Miracle-dz/UniFAR-FTUs huggingface dataset:
Doctors vs. Nurses: Understanding the Great Divide in Vaccine Hesitancy among Healthcare Workers

Healthcare workers such as doctors and nurses are expected to be trustworthy and creditable sources of vaccine-related information. Their opinions toward the COVID-19 vaccines may influence the vaccine uptake among the general population. However, vaccine hesitancy is still an important issue even among the healthcare workers. Therefore, it is critical to understand their opinions to help reduce the level of vaccine hesitancy. There have been studies examining healthcare workers’ viewpoints on COVID-19 vaccines using questionnaires. Reportedly, a considerably higher proportion of vaccine hesitancy is observed among nurses, compared to doctors. We intend to verify and study this phenomenon at a much larger scale and in fine grain using social media data, which has been effectively and efficiently leveraged by researchers to address real-world issues during the COVID-19 pandemic. More specifically, we use a keyword search to identify healthcare workers and further classify them into doctors and nurses from the profile descriptions of the corresponding Twitter users. Moreover, we apply a transformer-based language model to remove irrelevant tweets. Sentiment analysis and topic modeling are employed to analyze and compare the sentiment and thematic differences in the tweets posted by doctors and nurses. We find that doctors are overall more positive toward the COVID-19 vaccines. The focuses of doctors and nurses when they discuss vaccines in a negative way are in general different. Doctors are more concerned with the effectiveness of the vaccines over newer variants while nurses pay more attention to the potential side effects on children. Therefore, we suggest that more customized strategies should be deployed when communicating with different groups of healthcare workers.
The Symmetric Regularized-Long-Wave Equation: Ill-posedness and Long Period Limit

In the present work we obtain two important results for the Symmetric Regulraized-Long-Wave equation. First we prove that the initial value problem for this equation is ill-posed for data in $H^s(\mathbb{R})\times H^{s-1}(\mathbb{R}),$ if $s< 0,$ in the sense that the flow-map cannot be continuous at the origin from $H^s(\mathbb{R})\times H^{s-1}(\mathbb{R})$ to even $(\mathcal{D}'(\mathbb{R}))^2. $ We also establish an exact theory of convergence of the periodic solutions to the continuous one, in Sobolev spaces, as the period goes to infinity.
Kac's random walk and coupon collector's process on posets

In the first part of this work, we study a long standing open problem on the mixing time of Kac's random walk on SO(n, R) by random rotations. We obtain an upper bound mix = O (n . log n) for the weak convergence which is close to the trivial lower bound Q (n ). This improves the upper bound O (n4 log n) by Diaconis and SaloffCoste 1131. The proof is a variation on the coupling technique we develop to bound the mixing time for compact Markov chains, which is of independent interest. In the second part, we consider a generalization of the coupon collector's problem in which coupons are allowed to be collected according to a partial order. Along with the discrete process, we also study the Poisson version which admits a tractable parametrization. This allows us to prove convexity of the expected completion time E T with respect to sample probabilities, which has been an open question for the standard coupon collector's problem. Since the exact computation of E is formidable, we use convexity to establish the upper and the lower bound (these bounds differ by a log factor). We refine these bounds for special classes of posets. For instance, we show the cut-off phenomenon for shallow posets that are closely connected to the classical Dixie Cup problem. We also prove the linear growth of the expectation for posets whose number of chains grows at most exponentially with respect to the maximal length of a chain. Examples of these posets are d-dimensional grids, for which the Poisson process is usually referred as the last passage percolation problem. In addition, the coupon collector's process on a poset can be used to generate a random linear extension. We show that for forests of rooted directed trees it is possible to assign sample probabilities such that the induced distribution over all linear extensions will be uniform. Finally, we show the connection of the process with some combinatorial properties of posets. Thesis Supervisor: Igor Pak Title: Associate Professor of Applied Mathematics Acknowledgments I am grateful to Richard Dudley, Alex Postnikov, Peter Shor, Richard Stanley, and Dan Stroock for the insightful remarks and helpful conversations on the subject. Special thanks to Ravi Montenegro and Yuval Peres for finding a critical error in the early version of this paper, and to Igor Pak for having made my work as interesting and unforgettable as was possible.
Luminance and contrast ideal balancing based tone mapping algorithm

The tone mapping field represents a challenge for all the HDR researchers. Indeed, this field is very important since, it offers better display terms for the end-user. This paper details a design of a recent tone mapping operator used in high dynamic range imaging systems. The proposed operator represents a local method that uses an adaptable factor which combines both the average neighbouring contrast and the brightness difference. Thanks to that, this solution provides good results with better brightness, contrast, and visibility and without producing neither undesired artifacts nor shadow effects.
Higher genus polylogarithms on families of algebraic curves

: We describe polylogarithms on families of pointed curves of any genus using the theory of the universal Mumford curve which is the family of all pointed Mumford curves. As an application of the result, we show that the polylogarithms are expressed by rational polylogarithms and multiple zeta values.
Gluformer: Transformer-based Personalized glucose Forecasting with uncertainty quantification

Deep learning models achieve state-of-the art results in predicting blood glucose trajectories, with a wide range of architectures being proposed. However, the adaptation of such models in clinical practice is slow, largely due to the lack of uncertainty quantification of provided predictions. In this work, we propose to model the future glucose trajectory conditioned on the past as an infinite mixture of basis distributions (i.e., Gaussian, Laplace, etc.). This change allows us to learn the uncertainty and predict more accurately in the cases when the trajectory has a heterogeneous or multi-modal distribution. To estimate the parameters of the predictive distribution, we utilize the Transformer architecture. We empirically demonstrate the superiority of our method over existing state-of-the-art techniques both in terms of accuracy and uncertainty on the synthetic and benchmark glucose data sets.
Variational Autoencoder Kernel Interpretation and Selection for Classification

This work proposed kernel selection approaches for probabilistic classifiers based on features produced by the convolutional encoder of a variational autoencoder. Particularly, the developed methodologies allow the selection of the most relevant subset of latent variables. In the proposed implementation, each latent variable was sampled from the distribution associated with a single kernel of the last encoder’s convolution layer, as an individual distribution was created for each kernel. Therefore, choosing relevant features on the sampled latent variables makes it possible to perform kernel selection, filtering the uninformative features and kernels. Such leads to a reduction in the number of the model’s parameters. Both wrapper and filter methods were evaluated for feature selection. The second was of particular relevance as it is based only on the distributions of the kernels. It was assessed by measuring the Kullback-Leibler divergence between all distributions, hypothesizing that the kernels whose distributions are more similar can be discarded. This hypothesis was confirmed since it was observed that the most similar kernels do not convey relevant information and can be removed. As a result, the proposed methodology is suitable for developing applications for resource-constrained devices.
Knowledge Base Question Answering: A Semantic Parsing Perspective

Recent advances in deep learning have greatly propelled the research on semantic parsing. Improvement has since been made in many downstream tasks, including natural language interface to web APIs, text-to-SQL generation, among others. However, despite the close connection shared with these tasks, research on question answering over knowledge bases (KBQA) has comparatively been progressing slowly. We identify and attribute this to two unique challenges of KBQA, schema-level complexity and fact-level complexity. In this survey, we situate KBQA in the broader literature of semantic parsing and give a comprehensive account of how existing KBQA approaches attempt to address the unique challenges. Regardless of the unique challenges, we argue that we can still take much inspiration from the literature of semantic parsing, which has been overlooked by existing research on KBQA. Based on our discussion, we can better understand the bottleneck of current KBQA research and shed light on promising directions for KBQA to keep up with the literature of semantic parsing, particularly in the era of pre-trained language models.
Deep Reinforcement Learning for Online Latency Aware Workload Offloading in Mobile Edge Computing

Owing to the resource-constrained feature of Internet of Things (IoT) devices, offloading tasks from IoT devices to the nearby mobile edge computing (MEC) servers can not only save the energy of IoT devices but also reduce the response time of executing the tasks. However, offloading a task to the nearest MEC server may not be the optimal solution due to the limited computing resources of the MEC server. Thus, jointly optimizing the offloading decision and resource management is critical, but yet to be explored. Here, offloading decision refers to where to offload a task and resource management implies how much computing resource in an MEC server is allocated to a task. By considering the waiting time of a task in the communication and computing queues (which are ignored by most of the existing works) as well as tasks priorities, we propose the Deep reinforcement lEarning based offloading deCision and rEsource managemeNT (DECENT) algorithm, which leverages the advantage actor critic method to optimize the offloading decision and computing resource allocation for each arriving task in real-time such that the cumulative weighted response time can be minimized. The performance of DECENT is demonstrated via different experiments.
Analyzing Wearables Dataset to Predict ADLs and Falls: A Pilot Study

Healthcare is an important aspect of human life. Use of technologies in healthcare has increased manifolds after the pandemic. Internet of Things based systems and devices proposed in literature can help elders, children and adults facing/experiencing health problems. This paper exhaustively reviews thirty-nine wearable based datasets which can be used for evaluating the system to recognize Activities of Daily Living and Falls. A comparative analysis on the SisFall dataset using five machine learning methods i.e., Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbor, Decision Tree and Naive Bayes is performed in python. The dataset is modified in two ways, in first all the attributes present in dataset are used as it is and labelled in binary form. In second, magnitude of three axes(x,y,z) for three sensors value are computed and then used in experiment with label attribute. The experiments are performed on one subject, ten subjects and all the subjects and compared in terms of accuracy, precision and recall. The results obtained from this study proves that KNN outperforms other machine learning methods in terms of accuracy, precision and recall. It is also concluded that personalization of data improves accuracy.
Non-exponential Decay in Classical Stochastic Processes with Memory

The initial time-dependence of a state in circumstances where it makes transitions to, or decay to, a second state has been investigated. In classical stochastic processes, the observed time dependence of transition or decay proportional to $t^2$ is attributed to the noise with memory. In contrast to quantum mechanics, the quadratic form of initial decay is unable to decelerate the evolution of the system.
Testing for the 'Existence of a Long-run Relationship'

This paper develops a new approach to testing for the existence of a linear long-run relationship, when the orders of integration of the underlying regressors are not known with certainty. The test is the standard Wald or F statistic for testing the significance of the lagged levels of the variables in a first-difference regression. The asymptotic distributions of these statistics are non-standard under the null hypothesis that there exists no long-run relationship between the levels of the included variables. Two sets of asymptotic critical values are provided: one set assuming that all regressors are I(1), and another assuming they are all I(0). These provide a band covering all possible classifications of the regressors into I(0), I(1) or mutually cointegrated.
Spectral Hypergraph Sparsification via Chaining

In a hypergraph on n vertices where D is the maximum size of a hyperedge, there is a weighted hypergraph spectral -sparsifier with at most O(−2 log(D) · n logn) hyperedges. This improves over the bound of Kapralov, Krauthgamer, Tardos and Yoshida (2021) who achieve O(−4 n (logn)3), as well as the bound O(−2 D3 n logn) obtained by Bansal, Svensson, and Trevisan (2019). The same sparsification result was obtained independently by Jambulapati, Liu, and Sidford (2022).
Antiferromagnetic Chern insulator in centrosymmetric systems

An antiferromagnetic Chern insulator (AFCI) can exist if the effect of the time-reversal transformation on the electronic state cannot be compensated by a space group operation. The AFCI state with collinear magnetic order is already realized in noncentrosymmetric honeycomb structures through the Kane-Mele-Hubbard model. In this paper, we demonstrate the existence of the collinear AFCI in a square lattice model which preserves the inversion symmetry. Our study relies on the time-reversal-invariant Harper-Hofstadter-Hubbard model extended by a next-nearest-neighbor hopping term including spin-orbit coupling and a checkerboard potential. We show that an easy $z$-axis AFCI appears between the band insulator at weak and the easy $xy$-plane AF Mott insulator at strong Hubbard repulsion provided the checkerboard potential is large enough. The close similarity between our results and the results obtained for the noncentrosymmetric Kane-Mele-Hubbard model suggests the AFCI as a generic consequence of spin-orbit coupling and strong electronic correlation which exists beyond a specific model or lattice structure. An AFCI with the electronic and the magnetic properties originating from the same strongly interacting electrons is promising candidate for a strong magnetic blue shift of the charge gap below the N\'eel temperature and for realizing the quantum anomalous Hall effect at higher temperatures so that applications for data processing become possible.
Beamforming Based on Finite-Rate Feedback

Multiple-input multi-output (MIMO), emerged as one of the most significant breakthroughs inwireless communications theory over the last two decades, is considered as a key tomeeting the increasing demands for high data rates and mass wireless access services over a limited spectrum bandwidth. Transmit beamforming with receive combining is a low-complexity technique to exploit the benefits of MIMO wireless systems. It has received much interest over the last few years, because it provides substantial performance improvement without sophisticated signal processing. In order to enable the beamforming operation, either full or partial channel state information (CSI) has to be furnished to the transmitter. With full CSI, the optimal transmit beamforming scheme is maximum ratio transmission (MRT) [Dighe et al. (2003a)], where the principal right singular vector of the channel matrix is used as the beamforming vector. In Rayleigh fading, exact expressions for the symbol error rate (SER) of MRT were derived in [Dighe et al. (2003a;b)], and the asymptotic error performance was studied in [Zhou & Dai (2006)]. However, in certain application scenarios, e.g. frequency division duplex (FDD) systems, CSI is not usually available at the transmitter. To cope with the lack of CSI, a beamforming scheme based on finite-rate feedback has been proposed in the literature, where the CSI is quantized at the receiver and fed back to the transmitter. This scheme has been adopted in current 3GPP specifications. Under the assumption of independent block-fading and the assumption of delayand error-free feedback, the design and performance analysis of quantized beamforming systems have been well investigated. Different beamformer design methods were developed in [Mukkavilli et al. (2003); Love & Heath (2003); Xia & Giannakis (2006)]. In multiple-input single-output (MISO) cases, lower bounds to the outage probability and symbol error rate (SER) were derived in [Mukkavilli et al. (2003)] and [Zhou et al. (2005)], respectively. In MIMO cases, the average receive signal-to-noise ratio (SNR) and outage probability were studied in [Mondal & Heath (2006)]. Analytical results showed that full diversity order can be achieved by a well-designed beamformer [Love & Heath (2005)]. This chapter highlights recent advances in beamforming based on finite-rate feedback from a communication-theoretic perspective. We first study the SER performance when the feedback link is delayand error-free. Then non-ideal factors in the feedback link are investigated, and countermeasures are proposed to compensate the performance degradation due to non-ideal feedback. 12
People detection and social distancing classification in smart cities for COVID-19 by using thermal images and deep learning algorithms

COVID-19 is a disease caused by severe respiratory syndrome coronavirus. It was identified in December 2019 in Wuhan, China. It has resulted in an ongoing pandemic that caused infected cases including some deaths. Coronavirus is primarily spread between people during close contact. Motivating to this notion, this research proposes an artificial intelligence system for social distancing classification of persons by using thermal images. By exploiting YOLOv2 (you look at once), a deep learning detection technique is developed for detecting and tracking people in indoor and outdoor scenarios. An algorithm is also implemented for measuring and classifying the distance between persons and automatically check if social distancing rules are respected or not. Hence, this work aims at minimizing the spread of the COVID-19 virus by evaluating if and how persons comply with social distancing rules. The proposed approach is applied to images acquired through thermal cameras, to establish a complete AI system for people tracking, social distancing classification, and body temperature monitoring. The training phase is done with two datasets captured from different thermal cameras. Ground Truth Labeler app is used for labeling the persons in the images. The achieved results show that the proposed method is suitable for the creation of a smart surveillance system in smart cities for people detection, social distancing classification, and body temperature analysis.
Stress-directed compositional patterning of SiGe substrates for lateral quantum barrier manipulation

While vertical stacking of quantum well and dot structures is well established in heteroepitaxial semiconductor materials, manipulation of quantum barriers in the lateral directions poses a significant engineering challenge. Here, we demonstrate lateral quantum barrier manipulation in a crystalline SiGe alloy using structured mechanical fields to drive compositional redistribution. To apply stress, we make use of a nano-indenter array that is pressed against a Si0.8Ge0. 2 wafer in a custom-made mechanical press. The entire assembly is then annealed at high temperatures, during which the larger Ge atoms are selectively driven away from areas of compressive stress. Compositional analysis of the SiGe substrates reveals that this approach leads to a transfer of the indenter array pattern to the near-surface elemental composition, resulting in near 100% Si regions underneath each indenter that are separated from each other by the surrounding Si0.8Ge0. 2 bulk. The “stress transfer” process is studied in detail us.
9 v 2 6 M ar 2 00 2 Theory for the photon statistics of random lasers

A theory for the photon statistics of a random laser is presented. Noise is described by Langevin terms, where fluctuations of both the electromagnetic field and of the medium are included. The theory is valid for all lasers with small outcoupling when the laser cavity is large compared to the wavelength of the radiation. The theory is applied to a chaotic laser cavity with a small opening. It is known that a large number of modes can be above threshold simultaneously in such a cavity. It is shown the amount of fluctuations is increased above the Poissonian value by an amount that depends on the number of modes above threshold.
Non-trivial band topology in the superconductor AuSn4: a first principle study

Topological semimetals such as Weyl or Dirac semimetal with superconductivity have emerged as a new class of topological materials to realize and study Majorana Fermion. This article reports the density functional theory calculated bulk electronic band structure of recently discovered topological superconductor candidate AuSn4. The study has been performed on AuSn4 considering two space groups symmetries viz. Aea2 and Ccce as reported earlier. This study is further extended to the calculation of Z2 invariants. The Fermi surfaces corresponding to the bands, which are responsible for non-trivial band topology along with the surface states are also mapped. The complete study suggests that AuSn4 is a topological semimetal. On AuSn4, it is the first report in the literature showing the non-trivial band topology based on first-principle calculations.
On the structure of non dentable subsets of C({\omega}^{\omega}^k)

It is shown that there is no K closed convex bounded non-dentable subset of C({\omega}^{\omega} ^k) such that on the subsets of K the PCP and the RNP are equivalent properties. Then applying Schachermayer-Rosenthal theorem, we conclude that every non-dentable K contains non-dentable subset L so that on L the weak topology coincides with the norm one. It follows from known results that the RNP and the KMP are equivalent properties on the subsets of C({\omega}^{\omega} ^k).