Miracle-dz/UniFAR-FTUs huggingface dataset:
Profiling Occupant Behaviour in Danish Dwellings using Time Use Survey Data - Part II: Time-related Factors and Occupancy

SUMMARY Occupant behaviour has been shown to be one of the key driving factors of uncertainty in prediction of energy consumption in buildings. Building occupants affect building energy use directly and indirectly by interacting with building energy systems such as adjusting temperature set-points, switching lights on/off, using electrical devices and opening/closing windows. Furthermore, building inhabitants’ daily activity profiles clearly shape the timing of energy demand in households. Modelling energy-related human activities throughout the day, therefore, is crucial to defining more realistic occupant profiles for prediction of energy use to reduce the gap between predicted and real building energy consumptions. In this study, we exploit diary-based Danish Time Use Surveys for understanding and modelling occupant behaviour in the residential sector in Denmark. This paper is a continuation of “Profiling occupant behaviour in Danish Dwellings using Time Use Survey Data: Part I” that focuses on time-related and time-specific aspects of occupants’ activity profiles. Each activity was analysed in terms of daily time duration and starting/ending times. In detail, a Kaplan-Meier Survival analysis is performed in order to create an estimator of the survival function of the various activities. Finally, this study provides representative occupancy profiles in Danish households during weekdays and weekends.
Utilization assessment of antimicrobial prophylaxis in surgical patients at tertiary care teaching hospital

Background: Surgical site infection (SSI) includes all postoperative infections occurring at surgical sites. It is one of the most common causes of the morbidity and mortality in postoperative patients. Surgical antimicrobial prophylaxis (SAP) is a brief course of an anti-microbial agent for the prevention of SSI. Judicious use of antibiotics can decrease the morbidity associated with SSI, but inappropriate antibiotic use leads to antibiotic resistance. Aims and Objectives: Inappropriate use of antibiotics is still rampant, and the present study was planned to assess the prophylactic antibiotic usage in the surgery department. Materials and Methods: A prospective study was conducted on 200 patients in the Department of General Surgery, MMIMSR, Mullana, Haryana, with prior approval from the Institutional Ethical Committee. Results: Out of 200 patients, 193 received intravenous preoperative antimicrobial dose on the day of surgery. Most commonly prescribed group was the third-generation cephalosporin (61%). Fixed-dose combinations were also frequently used, among them piperacillin-tazobactam (20.5%) was the most common combination used. Average duration of postoperative prophylactic antimicrobial administration was 8.23 days. Conclusion: This study showed antibiotics were used as prophylactic in almost every patient. Still, many patients do suffer from SSI as average duration of antibiotic usage is around 8 days. To combat inappropriate antibiotic usage, there is need to make the SAP guidelines and to adhere to these guidelines.
Software Ecosystem Call Graph for Dependency Management

A popular form of software reuse is the use of open source software libraries hosted on centralized code repositories, such as Maven or npm. Developers only need to declare dependencies to external libraries, and automated tools make them available to the workspace of the project. Recent incidents, such as the Equifax data breach and the leftpad package removal, demonstrate the difficulty in assessing the severity, impact and spread of bugs in dependency networks. While dependency checkers are being adapted as a counter measure, they only provide indicative information. To remedy this situation, we propose a fine-grained dependency network that goes beyond packages and into call graphs. The result is a versioned ecosystem-level call graph. In this paper, we outline the process to construct the proposed graph and present a preliminary evaluation of a security issue from a core package to an affected client application.
Randomized Online Matching in Regular Graphs

In this paper we study the classic online matching problem, introduced in the seminal work of Karp, Vazirani and Vazirani (STOC 1990), in regular graphs. For such graphs, an optimal deterministic algorithm as well as efficient algorithms under stochastic input assumptions were known. In this work, we present a novel randomized algorithm with competitive ratio tending to one on this family of graphs, under adversarial arrival order. Our main contribution is a novel algorithm which achieves competitive ratio [EQUATION] in expectation on d-regular graphs. In contrast, we show that all previously-studied online algorithms have competitive ratio strictly bounded away from one. Moreover, we show the convergence rate of our algorithm's competitive ratio to one is nearly tight, as no algorithm achieves competitive ratio better than [EQUATION]. Finally, we show that our algorithm yields a similar competitive ratio with high probability, as well as guaranteeing each vertex a probability of being matched tending to one.
The expression and regulation of matrilysin (MMP-7) in human colon cancer and leukaemia cell lines

Matrilysin (MMP-7, EC 3.4.24.23) is the smallest member o f the matrix metalloproteinase (MMP) family and has been shown to be overexpressed in various tumours including breast and colon cancers. Matrilysin has also been shown to play an important role in several aspects of tumour biology including growth, progression, invasion and metastasis. With respect to colon cancer, matrilysin is unique in that it is the only MMP expressed exclusively by the malignant epithelia o f colonic adenocarcinomas. These facts combine to make matrilysin a promising therapeutic target. However, in order to develop drugs which specifically inhibit matrilysin it is important to understand how matrilysin gene expression is controlled, something which to date remains poorly understood. We have examined a panel of human colon tumour cell lines and have shown that matrilysin expression can be upregulated by a number of cytokines including EGF, IL-6 and bFGF. Analysis of the matrilysin promoter revealed the presence of a number of potential transcription factor binding sites including three ETS sites. We have shown that EGF treatment increased matrilysin gene expression by activation of PEA3 transcription factors using artificial promoter, western blot and EMSA analysis. ‘Supershift’ EMSA analysis showed that other PEA3 subfamily members such as ERM and ER81 may also be involved which is in agreement with other studies. In addition, we have found that EGF increased cellular levels o f [3-catenin through destabilisation of the E-cadherin/catenin complex which resulted in increased binding to the T c f site within the matrilysin promoter. We also examined the expression and regulation of matrilysin in the K562 and HL-60 myeloid leukaemia cell lines. Results showed that only the K562 cell line expressed matrilysin and in vitro invasion assays showed that the K562 cells were up to 4 times more invasive than the HL- 60 cell line. Matrilysin antibody blocking experiments showed a significant decrease in invasion in the K562 cell line suggesting a role for matrilysin in leukaemia invasion. The MMP and TIMP profiles o f these cell lines were also examined. Our data suggests that EGF plays an important role in the regulation of matrilysin gene expression via a number of new mechanisms. Furthermore, we have shown that matrilysin plays an important role in leukaemia cell line invasion. These findings have identified possible new drug targets that will inhibit matrilysin expression which in turn should lead to decreased tumourigenesis and invasion and metastasis.
Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge

Public evaluation campaigns and datasets promote active development in target research areas, allowing direct comparison of algorithms. The second edition of the challenge on detection and classification of acoustic scenes and events (DCASE 2016) has offered such an opportunity for development of the state-of-the-art methods, and succeeded in drawing together a large number of participants from academic and industrial backgrounds. In this paper, we report on the tasks and outcomes of the DCASE 2016 challenge. The challenge comprised four tasks: acoustic scene classification, sound event detection in synthetic audio, sound event detection in real-life audio, and domestic audio tagging. We present each task in detail and analyze the submitted systems in terms of design and performance. We observe the emergence of deep learning as the most popular classification method, replacing the traditional approaches based on Gaussian mixture models and support vector machines. By contrast, feature representations have not changed substantially throughout the years, as mel frequency-based representations predominate in all tasks. The datasets created for and used in DCASE 2016 are publicly available and are a valuable resource for further research.
EX draconis: using eclipses to separate outside-in and inside-out outbursts

We present a study of the eclipses in the accreting white dwarf EX Dra during TESS Cycles 14 and 15. During both of the two outbursts present in this dataset, the eclipses undergo a hysteretic loop in eclipse-depth/out-of-eclipse-flux space. In each case, the direction in which the loops are executed strongly suggests an outburst which is triggered near the inner edge of the accretion disk and propagates outwards. This in turn suggests that the outbursts in EX Dra are 'Inside Out' outbursts; events predicted by previous hydrodynamic studies of dwarf nova accretion disks and confirmed spectroscopically in a number of other accreting white dwarf systems. We therefore propose that the direction of the loop executed in eclipse-depth/out-of-eclipse flux space be used as a test to phenomenologically distinguish between 'inside out' and 'outside in' outbursts in other eclipsing dwarf novae; a reliable and purely photometric test to differentiate between these phenomena.
Total disconnectedness of Julia sets of random quadratic polynomials

Abstract For a sequence of complex parameters $(c_n)$ we consider the composition of functions $f_{c_n} (z) = z^2 + c_n$ , the non-autonomous version of the classical quadratic dynamical system. The definitions of Julia and Fatou sets are naturally generalized to this setting. We answer a question posed by Brück, Büger and Reitz, whether the Julia set for such a sequence is almost always totally disconnected, if the values $c_n$ are chosen randomly from a large disc. Our proof is easily generalized to answer a lot of other related questions regarding typical connectivity of the random Julia set. In fact we prove the statement for a much larger family of sets than just discs; in particular if one picks $c_n$ randomly from the main cardioid of the Mandelbrot set, then the Julia set is still almost always totally disconnected.
Dp-minimal expansions of $(\mathbb{Z},+)$ via dense pairs via Mordell-Lang

This is a contribution to the classification problem for dp-minimal expansions of $(\mathbb{Z},+)$. Let $S$ be a dense cyclic group order on $(\mathbb{Z},+)$. We use results on "dense pairs" to construct uncountably many dp-minimal expansions of $(\mathbb{Z},+,S)$. These constructions are applications of the Mordell-Lang conjecture and are the first examples of "non-modular" dp-minimal expansions of $(\mathbb{Z},+)$. We canonically associate an o-minimal expansion $\mathcal{R}$ of $(\mathbb{R},+,\times)$, an $\mathcal{R}$-definable circle group $\mathbb{H}$, and a character $\mathbb{Z} \to \mathbb{H}$ to a "non-modular" dp-minimal expansion of $(\mathbb{Z},+,S)$. We also construct a "non-modular" dp-minimal expansion of $(\mathbb{Z},+,\mathrm{Val}_p)$ from the character $\mathbb{Z} \to \mathbb{Z}^\times_p$, $k \mapsto \mathrm{exp}(pk)$.
H i absorption towards radio active galactic nuclei of different accretion modes

We present results of H i absorption experiment done using the Giant Metrewave Radio Telescope (GMRT) towards 27 low- and intermediate-luminosity ($P_{\rm 1.4 GHz}\, \sim 10^{23}$-1026 W Hz−1) radio active galactic nuclei (AGNs), classified as either low excitation radio galaxies (LERGs) or high excitation radio galaxies (HERGs) and with WISE colour W2[4.6 μm]−W3[12 μm]> 2. We report H i absorption detection towards seven radio AGNs, six of which are new. Combined with other sources from literature classified as LERGs or HERGs, we confirm our earlier result that compact radio AGNs with WISE colour W2−W3 > 2 have higher detection rates compared to those with W2−W3 < 2. We find that H i absorption detection rate is higher for HERGs (37.0$^{+15.8}_{-11.5}$ per cent) compared to LERGs (22.0$^{+3.9}_{-3.4}$ per cent), mainly due to a larger fraction of HERGs being gas and dust rich with a younger stellar population compared to LERGs. However, for similar compact radio structures and host galaxies with WISE colours W2−W3 > 2, we don’t find any significant difference in detection rates of two types of AGNs implying detection of H i gas may not necessarily mean high excitation mode AGN. We further analysed the kinematics towards these sources. We find that while LERGs show a wide range in the shift of centroid velocities ($\sim \, -$479 to +356 km s−1) relative to the optical systemic velocity, most of the HERGs have centroid velocity shift less than 200 km s−1, possibly due to differences in jet-interstellar medium interaction.
Turán‐ and Ramsey‐type results for unavoidable subgraphs

We study Turán‐ and Ramsey‐type problems on edge‐colored graphs. A two‐edge‐colored graph is called ε $\varepsilon $ ‐balanced if each color class contains at least an ε $\varepsilon $ ‐proportion of its edges. Given a family F ${\rm{ {\mathcal F} }}$ of two‐edge‐colored graphs, the Ramsey‐type function R(ε,F) $R(\varepsilon ,{\rm{ {\mathcal F} }})$ is the smallest n $n$ for which any ε $\varepsilon $ ‐balanced K n ${K}_{n}$ must contain a copy of an F∈F $F\in {\rm{ {\mathcal F} }}$ , and the Turán‐type function ex(ε,n,F) $\text{ex}(\varepsilon ,n,{\rm{ {\mathcal F} }})$ is the maximum number of edges in an n $n$ ‐vertex ε $\varepsilon $ ‐balanced graph which avoids all of F ${\rm{ {\mathcal F} }}$ . In this paper, we show that for any ε ′
Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device

Coronavirus Disease 2019 (COVID-19) has become a serious global epidemic in the past few months and caused huge loss to human society worldwide. For such a large-scale epidemic, early detection and isolation of potential virus carriers is essential to curb the spread of the epidemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the epidemic, many people tend to wear masks to reduce the risk of getting sick. Therefore, in this paper, we propose a portable non-contact method to screen the health condition of people wearing masks through analysis of the respiratory characteristics. The device mainly consists of a FLIR one thermal camera and an Android phone. This may help identify those potential patients of COVID-19 under practical scenarios such as pre-inspection in schools and hospitals. In this work, we perform the health screening through the combination of the RGB and thermal videos obtained from the dual-mode camera and deep learning architecture.We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with attention mechanism is applied to the respiratory data to obtain the health screening result. The results of validation experiments show that our model can identify the health status on respiratory with the accuracy of 83.7\% on the real-world dataset. The abnormal respiratory data and part of normal respiratory data are collected from Ruijin Hospital Affiliated to The Shanghai Jiao Tong University Medical School. Other normal respiratory data are obtained from healthy people around our researchers. This work demonstrates that the proposed portable and intelligent health screening device can be used as a pre-scan method for respiratory infections, which may help fight the current COVID-19 epidemic.
SPECTER: Document-level Representation Learning using Citation-informed Transformers

Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, accurate embeddings of documents are a necessity. We propose SPECTER, a new method to generate document-level embedding of scientific papers based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, Specter can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that Specter outperforms a variety of competitive baselines on the benchmark.
HLA predictions from the bronchoalveolar lavage fluid samples of five patients at the early stage of the wuhan seafood market COVID-19 outbreak

We are in the midst of a global viral pandemic, one with no cure and a high mortality rate. The Human Leukocyte Antigen (HLA) gene complex plays a critical role in host immunity. We predicted HLA class I and II alleles from the transcriptome sequencing data prepared from the bronchoalveolar lavage fluid samples of five patients at the early stage of the COVID-19 outbreak. We identified the HLA-I allele A*24:02 in four out of five patients, which is higher than the expected frequency (17.2%) in the South Han Chinese population. The difference is statistically significant with a p-value less than 10−4. Our analysis results may help provide future insights on disease susceptibility.
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing

Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly detection tasks to the cloud incurs long delay. In this paper, we propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems to solve this problem, for both univariate and multivariate IoT data. First, we construct multiple anomaly detection DNN models with increasing complexity, and associate each model with a layer in HEC from bottom to top. Then, we design an adaptive scheme to select one of these models on the fly, based on the contextual information extracted from each input data. The model selection is formulated as a contextual bandit problem characterized by a single-step Markov decision process, and is solved using a reinforcement learning policy network. We build an HEC testbed, implement our proposed approach, and evaluate it using real IoT datasets. The demo shows that our proposed approach significantly reduces detection delay (e.g., by 71.4% for univariate dataset) without sacrificing accuracy, as compared to offloading detection tasks to the cloud. We also compare it with other baseline schemes and demonstrate that it achieves the best accuracy-delay tradeoff.1
Two-way covert microwave quantum communication

Quantum communication addresses the problem of exchanging information across macroscopic distances by employing encryption techniques based on quantum mechanical laws. Here, we advance a new paradigm for quantum communication by combining backscattering concepts with covert communication in the microwave regime. Our protocol allows communication between Alice, who uses only discrete phase modulations, and Bob, who has access to cryogenic microwave technology. Security is reached by covering the carrier signal through the presence of the thermal noise in the environment. We find the ultimate bounds for the receiver performance under different assumptions, proving that quantum correlations can enhance the signal-to-noise ratio by up to $6$ dB using a collective strategy, and $3$ dB with local strategies. We extend the standard square-root law for one-way covert quantum communication to the two-way setup, proving that $O(\sqrt{n})$ number of bits can be reliably transmitted over $n$ channel usages. We show how to engineer all the stages of the entanglement-assisted version of the protocol, by using Jaynes-Cumming interactions and qubit measurements. Our proposal makes a decisive step toward implementing quantum communication concepts in the previously uncharted $1-10$ GHz frequency range.
Nonholomorphic Ramanujan-type congruences for Hurwitz class numbers

Significance Hurwitz class numbers were introduced over 100 y ago, and they impact different areas of mathematics. In particular, their generating function is a so-called mock modular form. Despite their importance, essentially nothing has been known about the divisibility properties of Hurwitz class numbers on arithmetic progressions. Our work provides results on such divisibility properties, which differ drastically from known divisibility results for coefficients of other mock modular forms. In contrast to all other known Ramanujan-type congruences, we discover that Ramanujan-type congruences for Hurwitz class numbers can be supported on nonholomorphic generating series. We establish a divisibility result for such nonholomorphic congruences of Hurwitz class numbers. The two key tools in our proof are the holomorphic projection of products of theta series with a Hurwitz class number generating series and a theorem by Serre, which allows us to rule out certain congruences.
Skew-products with concave fiber maps: entropy and weak$\ast$ approximation of measures and bifurcation phenomena

We consider skew-products with concave interval fiber maps over a certain subshift. This subshift naturally occurs as the projection of those orbits that stay in a given neighborhood. It gives rise to a new type of symbolic space which is (essentially) coded. The fiber maps have expanding and contracting regions. As a consequence, the skew-product dynamics has pairs of horseshoes of different type of hyperbolicity. In some cases, they dynamically interact due to the superimposed effects of the (fiber) contraction and expansion, leading to nonhyperbolic dynamics that is reflected on the ergodic level (existence of nonhyperbolic ergodic measures). We provide a description of the space of ergodic measures on the base as an entropy-dense Poulsen simplex. Those measures lift canonically to ergodic measures for the skew-product. We explain when and how the spaces of (fiber) contracting and expanding ergodic measures glue along the nonhyperbolic ones. A key step is the approximation (in the weak$\ast$ topology and in entropy) of nonhyperbolic measures by ergodic ones, obtained only by means of concavity without involving the standard "blending-like" arguments. The description of homoclinic relations is also a key instrument. We also see that these skew-products can be embedded in increasing entropy one-parameter family of diffeomorphisms which stretch from a heterodimensional cycle to a collision of homoclinic classes. We study associated bifurcation phenomena that involve a jump of the space of ergodic measures and, in some cases, also of entropy.