Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional Embedded Networks for Population Scale Clustering and Bio-Ancestry Inferencing

The study of genetic variants (GVs) can help find correlating population groups and to identify cohorts that are predisposed to common diseases and explain differences in disease susceptibility and how patients react to drugs. Machine learning techniques are increasingly being applied to identify interacting GVs to understand their complex phenotypic traits. Since the performance of a learning algorithm not only depends on the size and nature of the data but also on the quality of underlying representation, deep neural networks (DNNs) can learn non-linear mappings that allow transforming GVs data into more clustering and classification friendly representations than manual feature selection. In this paper, we propose convolutional embedded networks (CEN) in which we combine two DNN architectures called convolutional embedded clustering (CEC) and convolutional autoencoder (CAE) classifier for clustering individuals and predicting geographic ethnicity based on GVs, respectively. We employed CAE-based representation learning to 95 million GVs from the ‘1000 genomes’ (covering 2,504 individuals from 26 ethnic origins) and ‘Simons genome diversity’ (covering 279 individuals from 130 ethnic origins) projects. Quantitative and qualitative analyses with a focus on accuracy and scalability show that our approach outperforms state-of-the-art approaches such as VariantSpark and ADMIXTURE. In particular, CEC can cluster targeted population groups in 22 hours with an adjusted rand index (ARI) of 0.915, the normalized mutual information (NMI) of 0.92, and the clustering accuracy (ACC) of 89 percent. Contrarily, the CAE classifier can predict the geographic ethnicity of unknown samples with an F1 and Mathews correlation coefficient (MCC) score of 0.9004 and 0.8245, respectively. Further, to provide interpretations of the predictions, we identify significant biomarkers using gradient boosted trees (GBT) and SHapley Additive exPlanations (SHAP). Overall, our approach is transparent and faster than the baseline methods, and scalable for 5 to 100 percent of the full human genome.
Tetracene ultrathin film growth on silicon

Inorganic-organic interfaces are important for enhancing the power conversion efficiency of silicon-based solar cells through singlet exciton fission (SF). We elucidated the structure of the first monolayers of tetracene (Tc), a SF molecule, on hydrogen-passivated Si(111) [H-Si(111)] and hydrogenated amorphous Si (a-Si:H) by combining near-edge X-ray absorption fine structure (NEXAFS) and X-ray photoelectron spectroscopy (XPS) experiments with density functional theory (DFT) calculations. For samples grown at or below substrate temperatures of 265 K, the resulting ultrathin Tc films are dominated by almost uprightstanding molecules. The molecular arrangement is very similar to the Tc bulk phase, with only slightly higher average angle between the conjugated molecular plane normal and the surface normal () around 77°. Judging from carbon K-edge X-ray absorption spectra, the orientation of the Tc molecules are almost identical when grown on H-Si(111) and a-Si:H substrates as well as for (sub)monoto severalmonolayer coverages. Annealing to room temperature, however, changes the film structure towards a smaller  of about 63°. A detailed DFT-assisted analysis suggests that this structural transition is correlated with a lower packing density and requires a well-chosen amount of thermal energy. Therefore, we attribute the resulting structure to a distinct monolayer configuration that features less inclined, but still well-ordered molecules. The larger overlap with the substrate wavefunctions makes this arrangement attractive for an optimized interfacial electron transfer in SF-assisted silicon solar cells.
Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation

Question Generation (QG) is fundamentally a simple syntactic transformation; however, many aspects of semantics influence what questions are good to form. We implement this observation by developing Syn-QG, a set of transparent syntactic rules leveraging universal dependencies, shallow semantic parsing, lexical resources, and custom rules which transform declarative sentences into question-answer pairs. We utilize PropBank argument descriptions and VerbNet state predicates to incorporate shallow semantic content, which helps generate questions of a descriptive nature and produce inferential and semantically richer questions than existing systems. In order to improve syntactic fluency and eliminate grammatically incorrect questions, we employ back-translation over the output of these syntactic rules. A set of crowd-sourced evaluations shows that our system can generate a larger number of highly grammatical and relevant questions than previous QG systems and that back-translation drastically improves grammaticality at a slight cost of generating irrelevant questions.
Pure gravity traveling quasi‐periodic water waves with constant vorticity

We prove the existence of small amplitude time quasi‐periodic solutions of the pure gravity water waves equations with constant vorticity, for a bidimensional fluid over a flat bottom delimited by a space periodic free interface. Using a Nash‐Moser implicit function iterative scheme we construct traveling nonlinear waves which pass through each other slightly deforming and retaining forever a quasiperiodic structure. These solutions exist for any fixed value of depth and gravity and restricting the vorticity parameter to a Borel set of asymptotically full Lebesgue measure.
sFuzz: An Efficient Adaptive Fuzzer for Solidity Smart Contracts

Smart contracts are Turing-complete programs that execute on the infrastructure of the blockchain, which often manage valuable digital assets. Solidity is one of the most popular programming languages for writing smart contracts on the Ethereum platform. Like traditional programs, smart contracts may contain vulnerabilities. Unlike traditional programs, smart contracts cannot be easily patched once they are deployed. It is thus important that smart contracts are tested thoroughly before deployment. In this work, we present an adaptive fuzzer for smart contracts on the Ethereum platform called sFuzz. Compared to existing Solidity fuzzers, sFuzz combines the strategy in the AFL fuzzer and an efficient lightweight multi-objective adaptive strategy targeting those hard-to-cover branches. sFuzz has been applied to more than 4 thousand smart contracts and the experimental results show that (1) sFuzz is efficient, e.g., two orders of magnitude faster than state-of-the-art tools; (2) sFuzz is effective in achieving high code coverage and discovering vulnerabilities; and (3) the different fuzzing strategies in sFuzz complement each other.
Fast Hardware-Aware Neural Architecture Search

Designing accurate and efficient convolutional neural architectures for vast amount of hardware is challenging because hardware designs are complex and diverse. This paper addresses the hardware diversity challenge in Neural Architecture Search (NAS). Unlike previous approaches that apply search algorithms on a small, human-designed search space without considering hardware diversity, we propose HURRICANEthat explores the automatic hardware-aware search over a much larger search space and a two-stage search algorithm, to efficiently generate tailored models for different types of hardware. Extensive experiments on ImageNet demonstrate that our algorithm outperforms state-of-the-art hardware-aware NAS methods under the same latency constraint on three types of hardware. Moreover, the discovered architectures achieve much lower latency and higher accuracy than current state-of-the-art efficient models. Remarkably, HURRICANE achieves a 76.67% top-1 accuracy on ImageNet with a inference latency of only 16.5 ms for DSP, which is a 3.47% higher accuracy and a 6.35× inference speedup than FBNet-iPhoneX, respectively. For VPU, we achieve a 0.53% higher top-1 accuracy than Proxyless-mobile with a 1.49× speedup. Even for well-studied mobile CPU, we achieve a 1.63% higher top-1 accuracy than FBNet-iPhoneX with a comparable inference latency. HURRICANE also reduces the training time by 30.4% compared to SPOS.
Framed instanton homology and concordance

We define two concordance invariants of knots using framed instanton homology. These invariants ν♯ and τ♯ provide bounds on slice genus and maximum self‐linking number, and the latter is a concordance homomorphism which agrees in all known cases with the τ invariant in Heegaard Floer homology. We use ν♯ and τ♯ to compute the framed instanton homology of all nonzero rational Dehn surgeries on: 20 of the 35 nontrivial prime knots through eight crossings, infinite families of twist and pretzel knots, and instanton L‐space knots; and of 19 of the first 20 closed hyperbolic manifolds in the Hodgson–Weeks census. In another application, we determine when the cable of a knot is an instanton L‐space knot. Finally, we discuss applications to the spectral sequence from odd Khovanov homology to the framed instanton homology of branched double covers, and to the behaviors of τ♯ and τ under genus‐2 mutation.
Is distribution-free inference possible for binary regression?

For a regression problem with a binary label response, we examine the problem of constructing confidence intervals for the label probability conditional on the features. In a setting where we do not have any information about the underlying distribution, we would ideally like to provide confidence intervals that are distribution-free---that is, valid with no assumptions on the distribution of the data. Our results establish an explicit lower bound on the length of any distribution-free confidence interval, and construct a procedure that can approximately achieve this length. In particular, this lower bound is independent of the sample size and holds for all distributions with no point masses, meaning that it is not possible for any distribution-free procedure to be adaptive with respect to any type of special structure in the distribution.
A Bourgain-Brezis-Mironescu-D\'avila theorem in groups of Heisenberg type

In this note we prove the following theorem in any group of Heisenberg type $\mathbb{G}$: \[ \underset{s\nearrow 1/2}{\lim} (1 - 2s) \mathfrak P_{H,s}(E) = \frac{4}{\sqrt \pi}\ \mathfrak P_H(E). \] Here, $\mathfrak P_H(E)$ represents the horizontal perimeter of a measurable set $E\subset \mathbb{G}$, whereas the nonlocal horizontal perimeter $\mathfrak P_{H,s}(E)$ is a heat based Besov seminorm. This result represents a dimensionless sub-Riemannian counterpart of a famous characterisation of Bourgain-Brezis-Mironescu and Davila.
Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary

Designing dynamic graph algorithms against an adaptive adversary is a major goal in the field of dynamic graph algorithms. While a few such algorithms are known for spanning trees, matchings, and single-source shortest paths, very little was known for an important primitive like graph sparsifiers. The challenge is how to approximately preserve so much information about the graph (e.g., all-pairs distances and all cuts) without revealing the algorithms' underlying randomness to the adaptive adversary. In this paper we present the first non-trivial efficient adaptive algorithms for maintaining spanners and cut sparisifers. These algorithms in turn imply improvements over existing algorithms for other problems. Our first algorithm maintains a polylog$(n)$-spanner of size $\tilde O(n)$ in polylog$(n)$ amortized update time. The second algorithm maintains an $O(k)$-approximate cut sparsifier of size $\tilde O(n)$ in $\tilde O(n^{1/k})$ amortized update time, for any $k\ge1$, which is polylog$(n)$ time when $k=\log(n)$. The third algorithm maintains a polylog$(n)$-approximate spectral sparsifier in polylog$(n)$ amortized update time. The amortized update time of both algorithms can be made worst-case by paying some sub-polynomial factors. Prior to our result, there were near-optimal algorithms against oblivious adversaries (e.g. Baswana et al. [TALG'12] and Abraham et al. [FOCS'16]), but the only non-trivial adaptive dynamic algorithm requires $O(n)$ amortized update time to maintain $3$- and $5$-spanner of size $O(n^{1+1/2})$ and $O(n^{1+1/3})$, respectively [Ausiello et al. ESA'05]. Our results are based on two novel techniques. The first technique, is a generic black-box reduction that allows us to assume that the graph undergoes only edge deletions and, more importantly, remains an expander with almost-uniform degree. The second technique we call proactive resampling. [. ]
First-principles machine learning modelling of COVID-19

The coronavirus disease 2019 (COVID-19) has changed the world since the World Health Organization declared its outbreak on 30th January 2020, recognizing the outbreak as a pandemic on 11th March 2020. As often said by politicians and scientific advisors, the objective is "to flatten the curve", or "push the peak down", or similar wording, of the virus spreading. Central to the official advice are mathematical models and data, which provide estimates on the evolution of the number of infected, recovered and deaths. The accuracy of the models is improved day by day by inferring the contact, recovery, and death rates from data (confirmed cases). A data-driven model trained with {\it both} data {\it and} first principles is proposed. The model can quickly be re-trained any time that new data becomes available. The method can be applied to more detailed epidemic models with virtually no conceptual modification.
A fast calibration approach for onboard LiDAR-camera systems

Outdoor surveillance and security robots have a wide range of industrial, military, and civilian applications. In order to achieve autonomous navigation, the LiDAR-camera system is widely applied by outdoor surveillance and security robots. The calibration of the LiDAR-camera system is essential and important for robots to correctly acquire the scene information. This article proposes a fast calibration approach that is different from traditional calibration algorithms. The proposed approach combines two independent calibration processes, which are the calibration of LiDAR and the camera to robot platform, so as to address the relationship between LiDAR sensor and camera sensor. A novel approach to calibrate LiDAR to robot platform is applied to improve accuracy and robustness. A series of indoor experiments are carried out and the results show that the proposed approach is effective and efficient. At last, it is applied to our own outdoor security robot platform to detect both positive and negative obstacles in a field environment, in which two Velodyne-HDL-32 LiDARs and a color camera are employed. The real application illustrates the robustness performance of the proposed approach.
Algorithmic Identification of the Best WLAN Protocol and Network Architecture for Internet-Based Applications

This research developed a novel algorithm to evaluate internet-based services such as VoIP, Video Conferencing, HTTP and FTP, of different IEEE 802.11 technologies in order to identify the optimum network architecture among Basic Service Set (BSS), Extended Service Set (ESS) and the Independent Basic Service Set (IBSS). The proposed algorithm will yield the rank order of different IEEE 802.11 technologies. By selecting the optimum network architecture and technology, the best overall network performance that provides good voice, video and data quality is guaranteed. Furthermore, it meets the acceptance threshold values for the VoIP, Video Conferencing, HTTP and FTP quality metrics. This algorithm was applied to various room sizes ranging from [Formula: see text][Formula: see text]m to [Formula: see text][Formula: see text]m and the number of nodes ranged from 1 to 65. The spatial distributions considered were circular, uniform and random. The Quality of Service (QoS) metrics used were delay, jitter, throughput and packet loss.
Asymmetrical third-person effects on the perceptions of online risk and harm among adolescents and adults

ABSTRACT Although research has identified a range of opportunities, risks, and harms related to online social networking, the public debate on online risks follows a set pattern by which members of older age groups (parents, regulators) hold a picture of members of younger age groups (teenagers, digital natives) at a uniformly high level of risk. Perceptions of online risk, however, are prone to third-person effects in which individuals perceive risks to be more apparent in others than themselves. This study investigated third-person effects across age groups to further our understanding of the set positions found in current public debate. Multivariate analysis was used to compare adolescent and adult users’ personal and third-person perceptions of common psycho-social risks associated with social networking engagement in a sample of 506 UK-based Facebook users (53% male; 13–77 years). Results indicated that rates of exposure to online vulnerabilities were similar for both age groups. However, differences in adult and adolescent perceptions of risk highlighted apparent mismatches between reported exposure to risk and an individual’s perceptions, with adults demonstrating lower personal perceptions and higher third-person perceptions of risk than their adolescent counterparts. The research considers the implications of risk perception on an individual’s online vulnerability.
Stroke Units in Their Natural Habitat

Background and Purpose— Within clinical trials, stroke patients allocated to receive organized inpatient (stroke unit) care are more likely to survive, return home, and regain independence than those allocated to conventional care. However, there are concerns that the benefits seen in clinical trials may not be replicated in routine practice. We carried out a systematic review of observational studies of stroke unit implementation. Methods— We searched (up to January 2006) MEDLINE, EMBASE, CINAHL, Cochrane Library, British Nursing Index, Cochrane Stroke Group register, and recent conference abstracts for observational studies that compared the outcomes of stroke patients managed in a stroke unit versus non–stroke unit care. We excluded studies that did not describe either matching for baseline prognostic factors or adjustment for case-mix characteristics. The primary outcome was death within 1 year. We also recorded poor outcome (death, institutional care, or dependency). Data analysis used the generic in.
The Structure of the Internet Flows in Cyberspace

This paper describes the structure of international Internet traffic based on data obtained from the Organization for Economic Co-operation and Development for July, 1998. It also describes the relationship between the structure of the Internet and international telecommunications, as well as telephone rates, language, physical location, trade blocs, international migration, student flows, and science and structural asynchrony. The results indicate that the structure of the Internet is significantly related to the structure of the international telecommunications, trade, science and student flow networks, as well language and asynchrony. In combination, only telecommunications, trade, science and asynchrony were significant, accounting for 49. 7% of the variance in the structure of the Internet.
Histopathological Analysis of Neoplastic Lesions of the Ovary: A 5-Year Retrospective Study at Tertiary Health Care Centre

Introduction: Ovarian tumors represent about 30% of all cancers of the female genital system and 4% of all cancers in women. Indian trend analysis reveal a steady increase in the age-standardized incidence rate of ovarian cancer over past years, comprising up to 8.7% of cancers in different parts of the country. Aim and objective: To study the morphological pattern of benign and malignant ovarian neoplasm in various age presenting at our center over a period of 5-years. Material and methods: The present study was conducted retrospectively in the department of Pathology, BPS, GMC, Khanpur Kalan, Sonipat. Data was collected after reviewing cases from 2012 to 2016 (5years). All cases of ovarian neoplasm both primary (benign and malignant) and metastatic were included in the study. Results: Of the total 152 ovarian specimens that presented during the study period, 119 were benign, 5 were borderline tumors and 28 malignant. Most of the benign tumors occurred between 20-40 years of age, while malignant tumors presented commonly after 50 years of age. Out of these only 24 cases had bilateral ovarian neoplasm. Surface epithelial tumors were the most common type followed by germ cell tumors. Conclusion: Benign ovarian tumors were more common than malignant ones across all age groups. On morphological grounds, tumors originating from surface epithelium were the most common variant.
Comparing the Effect of Acupressure and Cryotherapy on the Pain Caused by Removal of Chest Drain Tube in the Elderly Patients Undergoing Open Heart Surgery

Background: Aging has a growing trend in the world. The elderly experience a high incidence of hospitalization and coronary bypass surgery compared with other age groups, and pain is the most common complaint in these patients. This research was conducted to compare the effect of acupressure and cryotherapy on the pain of removing drain tube from the chest of the elderly who have undergone open heart surgery.
User experience improvement of japanese language mobile learning application through mental model and A/B testing

Advances in smartphone technology have led to the strong emergence of mobile learning (m-learning) on the market to support foreign language learning purposes, especially for the Japanese language. No matter what kind of m-learning application, their goal should help learners to learn the Japanese language independently. However, popular Japanese m-learning applications only accommodate on enhancing reading, vocabulary and writing ability so that user experience issues are still prevalent and may affect the learning outcome. In the context of user experience, usability is one of the essential factors in mobile application development to determine the level of the application’s user experience. In this paper, we advocate for a user experience improvement by using the mental model and A/B testing. The mental model is used to reflect the user’s inner thinking mode. A comparative approach was used to investigate the performance of 20 high-grade students with homogenous backgrounds and coursework. User experience level was measured based on the usability approach on pragmatic quality and hedonic quality like effectiveness (success rate of task completion), efficiency (task completion time) and satisfaction. The results then compared with an existing Japanese m-learning to gather the insight of improvement of our proposed method. Experimental results show that both m-learning versions proved can enhance learner performance in pragmatic attributes. Nevertheless, the study also reveals that an m-learning that employs the conversational mental model in the learning process is more valued by participants in hedonic qualities. Mean that the proposed m-learning which is developed with the mental model consideration and designed using A/B testing is able to provide conversational learning experience intuitively.