Miracle-dz/UniFAR-FTUs huggingface dataset:
The epidemiological characteristics of dengue in high-risk areas of China, 2013–2016

Introduction Dengue has become a more serious human health concern in China, with increased incidence and expanded outbreak regions. The knowledge of the cross-sectional and longitudinal epidemiological characteristics and the evolutionary dynamics of dengue in high-risk areas of China is limited. Methods Records of dengue cases from 2013 to 2016 were obtained from the China Notifiable Disease Surveillance System. Full envelope gene sequences of dengue viruses detected from the high-risk areas of China were collected. Maximum Likelihood tree and haplotype network analyses were conducted to explore the phylogenetic relationship of viruses from high-risk areas of China. Results A total of 56,520 cases was reported in China from 2013 to 2016. During this time, Yunnan, Guangdong and Fujian provinces were the high-risk areas. Imported cases occurred almost year-round, and were mainly introduced from Southeast Asia. The first indigenous case usually occurred in June to August, and the last one occurred before December in Yunnan and Fujian provinces but in December in Guangdong Province. Seven genotypes of DENV 1–3 were detected in the high-risk areas, with DENV 1-I the main genotype and DENV 2-Cosmopolitan the secondary one. The Maximum Likelihood trees show that almost all the indigenous viruses separated into different clusters. DENV 1-I viruses were found to be clustered in Guangdong Province, but not in Fujian and Yunnan, from 2013 to 2015. The ancestors of the Guangdong viruses in the cluster in 2013 and 2014 were most closely related to strains from Thailand or Singapore, and the Guangdong virus in 2015 was most closely related to the Guangdong virus of 2014. Based on closest phylogenetic relationships, viruses from Myanmar possibly initiated further indigenous cases in Yunnan, those from Indonesia in Fujian, while viruses from Thailand, Malaysia, Singapore and Indonesia were predominant in Guangdong Province. Conclusions Dengue is still an imported disease in China, although some genotypes continued to circulate in successive years. Viral phylogenies based on the envelope gene suggested periodic introductions of dengue strains into China, primarily from Southeast Asia, with occasional sustained, multi-year transmission in some regions of China.
What are Attackers after on IoT Devices? An approach based on a multi-phased multi-faceted IoT honeypot ecosystem and data clustering

The growing number of Internet of Things (IoT) devices makes it imperative to be aware of the real-world threats they face in terms of cybersecurity. While honeypots have been historically used as decoy devices to help researchers/organizations gain a better understanding of the dynamic of threats on a network and their impact, IoT devices pose a unique challenge for this purpose due to the variety of devices and their physical connections. In this work, by observing real-world attackers' behavior in a low-interaction honeypot ecosystem, we (1) presented a new approach to creating a multi-phased, multi-faceted honeypot ecosystem, which gradually increases the sophistication of honeypots' interactions with adversaries, (2) designed and developed a low-interaction honeypot for cameras that allowed researchers to gain a deeper understanding of what attackers are targeting, and (3) devised an innovative data analytics method to identify the goals of adversaries. Our honeypots have been active for over three years. We were able to collect increasingly sophisticated attack data in each phase. Furthermore, our data analytics points to the fact that the vast majority of attack activities captured in the honeypots share significant similarity, and can be clustered and grouped to better understand the goals, patterns, and trends of IoT attacks in the wild.
Analysis and Design of Offset QPSK Using Redundant Filter Banks

This paper considers the analysis and design of OQPSK digital modulation. We first establish the discrete time formulation, which allows us to find the equivalent redundant filter banks. It is well known that redundant filter banks are related with redundant transformation of the Frame theory. According to the Frame theory, the redundant transformations and corresponding representations are not unique. In this way, we show that the solution to the pulse shaping problem is not unique. Then we use this property to minimize the effect of the channel noise in the reconstructed symbol stream. We evaluate the performance of the digital communication using numerical examples.
Towards a discovery of BSM physics from the Cabibbo angle anomaly

New developments in both the theories and experiments related to the extraction of the top-row Cabibbo–Kobayashi–Maskawa matrix elements [Formula: see text] and [Formula: see text] led to a series of new anomalies, for instance, the apparent violation of the top-row unitarity relation. It is important to further reduce all the associated Standard Model theory uncertainties in order to better understand whether such observations point towards the possibility of physics beyond the Standard Model, or rather some unexpectedly large Standard Model effects. This requires improved studies of tree-level and higher-order Standard Model corrections that enter the beta decays of pions, neutron, nuclei and kaons. We will briefly review the recent progress along this direction and discuss possible improvements in the future.
Automated Drug-Related Information Extraction from French Clinical Documents: ReLyfe Approach

Structuring medical data in France remains a challenge mainly because of the lack of medical data due to privacy concerns and the lack of methods and approaches on processing the French language. One of these challenges is structuring drug-related information in French clinical documents. To our knowledge, over the last decade, there are less than five relevant papers that study French prescriptions. This paper proposes a new approach for extracting drug-related information from French clinical scanned documents while preserving patients' privacy. In addition, we deployed our method in a health data management platform where it is used to structure drug medical data and help patients organize their drug schedules. It can be implemented on any web or mobile platform. This work closes the gap between theoretical and practical work by creating an application adapted to real production problems. It is a combination of a rule-based phase and a Deep Learning approach. Finally, numerical results show the outperformance and relevance of the proposed methodology.
Free boundary partial regularity in the thin obstacle problem

For the thin obstacle problem in Rn$\mathbb {R}^n$ , n≥2$n\ge 2$ , we prove that at all free boundary points, with the exception of a (n−3)$(n-3)$ ‐dimensional set, the solution differs from its blow‐up by higher order corrections. This expansion entails a C1, 1‐type free boundary regularity result, up to a codimension 3 set.
Interpreting how nonlinear diffusion affects the fate of bistable populations using a discrete modelling framework

Understanding whether a population will survive or become extinct is a central question in population biology. One way of exploring this question is to study population dynamics using reaction–diffusion equations, where migration is usually represented as a linear diffusion term, and birth–death is represented with a nonlinear source term. While linear diffusion is most commonly employed to study migration, there are several limitations of this approach, such as the inability of linear diffusion-based models to predict a well-defined population front. One way to overcome this is to generalize the constant diffusivity, D, to a nonlinear diffusivity function D(C), where C>0 is the population density. While the choice of D(C) affects long-term survival or extinction of a bistable population, working solely in a continuum framework makes it difficult to understand how the choice of D(C) affects survival or extinction. We address this question by working with a discrete simulation model that is easy to interpret. This approach provides clear insight into how the choice of D(C) either encourages or suppresses population extinction relative to the classical linear diffusion model.
Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding

We consider the problem of constructing bounds on the average treatment eﬀect (ATE) when unmeasured confounders exist but have bounded inﬂuence. Speciﬁcally, we assume that omitted confounders could not change the odds of treatment for any unit by more than a ﬁxed factor. We derive the sharp partial identiﬁcation bounds implied by this assumption by leveraging distributionally robust optimization, and we propose estimators of these bounds with several novel robustness properties. The ﬁrst is double sharpness : our estimators consistently estimate the sharp ATE bounds when one of two nuisance parameters is misspeciﬁed and achieve semiparametric eﬃciency when all nuisance parameters are suit-ably consistent. The second is double validity : even when most nuisance parameters are misspeciﬁed, our estimators still provide valid but possibly conservative bounds for the ATE and our Wald conﬁdence intervals remain valid even when our estimators are not asymptotically normal. As a result, our estimators provide a highly credible method for sensitivity analysis of causal inferences.
Statistical mechanical approach of complex networks with weighted links

Systems that consist of many localized constituents interacting with each other can be represented by complex networks. Consistently, network science has become highly popular in vast fields focusing on natural, artificial and social systems. We numerically analyze the growth of d-dimensional geographic networks (characterized by the index α G ⩾ 0; d = 1, 2, 3, 4) whose links are weighted through a predefined random probability distribution, namely P(w)∝e−|w−wc|/τ , w being the weight (w c ⩾ 0; τ > 0). In this model, each site has an evolving degree k i and a local energy εi≡∑j=1kiwij/2 (i = 1, 2, …, N) that depend on the weights of the links connected to it. Each newly arriving site links to one of the pre-existing ones through preferential attachment given by the probability Πij∝εi/dijαA(αA⩾0) , where d ij is the Euclidean distance between the sites. Short- and long-range interactions respectively correspond to α A/d > 1 and 0 ⩽ α A/d ⩽ 1; α A/d → ∞ corresponds to interactions between close neighbors, and α A/d → 0 corresponds to infinitely-ranged interactions. The site energy distribution p(ɛ) corresponds to the usual degree distribution p(k) as the particular instance (w c, τ) = (1, 0). We numerically verify that the corresponding connectivity distribution p(ɛ) converges, when α A/d → ∞, to the weight distribution P(w) for infinitely wide distributions (i.e. τ → ∞, ∀w c) as well as for w c → 0, ∀τ. Finally, we show that p(ɛ) is well approached by the q-exponential distribution eq−βq|ε−wc′| [0⩽wc′(wc,αA/d)⩽wc] , which optimizes the nonadditive entropy S q under simple constraints; q depends only on α A/d, thus exhibiting universality.
Contrastive Object Detection Using Knowledge Graph Embeddings

Object recognition for the most part has been approached as a one-hot problem that treats classes to be discrete and unrelated. Each image region has to be assigned to one member of a set of objects, including a background class, disregarding any similarities in the object types. In this work, we compare the error statistics of the class embeddings learned from a one-hot approach with semantically structured embeddings from natural language processing or knowledge graphs that are widely applied in open world object detection. Extensive experimental results on multiple knowledge-embeddings as well as distance metrics indicate that knowledge-based class representations result in more semantically grounded misclassifications while performing on par compared to one-hot methods on the challenging COCO and Cityscapes object detection benchmarks. We generalize our findings to multiple object detection architectures by proposing a knowledge-embedded design for keypoint-based and transformer-based object detection architectures.
Examining Older Adults' Information Exposure, Wellbeing, and Adherence to Protective Measures During the COVID-19 Pandemic

Older adults are at greater risk of experiencing negative physical and psychological impacts of the novel coronavirus 2019 (COVID-19) pandemic. Our ongoing study is assessing COVID-19 information exposure in adults aged 55 and above compared to other age groups living in Massachusetts and Georgia. This work investigates the potential association between information exposure and wellbeing as well as adherence to COVID-19 protective measures. Our initial results show that older adults received information related to COVID-19 less frequently than the middle-aged group, yet they feel more content and less stressed than the other age groups. Further analysis to identify other potential confounding variables is addressed.
Dynamically Stable Poincaré Embeddings for Neural Manifolds

In a Riemannian manifold, the Ricci flow is a partial differential equation for evolving the metric to become more regular. We hope that topological structures from such metrics may be used to assist in the tasks of machine learning. However, this part of the work is still missing. In this paper, we propose Ricci flow assisted Eucl2Hyp2Eucl neural networks that bridge this gap between the Ricci flow and deep neural networks by mapping neural manifolds from the Euclidean space to the dynamically stable Poincar\'e ball and then back to the Euclidean space. As a result, we prove that, if initial metrics have an $L^2$-norm perturbation which deviates from the Hyperbolic metric on the Poincar\'e ball, the scaled Ricci-DeTurck flow of such metrics smoothly and exponentially converges to the Hyperbolic metric. Specifically, the role of the Ricci flow is to serve as naturally evolving to the stable Poincar\'e ball. For such dynamically stable neural manifolds under the Ricci flow, the convergence of neural networks embedded with such manifolds is not susceptible to perturbations. And we show that Ricci flow assisted Eucl2Hyp2Eucl neural networks outperform with their all Euclidean counterparts on image classification tasks.
Functional CLT for non-Hermitian random matrices

For large dimensional non-Hermitian random matrices $X$ with real or complex independent, identically distributed, centered entries, we consider the fluctuations of $f(X)$ as a matrix where $f$ is an analytic function around the spectrum of $X$. We prove that for a generic bounded square matrix $A$, the quantity $\mathrm{Tr}f(X)A$ exhibits Gaussian fluctuations as the matrix size grows to infinity, which consists of two independent modes corresponding to the tracial and traceless parts of $A$. We find a new formula for the variance of the traceless part that involves the Frobenius norm of $A$ and the $L^{2}$-norm of $f$ on the boundary of the limiting spectrum.
Best Privacy Practice Recommendations for Global Audio Streaming Platforms

Spoon Radio is a rapidly growing global audio streaming platform which currently operates in South Korea, the United States, Japan as well as the Middle East and North Africa. The platform believes that its commitment to user privacy is an important competitive factor. As such, it aims to not just comply with existing privacy regulations in regions where it operates today but to also ensure that it anticipates likely evolution to these regulations and of user expectations. In doing so, Spoon Radio wants to ensure it is well prepared to continue its expansion into new markets. As part of an effort to inform the evolution of its data practices, Spoon Radio reached out to the Privacy Engineering Program at CMU and sponsored a capstone project in which two master's students in the Program worked with Spoon Radio personnel over the course of the 2021 Fall Semester. The present report summarizes best practice recommendations that have emerged from this collaboration. These best practices are a combination of practices that are already implemented or in the process of being implemented by Spoon Radio today as well as more aspirational recommendations, which are expected to help inform Spoon Radio's practices in the future. In this report, best practice recommendations are organized around four stages of the data life cycle: data collection, data storage, data usage, and finally data destruction. A separate section is devoted to content moderation, an area where platforms such as Spoon Radio need to reconcile considerations such as promoting freedom of expression with the need to create a safe and respectful environment that complies with applicable laws and respects relevant cultural values.
Giant effective electron-magnon coupling in a nonmagnetic metal–ferromagnetic insulator heterostructure

Magnon-mediated spin transport across nonmagnetic metal (NM) and ferromagnetic insulator (FI) interface depends critically on electron-magnon coupling. We propose a novel route to enhance electron-magnon coupling dynamically from transport viewpoint. Using non-equilibrium Green's function a theoretical formalism for magnon-mediated spin current is developed. In the language of transport, the effective electron-magnon coupling at NM/FI interface is determined by self-energy of FI lead, which is proportional to density of states (DOS) at NM/FI interface due to nonlinear process of electron-magnon conversion. By modifying interfacial DOS, the spin conductance of 2D and 3D NM/FI systems can be increased by almost three orders of magnitude, setting up a new platform of manipulating dynamical electron-magnon coupling.
Development of a Virtual Orthodontic Alignment System using a Haptic Device

AbstractThis paper presents an efficient and economic virtual teeth setup system. The virtual teeth set-up procedure follows the traditional method of using aligners to assist the dentist in solving orthodontic problems. Unlike the traditional method, our virtual setup procedure first digitizes a plaster model using a 3D scanner. Next, crown segmentation algorithm is used to separate the virtual model into the teeth and gum. The virtual teeth can be adjusted by a force feedback device. During the chewing motion, the continuous detection algorithm is used to prevent the teeth from intersecting. In addition, in order to avoid defects while the teeth are moving, the morphing algorithm is used to deform the virtual gum during teeth movement. After orthodontic planning is finished in our system, a new orthodontic treatment method, called clear aligner, will be added to our system. First, each step of set-up model can be milled using a 5-axis machine. Then the clear aligner can be manufactured by vacuum forming.
Spectroscopic signature of surface states and bunching of bulk subbands in topological insulator (
Bi0.4Sb0.6)2Te3
 thin films

High quality thin films of the topological insulator (Bi$_{0.4}$Sb$_{0.6}$)$_2$Te$_3$ have been deposited on SrTiO$_3$ (111) by molecular beam epitaxy. Their electronic structure was investigated by in situ angle-resolved photoemission spectroscopy and in situ scanning tunneling spectroscopy. The experimental results reveal striking similarities with relativistic ab-initio tight binding calculations. We find that ultrathin slabs of the three-dimensional topological insulator (Bi$_{0.4}$Sb$_{0.6}$)$_2$Te$_3$ display topological surface states, surface states with large weight on the outermost Te atomic layer, and dispersive bulk energy levels that are quantized. We observe that the bandwidth of the bulk levels is strongly reduced. These bunched bulk states as well as the surface states give rise to strong peaks in the local density of states.
AmPyfier: Test amplification in Python

Test amplification aims to automatically improve a test suite. One technique generates new test methods through transformations of the original tests. These test amplification tools heavily rely on analysis techniques that benefit a lot from type declarations present in the source code of projects written in statically typed languages. In dynamically typed languages, such type declarations are not available, and therefore, research regarding test amplification for those languages is sparse. Recent work has brought test amplification to the dynamically typed language Pharo Smalltalk by introducing the concept of dynamic type profiling. The technique is dependent on Pharo‐specific frameworks and has not yet been generalized to other languages. Another significant downside in test amplification tools based on the mutation score of a test suite is their high time cost. In this paper, we present AmPyfier, a tool that brings test amplification and type profiling to the dynamically typed language Python. AmPyfier introduces multi‐metric selection in order to increase the time efficiency of test amplification. We evaluated AmPyfier on 11 open‐source projects and found that AmPyfier could strengthen 37 out of 54 test classes. Multi‐metric selection decreased the time cost ranging from 17% to 98% as opposed to selection based on the full mutation score.
Searching for a source without gradients: how good is infotaxis and how to beat it

Infotaxis is a popular search algorithm designed to track a source of odour in a turbulent environment using information provided by odour detections. To exemplify its capabilities, the source-tracking task was framed as a partially observable Markov decision process consisting in finding, as fast as possible, a stationary target hidden in a two-dimensional grid using stochastic partial observations of the target location. Here, we provide an extended review of infotaxis, together with a toolkit for devising better strategies. We first characterize the performance of infotaxis in domains from one dimension to four dimensions. Our results show that, while being suboptimal, infotaxis is reliable (the probability of not reaching the source approaches zero), efficient (the mean search time scales as expected for the optimal strategy) and safe (the tail of the distribution of search times decays faster than any power law, though subexponentially). We then present three possible ways of beating infotaxis, all inspired by methods used in artificial intelligence: tree search, heuristic approximation of the value function, and deep reinforcement learning. The latter is able to find, without any prior human knowledge, the (near) optimal strategy. Altogether, our results provide evidence that the margin of improvement of infotaxis towards the optimal strategy gets smaller as the dimensionality increases.
Chat2Code: Towards conversational concrete syntax for model specification and code generation, the case of smart contracts

The revolutionary potential of automatic code generation tools based on Model-Driven Engineering (MDE) frameworks has yet to be realized. Beyond their ability to help software professionals write more accurate, reusable code, they could make programming accessible for a whole new class of non-technical users. However, non-technical users have been slow to embrace these tools. This may be because their concrete syntax is often patterned after the operations of textual or graphical interfaces. The interfaces are common, but users would need more extensive, precise and detailed knowledge of them than they can be assumed to have, to use them as concrete syntax. Conversational interfaces (chatbots) offer a much more accessible way for non-technical users to generate code. In this paper, we discuss the basic challenge of integrating conversational agents within Model-Driven Engineering (MDE) frameworks, then turn to look at a specific application: the auto-generation of smart contract code in multiple languages by non-technical users, based on conversational syntax. We demonstrate how this can be done, and evaluate our approach by conducting user experience survey to assess the usability and functionality of the chatbot framework.