to Exploit

Cybercrime is a significant challenge to society, but it can be particularly harmful to the individuals who become victims. This chapter engages in a comprehensive and topical analysis of the cybercrimes that target individuals. It also examines the motivation of criminals that perpetrate such attacks and the key human factors and psychological aspects that help to make cybercriminals successful. Key areas assessed include social engineering (e.g., phishing, romance scams, catfishing), online harassment (e.g., cyberbullying, trolling, revenge porn, hate crimes), identity-related crimes (e.g., identity theft, doxing), hacking (e.g., malware, cryptojacking, account hacking), and denial-of-service crimes. As a part of its contribution, the chapter introduces a summary taxonomy of cybercrimes against individuals and a case for why they will continue to occur if concerted interdisciplinary efforts are not pursued.
Context-Dependent Upper-Confidence Bounds for Directed Exploration

Directed exploration strategies for reinforcement learning are critical for learning an optimal policy in a minimal number of interactions with the environment. Many algorithms use optimism to direct exploration, either through visitation estimates or upper confidence bounds, as opposed to data-inefficient strategies like \epsilon-greedy that use random, undirected exploration. Most data-efficient exploration methods require significant computation, typically relying on a learned model to guide exploration. Least-squares methods have the potential to provide some of the data-efficiency benefits of model-based approaches -- because they summarize past interactions -- with the computation closer to that of model-free approaches. In this work, we provide a novel, computationally efficient, incremental exploration strategy, leveraging this property of least-squares temporal difference learning (LSTD). We derive upper confidence bounds on the action-values learned by LSTD, with context-dependent (or state-dependent) noise variance. Such context-dependent noise focuses exploration on a subset of variable states, and allows for reduced exploration in other states. We empirically demonstrate that our algorithm can converge more quickly than other incremental exploration strategies using confidence estimates on action-values.
Progressive Algorithms for Domination and Independence

We consider a generic algorithmic paradigm that we call progressive exploration, which can be used to develop simple and efficient parameterized graph algorithms. We identify two model-theoretic properties that lead to efficient progressive algorithms, namely variants of the Helly property and stability. We demonstrate our approach by giving linear-time fixed-parameter algorithms for the distance-r dominating set problem (parameterized by the solution size) in a wide variety of restricted graph classes, such as powers of nowhere dense classes, map graphs, and (for $r=1$) biclique-free graphs. Similarly, for the distance-r independent set problem the technique can be used to give a linear-time fixed-parameter algorithm on any nowhere dense class. Despite the simplicity of the method, in several cases our results extend known boundaries of tractability for the considered problems and improve the best known running times.
An Argument for the Foundations of Population Mental Health

The global burden of disease has shifted from the infectious diseases of the nineteenth and early twentieth century toward a growing burden of chronic illness (1). Our understanding of disease causation has commensurately shifted over time. Our original study of infectious diseases necessitated isolating specific causes; this was embedded most clearly perhaps in Koch’s postulates that served as some of the earliest grounding for causal thinking in population health science (2). However, as chronic disease has become much more pervasive, it is clear that a focus on isolating individual causes is insufficient to explain more complex disease etiologies. By way of illustration, it is clearly inadequate to describe the dramatic rise in obesity worldwide as a function solely of our genes; any comprehensive causal approach must take into consideration changing dietary patterns and food availability. That we fall short in turning our attention to a more complex view of disease causation was argued perhaps most elegantly by McMichael in a seminal paper at the turn of the century (3). McMichael suggests that preoccupation with individual genetic or behavioral factors falls short of the larger socio-ecologic context. If we are to extend beyond proximate risk factors, McMichael argues, wemust broaden our causal models and learn to apply a social-ecologic systems perspective. While McMichael’s work was aimed at chronic physical diseases, his admonition has relevance and applicability to our thinking about population mental health today.
CAN: Composite Appearance Network and a Novel Evaluation Metric for Person Tracking

Tracking multiple people across multiple cameras is an open problem. It is typically divided into two tasks: (i) single-camera tracking (SCT) - identify trajectories in the same scene, and (ii) inter-camera tracking (ICT) - identify trajectories across cameras for real surveillance scenes. Many methods cater to SCT, while ICT still remains a challenge. In this paper, we present a feature aggregation architecture called Composite Appearance Network (CAN) to address the above problem. The key structure of this architecture is called EvalNet that pays attention to each feature vector and learns to weight them based on gradients it receives for the overall template for optimal re-identification performance. We demonstrate the efficiency of our approach with experiments on the challenging multi-camera tracking dataset, DukeMTMC. We also survey existing tracking measures and present an online error metric called "Inference Error" (IE) that provides a better estimate of tracking/re-identification error, by treating SCT and ICT errors uniformly.
A Population-Based Descriptive Atlas of Invasive Pneumococcal Strains Recovered Within the U.S. During 2015–2016

Invasive pneumococcal disease (IPD) has greatly decreased since implementation in the U.S. of the 7 valent conjugate vaccine (PCV7) in 2000 and 13 valent conjugate vaccine (PCV13) in 2010. We used whole genome sequencing (WGS) to predict phenotypic traits (serotypes, antimicrobial phenotypes, and pilus determinants) and determine multilocus genotypes from 5334 isolates (~90% of cases) recovered during 2015–2016 through Active Bacterial Core surveillance. We identified 44 serotypes; 26 accounted for 98% of the isolates. PCV13 serotypes (inclusive of serotype 6C) accounted for 1503 (28.2%) isolates, with serotype 3 most common (657/5334, 12.3%), while serotypes 1 and 5 were undetected. Of 305 isolates from children <5 yrs, 60 (19.7%) were of PCV13 serotypes 19A, 19F, 3, 6B, and 23F (58/60 were 19A, 19F, or 3). We quantitated MLST-based lineages first detected during the post-PCV era (since 2002) that potentially arose through serotype-switching. The 7 predominant emergent post-PCV strain complexes included 23B/CC338, 15BC/CC3280, 19A/CC244, 4/CC439, 15A/CC156, 35B/CC156, and 15BC/CC156. These strains accounted for 332 isolates (6.2% of total) and were more frequently observed in children <5 yrs (17.7%; 54/305). Fifty-seven categories of recently emerged (in the post PCV7 period) putative serotype-switch variants were identified, accounting for 402 isolates. Many of these putative switch variants represented newly emerged resistant strains. Penicillin-nonsusceptibility (MICs > 0.12 μg/ml) was found among 22.4% (1193/5334) isolates, with higher penicillin MICs (2–8 μg/ml) found in 8.0% (425/5334) of isolates that were primarily (372/425, 87.5%) serotypes 35B and 19A. Most (792/1193, 66.4%) penicillin-nonsusceptible isolates were macrolide-resistant, 410 (34.4%) of which were erm gene positive and clindamycin-resistant. The proportion of macrolide-resistant isolates increased with increasing penicillin MICs; even isolates with reduced penicillin susceptibility (MIC = 0.06 μg/ml) were much more likely to be macrolide-resistant than basally penicillin-susceptible isolates (MIC < 0.03 μg/ml). The contribution of recombination to strain diversification was assessed through quantitating 35B/CC558-specific bioinformatic pipeline features among non-CC558 CCs and determining the sizes of gene replacements. Although IPD has decreased greatly and stabilized in the post-PCV13 era, the species continually generates recombinants that adapt to selective pressures exerted by vaccines and antimicrobials. These data serve as a baseline for monitoring future changes within each invasive serotype.
Advances in Cellular Characterization of the Sirtuin Isoform, SIRT7

SIRT7 is one of seven mammalian sirtuins that functions as an NAD+-dependent histone/protein deacetylase. SIRT7 is the least well-known member of the sirtuin family, but recent efforts have identified its involvement in various cellular processes, such as ribosome biogenesis, gene expression, cellular metabolism and cancer. Here we provide an update on the functions and mechanisms of SIRT7 in cellular regulation and disease.
Oral Zinc Sulfate for Prevention and Treatment of Chemotherapy-Induced Oral Mucositis: A Meta-Analysis of Five Randomized Controlled Trials

Chemotherapy-induced oral mucositis is an extremely serious complication faced by cancer patients. The role of oral zinc sulfate in preventing and treating chemotherapy-induced oral mucositis remains a subject of debate. The aim of this systematic review is to assess the potential of oral zinc sulfate to alleviate this morbid condition. A systematic search was conducted electronically in PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials (CENTRAL) to capture all potential randomized controlled trials investigating efficacy and safety of oral zinc sulfate in prevention and treatment of chemotherapy-induced oral mucositis, and the retrieval time was limited from their inception to April 2018. We assigned two independent investigators to perform a search, screen citations, extract information, and evaluate the risk of bias in all included trials. Subsequently, the RevMan 5.3 software was utilized to perform all statistical analyses. We included five eligible studies involving 352 patients. Meta-analysis based on limited data revealed that oral zinc sulfate failed to decrease the incidence of chemotherapy-induced oral mucositis (RR [relative risk] = 0.52, 95% CI [confidence interval] = 0.17–1.64) as well as relieve chemotherapy-induced oral mucositits grade (RR = 0.62, 95% CI = 0.11–3.56; RR = 0.70, 95% CI = 0.29–1.71). Moreover, qualitative analyses also suggested that oral zinc sulfate was not associated with reduced oral pain intensity, delayed onset of chemotherapy-induced oral mucositis, decreased adverse events, or improved quality of life compared with control regimes. Oral zinc sulfate may not provide clinical benefits in preventing or reducing incidence, severity, or pain intensity of chemotherapy-induced oral mucositis in cancer patients. However, more studies with large-scale and rigorous methods are warranted for the purpose of further investigating efficacy and safety of oral zinc sulfate for this pathologic condition due to the presence of limitations.
Infinite-Horizon Gaussian Processes

Gaussian processes provide a flexible framework for forecasting, removing noise, and interpreting long temporal datasets. State space modelling (Kalman filtering) enables these non-parametric models to be deployed on long datasets by reducing the complexity to linear in the number of data points. The complexity is still cubic in the state dimension m which is an impediment to practical application. In certain special cases (Gaussian likelihood, regular spacing) the GP posterior will reach a steady posterior state when the data are very long. We leverage this and formulate an inference scheme for GPs with general likelihoods, where inference is based on single-sweep EP (assumed density filtering). The infinite-horizon model tackles the cubic cost in the state dimensionality and reduces the cost in the state dimension m to O(m^2) per data point. The model is extended to online-learning of hyperparameters. We show examples for large finite-length modelling problems, and present how the method runs in real-time on a smartphone on a continuous data stream updated at 100 Hz.
PRAMs over integers do not compute maxflow efficiently

Finding lower bounds in complexity theory has proven to be an extremely difficult task. In this article, we analyze two proofs of complexity lower bound: Ben-Or's proof of minimal height of algebraic computational trees deciding certain problems and Mulmuley's proof that restricted Parallel Random Access Machines (prams) over integers can not decide P-complete problems efficiently. We present the aforementioned models of computation in a framework inspired by dynamical systems and models of linear logic : graphings. This interpretation allows to connect the classical proofs to topological entropy, an invariant of these systems; to devise an algebraic formulation of parallelism of computational models; and finally to strengthen Mulmuley's result by separating the geometrical insights of the proof from the ones related to the computation and blending these with Ben-Or's proof. Looking forward, the interpretation of algebraic complexity theory as dynamical system might shed a new light on research programs such as Geometric Complexity Theory.
PPIs Are Not Responsible for Elevating Cardiovascular Risk in Patients on Clopidogrel—A Systematic Review and Meta-Analysis

Background: Clopidogrel and proton pump inhibitors (PPIs) are metabolized by cytochrome P450 enzymes. Contradictory results have been reported on possible complications of simultaneous PPI and clopidogrel use. Our aim was to investigate the clinical relevance of this debate with a systematic review and meta-analysis. Methods: The PubMed, Embase, and Cochrane Central Register of Controlled Trials electronic databases were searched for human studies [randomized controlled trials (RCTs) and observational studies] using the PICO format (P: patients on clopidogrel; I: patients treated with PPI; C: patients without PPI treatment; O: cardiovascular risk). We screened eligible studies from 2009 to 2016. After study exclusions, we extracted data from 27 articles for three outcomes: major adverse cardiac event (MACE), myocardial infarction (MI) and cardiovascular (CV) death. The meta-analysis was registered on PROSPERO (CRD42017054316). Results: Data were extracted on 156,823 patients from the 27 trials included (MACE: 23, CV death: 10, MI: 14). The risks of MACE (RR = 1.22, 95% CI = 1.06–1.396, p = 0.004) and MI (RR = 1.43, 95% CI = 1.24–1.66, p < 0.001) were significantly higher in the PPI plus clopidogrel group. However, subgroup analysis demonstrated that this significance disappeared in RCTs (RR = 0.99, 95% CI = 0.76–1.28, p = 0.93) in the MACE outcome group. There was no effect of combined PPI and clopidogrel therapy on CV death outcome (RR = 1.21, 95% CI = 0.97–1.50, p = 0.09). Conclusion: Concomitant use of PPIs and clopidogrel has been proved not to be associated with elevated cardiovascular risks according to RCTs. Based on our results, no restrictions should be applied whenever PPIs and clopidogrel are administered simultaneously.
Incentivizing the Dynamic Workforce: Learning Contracts in the Gig-Economy

In principal-agent models, a principal offers a contract to an agent to perform a certain task. The agent exerts a level of effort that maximizes her utility. The principal is oblivious to the agent's chosen level of effort, and conditions her wage only on possible outcomes. In this work, we consider a model in which the principal is unaware of the agent's utility and action space. She sequentially offers contracts to identical agents, and observes the resulting outcomes. We present an algorithm for learning the optimal contract under mild assumptions. We bound the number of samples needed for the principal obtain a contract that is within $\epsilon$ of her optimal net profit for every $\epsilon>0$.
Pathogenic, Phylogenetic, and Serological Analysis of Group I Fowl Adenovirus Serotype 4 SDSX Isolated From Shandong, China

Hydropericardium hepatitis syndrome (HHS) caused by group I fowl adenovirus serotype 4 (FAdV-4) is an acute and infectious disease in fowl, particularly in broilers aged 3–5 weeks. In June 2015, a highly pathogenic disease outbroke in 25–40 day-old ducklings in Shandong province, characterized similar symptom to HHS. In order to determine the pathogenic mechanism of FAdV-4 (SDSX strain) in meat ducks. We divided 90 25-day-old Cherry Valley meat ducks into three groups (oral, subcutaneous, and control; 30 ducks in each group) and infected them with the virus. HHS, inclusion body hepatitis, and enlargement and hemorrhage of the spleen, kidney, lung, thymus, and brain were observed in FAdV-4-infected meat ducks. Histopathological changes were mainly characterized by severe fatty degeneration in the liver, basophilic inclusion bodies in hepatocytes, and vacuolation in the bursa. More importantly, viral DNA could be detected by quantitative real-time polymerase chain reaction in several viscera tissues (e.g., heart, liver, spleen) on the third day after infection. Notably, the livers of the two infected groups contained the highest concentration of viral DNA. In addition, immune responses were studied based on titer levels of the virus antibody and the levels of inflammatory cytokines interleukin (IL)-2 and interferon (IFN)-γ, and most levels were significantly upregulated, indicating that the host immune responses were activated early in infection. These findings increase our understanding of the pathogenicity of FAdV-4 (SDSX) in meat ducks and provide the foundation for further in-depth study of the pathogenic mechanism of this virus.
Increasing Impact by Mechanical Resonance for Teleoperated Hammering

Series elastic actuators (SEAs) are interesting for usage in harsh environments as they are more robust than rigid actuators. This paper shows how SEAs can be used in teleoperation to increase output velocity in dynamic tasks. A first experiment is presented that tested human ability to achieve higher hammerhead velocities with a flexible hammer than with a rigid hammer, and to evaluate the influence of the resonance frequency. In this experiment, 13 participants executed a hammering task in direct manipulation using flexible hammers in four conditions with resonance frequencies of 3.0 Hz to 9.9 Hz and one condition with a rigid hammer. Then, a second experiment is presented that tested the ability of 32 participants to reproduce the findings of the first experiment in teleoperated manipulation with different feedback conditions: with visual and force feedback, without visual feedback, without force feedback, and with a communication delay of 40 ms. The results indicate that humans can exploit the mechanical resonance of a flexible system to at least double the output velocity without combined force and vision feedback. This is an unexpected result, allowing the design of simpler and more robust teleoperators for dynamic tasks.
Entropy-regularized Optimal Transport Generative Models

We investigate the use of entropy-regularized optimal transport (EOT) cost in developing generative models to learn implicit distributions. Two generative models are proposed. One uses EOT cost directly in an one-shot optimization problem and the other uses EOT cost iteratively in an adversarial game. The proposed generative models show improved performance over contemporary models on scores of sample based test.
The Potential of Learned Index Structures for Index Compression

Inverted indexes are vital in providing fast key-word-based search. For every term in the document collection, a list of identifiers of documents in which the term appears is stored, along with auxiliary information such as term frequency, and position offsets. While very effective, inverted indexes have large memory requirements for web-sized collections. Recently, the concept of learned index structures was introduced, where machine learned models replace common index structures such as B-tree-indexes, hash-indexes, and bloom-filters. These learned index structures require less memory, and can be computationally much faster than their traditional counterparts. In this paper, we consider whether such models may be applied to conjunctive Boolean querying. First, we investigate how a learned model can replace document postings of an inverted index, and then evaluate the compromises such an approach might have. Second, we evaluate the potential gains that can be achieved in terms of memory requirements. Our work shows that learned models have great potential in inverted indexing, and this direction seems to be a promising area for future research.