Miracle-dz/UniFAR-FTUs huggingface dataset:
Homogeneous Open Quantum Walks on the Line: Criteria for Site Recurrence and Absorption

In this work, we study open quantum random walks, as described by S. Attal et al. These objects are given in terms of completely positive maps acting on trace-class operators, leading to one of the simplest open quantum versions of the recurrence problem for classical, discrete-time random walks. This work focuses on obtaining criteria for site recurrence of nearest-neighbor, homogeneous walks on the integer line, with the description presented here making use of recent results of the theory of open walks, most particularly regarding reducibility properties of the operators involved. This allows us to obtain a complete criterion for site recurrence in the case for which the internal degree of freedom of each site (coin space) is of dimension 2. We also present the analogous result for irreducible walks with an internal degree of arbitrary finite dimension and the absorption problem for walks on the semi-infinite line.
Fairness in Influence Maximization through Randomization

The influence maximization paradigm has been used by researchers in various fields in order to study how information spreads in social networks. While previously the attention was mostly on efficiency, more recently fairness issues have been taken into account in this scope. In the present paper, we propose to use randomization as a mean for achieving fairness. While this general idea is not new, it has not been applied in this area. Similar to previous works like Fish et al. (WWW ’19) and Tsang et al. (IJCAI ’19), we study the maximin criterion for (group) fairness. In contrast to their work however, we model the problem in such a way that, when choosing the seed sets, probabilistic strategies are possible rather than only deterministic ones. We introduce two different variants of this probabilistic problem, one that entails probabilistic strategies over nodes (node-based problem) and a second one that entails probabilistic strategies over sets of nodes (set-based problem). After analyzing the relation between the two probabilistic problems, we show that, while the original deterministic maximin problem was inapproximable, both probabilistic variants permit approximation algorithms that achieve a constant multiplicative factor of 1 − 1/e minus an additive arbitrarily small error that is due to the simulation of the information spread. For the node-based problem, the approximation is achieved by observing that a polynomial-sized linear program approximates the problem well. For the set-based problem, we show that a multiplicative-weight routine can yield the approximation result. For an experimental study, we provide implementations of multiplicative-weight routines for both the set-based and the node-based problems and compare the achieved fairness values to existing methods. Maybe non-surprisingly, we show that the ex-ante values, i.e., minimum expected value of an individual (or group) to obtain the information, of the computed probabilistic strategies are significantly larger than the (ex-post) fairness values of previous methods. This indicates that studying fairness via randomization is a worthwhile path to follow. Interestingly and maybe more surprisingly, we observe that even the ex-post fairness values, i.e., fairness values of sets sampled according to the probabilistic strategies computed by our routines, dominate over the fairness achieved by previous methods on many of the instances tested.
Evaluating the Impact of Caching on the Energy Consumption and Performance of Progressive Web Apps

Context: Since today mobile devices have limited battery life, the energy consumption of the software running on them can play a strong role with respect to the success of mobile-based businesses. Progressive Web Applications (PWAs) are built using common web technologies like HTML, CSS, and JavaScript and are commonly used for providing a better user experience to mobile users. Caching is the main technique used by PWA developers for optimizing network usage and for providing a meaningful experience even when the user’s device is offline.Goal: This paper aims at assessing the impact of caching on both the energy consumption and performance of PWAs.Method: We conducted an empirical experiment targeting 9 real PWAs developed by third-party developers. The experiment is designed as a 1 factor - 2 treatments study, with the usage of caching as the single factor and the status of the cache as treatments (empty vs populated cache). The response variables of the experiment are (i) the energy consumption of the mobile device and (ii) the page load time of the PWAs. The experiment is executed on a real Android device running the Mozilla Firefox browser.Results: Our results show that PWAs do not consume significantly different amounts of energy when loaded either with an empty or populated cache. However, the page load time of PWAs is significantly lower when the cache is already populated, with a medium effect size.Conclusions: This study confirms that PWAs are promising in terms of energy consumption and provides evidence that caching can be safely exploited by PWA developers concerned with energy consumption. The study provides also empirical evidence that caching is an effective technique for improving the user experience in terms of page loading time of PWAs.
Modeling Human Driving Behavior in Highway Scenario using Inverse Reinforcement Learning

Human driving behavior modeling is of great importance for designing safe, smart, smooth as well as personalized autonomous driving systems. In this paper, an internal reward function-based driving model that emulates the human's internal decision-making mechanism is proposed. Besides, a sampling-based inverse reinforcement learning (IRL) algorithm that learns the reward function from human naturalistic driving data is also developed. A polynomial trajectory sampler is adopted to generate feasible trajectories and approximate the partition function in the maximum entropy IRL framework, and a dynamic and interactive environment is built upon the static driving dataset to estimate the generated trajectories considering the mutual dependency of agents' actions. The proposed method is applied to learn personalized reward functions for individual human drivers from the NGSIM dataset. The qualitative results demonstrate that the learned reward function is able to interpret their decisions. The quantitative results also reveal that the personalized modeling method significantly outperforms the general modeling approach, reducing the errors in human likeness by 24%, and the proposed reward-function-based method delivers better results compared to other baseline methods. Moreover, it is found that estimating the response actions of surrounding vehicles plays an integral role in estimating the trajectory accurately and achieving a better generalization ability.
Chaos on compact manifolds: Differentiable synchronizations beyond Takens.

This Letter shows that a large class of fading memory state-space systems driven by discrete-time observations of dynamical systems defined on compact manifolds always yields continuously differentiable synchronizations. This general result provides a powerful tool for the representation, reconstruction, and forecasting of chaotic attractors. It also improves previous statements in the literature for differentiable generalized synchronizations, whose existence was so far guaranteed for a restricted family of systems and was detected using H\"older exponent-based criteria.
Space-Time Adaptive Detection at Low Sample Support

An important problem in space-time adaptive detection is the estimation of the large <inline-formula><tex-math notation="LaTeX">$p\times p$</tex-math></inline-formula> interference covariance matrix from training signals. When the number of training signals <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is greater than <inline-formula><tex-math notation="LaTeX">$2p$</tex-math></inline-formula>, existing estimators are generally considered to be adequate, as demonstrated by fixed-dimensional asymptotics. But in the low-sample-support regime (<inline-formula><tex-math notation="LaTeX">$n < 2p$</tex-math></inline-formula> or even <inline-formula><tex-math notation="LaTeX">$n < p$</tex-math></inline-formula>), fixed-dimensional asymptotics are no longer applicable. The remedy undertaken in this paper is to consider the “large dimensional limit” in which <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> go to infinity together. In this asymptotic regime, a new type of estimator is defined (Definition <xref ref-type="definition" rid="definition3">2</xref>), shown to exist (Theorem 1), and shown to be detection-theoretically ideal (Theorem 2). Further, asymptotic conditional detection and false-alarm rates of filters formed from this type of estimator are characterized (Theorems 3 and 4) and shown to depend only on data that is given, even for non-Gaussian interference statistics. The paper concludes with several Monte Carlo simulations that compare the performance of the estimator in Theorem 1 to the predictions of Theorems 2-4, showing in particular higher detection probability than Steiner and Gerlach's Fast Maximum Likelihood estimator.
Burden of Obstructive Lung Disease in Iran: Prevalence and Risk Factors for COPD in North of Iran

Background: Globally chronic obstructive pulmonary disease (COPD) was reported as the fourth leading cause of death (5.1%) in 2004 and is projected to occupy the third position (8.6%) in 2030. The goal of the present project is to describe the prevalence and risk factors of COPD in a province in the north of Iran. Methods: This study followed a stratified cluster sampling strategy with proportional allocation within strata. The stratification of the sample according to the 31 provinces of Iran is incorporated in the sampling process. The single most important outcome measure obtained as part of this protocol was spirometry before and after the administration of 200 mg (2 puffs) of salbutamol. The descriptive statistics for categorical variables included the number and percent and for continues variables included the mean ± SD. Results: A total of 1007 subjects were included in the study. Among all participants, 46 (5%) subjects had COPD on the basis of symptoms and 43 (8.3%) subjects had COPD on the basis of spirometry criteria. In univariate analysis, urban inhabitants in comparison with rural inhabitants had lower COPD risk (OR: 0.48; 95% CI: 0.24–0.95), smoker had higher risk compared with nonsmokers (OR: 1.97; 95% CI: 1.01–3.82), and subjects with exposure to dust (OR: 2.07; 95% CI: 1.09–3.94) had higher risk compared with contrary status. Conclusions: This study showed that occupational and environmental smoke exposure was associated with COPD. A new design of preventive measures must be taken to control cooking energy and cooking stoves, particularly in rural areas.
Implications of Subject Matter Expertise as a Requirement for Debriefing: A Randomized Control Trial

Introduction. Healthcare certification standards do not mention subject matter expertise. This research answers the question: Is there a difference in participant perception of simulation effectiveness between simulation facilitators and SME simulation facilitators during the debrief? Methods. Senior-level nursing students (N=120) were randomized into groups to complete one pediatric critical care simulation. Simulation-based education (SBE) was delivered across five weeks. Two experienced facilitators, one with pediatric expertise and one without, used the PEARLS method to debrief each simulation. Following each debrief, participants completed a demographic survey and a SET-M. Results were analyzed using SPSS Version 24. Results. Eighty-four students completed the survey and SET-M instrument. Independent samples t-tests showed no significant difference between facilitators based on the SET-M total scores (p=.516), or mean scores (p=.492). Debriefing-specific items also showed no difference (p=.983). Conclusion. Subject matter expertise did not change the perception of simulation effectiveness for undergraduate nursing students during a critical care scenario. While SMEs are valuable for scenario creation and coaching non-SME facilitators on key elements for guided reflection, they may not be required to facilitate the debrief. Further research in this area is needed.
Double-expressor and triple-expressor lymphomas: are these prognostically distinct groups of diffuse large B-cell lymphoma?

Background Diffuse large B-cell lymphomas (DLBCLs) are a heterogeneous group with variable prognosis. Double-expressor (DE) and triple-expressor (TE) lymphomas have emerged as uncommon indistinct subgroups of high-grade B-cell lymphomas that may have prognostic value. This study aimed at evaluating the role of immunohistochemistry (IHC) in the detection of DE and TE lymphomas as well as correlating them with clinicopathological parameters. Materials and methods This is a retrospective study conducted on 100 cases of DLBCL. Tissue microarray blocks were constructed and immunostained with antibodies for MYC, BCL2, BCL6, CD10, MUM1, and P53 to determine the germinal center, activated B cell, DE, and TE phenotypes. Results We have found a statistically significant correlation between both DE and TE lymphomas and reduced disease-free survival (P=0.03) as well as with nodal location (P=0.006). No statistically significant correlation was found with clinicopathological parameters such as age, sex, stage, international prognostic index score, or response to therapy. No statistically significant difference was found between DE and TE groups. Conclusion DE and TE lymphomas can be considered as poor prognostic subgroups of DLBCL associated with reduced disease-free survival. Our study stresses the value of immunohistochemistry in the detection of such adverse groups of DLBCL.
A Linguistic Analysis of Visually Grounded Dialogues Based on Spatial Expressions

Recent models achieve promising results in visually grounded dialogues. However, existing datasets often contain undesirable biases and lack sophisticated linguistic analyses, which make it difficult to understand how well current models recognize their precise linguistic structures. To address this problem, we make two design choices: first, we focus on OneCommon Corpus (CITATION), a simple yet challenging common grounding dataset which contains minimal bias by design. Second, we analyze their linguistic structures based on spatial expressions and provide comprehensive and reliable annotation for 600 dialogues. We show that our annotation captures important linguistic structures including predicate-argument structure, modification and ellipsis. In our experiments, we assess the model’s understanding of these structures through reference resolution. We demonstrate that our annotation can reveal both the strengths and weaknesses of baseline models in essential levels of detail. Overall, we propose a novel framework and resource for investigating fine-grained language understanding in visually grounded dialogues.
Qubit-efficient entanglement spectroscopy using qubit resets

One strategy to fit larger problems on NISQ devices is to exploit a tradeoff between circuit width and circuit depth. Unfortunately, this tradeoff still limits the size of tractable problems since the increased depth is often not realizable before noise dominates. Here, we develop qubit-efficient quantum algorithms for entanglement spectroscopy which avoid this tradeoff. In particular, we develop algorithms for computing the trace of the n-th power of the density operator of a quantum system, Tr(ρn), (related to the Rényi entropy of order n) that use fewer qubits than any previous efficient algorithm while achieving similar performance in the presence of noise, thus enabling spectroscopy of larger quantum systems on NISQ devices. Our algorithms, which require a number of qubits independent of n, are variants of previous algorithms with width proportional to n, an asymptotic difference. The crucial ingredient in these new algorithms is the ability to measure and reinitialize subsets of qubits in the course of the computation, allowing us to reuse qubits and increase the circuit depth without suffering the usual noisy consequences. We also introduce the notion of effective circuit depth as a generalization of standard circuit depth suitable for circuits with qubit resets. This tool helps explain the noise-resilience of our qubit-efficient algorithms and should aid in designing future algorithms. We perform numerical simulations to compare our algorithms to the original variants and show they perform similarly when subjected to noise. Additionally, we experimentally implement one of our qubit-efficient algorithms on the Honeywell System Model H0, estimating Tr(ρn) for larger n than possible with previous algorithms.
Theedhum Nandrum@Dravidian-CodeMix-FIRE2020: A Sentiment Polarity Classifier for YouTube Comments with Code-switching between Tamil Malayalam and English

Theedhum Nandrum is a sentiment polarity detection system using two approaches--a Stochastic Gradient Descent (SGD) based classifier and a Long Short-term Memory (LSTM) based Classifier. Our approach utilises language features like use of emoji, choice of scripts and code mixing which appeared quite marked in the datasets specified for the Dravidian Codemix - FIRE 2020 task. The hyperparameters for the SGD were tuned using GridSearchCV. Our system was ranked 4th in Tamil-English with a weighted average F1 score of 0.62 and 9th in Malayalam-English with a score of 0.65. We achieved a weighted average F1 score of 0.77 for Tamil-English using a Logistic Regression based model after the task deadline. This performance betters the top ranked classifier on this dataset by a wide margin. Our use of language-specific Soundex to harmonise the spelling variants in code-mixed data appears to be a novel application of Soundex. Our complete code is published in github at this https URL.
Decoherence and the puzzle of quantum Brownian motion in a gas

Heinz-Dieter Zeh's discovery that the motion of macroscopic objects can not, under typical conditions, follow the Schr\"odinger equation necessitates a suitably modified dynamics. This unfolded a long-lasting puzzle in the open quantum system context: what is the quantum counterpart of the classical Brownian motion in a gas. Presented is a criticism and an open-end discussion of the quantum linear Boltzmann and quantum Fokker-Planck equations -- with constant respect for foundational research.
Abnormally Shallow Boundary Layer Associated With Severe Air Pollution During the COVID‐19 Lockdown in China

After the 2020 Lunar New Year, the Chinese government implemented a strict nationwide lockdown to inhibit the spread of the Coronavirus Disease 2019 (COVID‐19). Despite the abrupt decreases in gaseous emissions caused by record‐low anthropogenic activities, severe haze pollution occurred in northern China during the COVID lockdown. This paradox has attracted the attention of both the public and the scientific community. By analyzing comprehensive measurements of air pollutants, planetary boundary layer (PBL) height, and surface meteorology, we show that the severe air pollution episode over northern China coincided with the abnormally low PBL height, which had reduced by 45%, triggering strong aerosol‐PBL interactions. After dynamical processes initiated the temperature inversion, the Beijing metropolitan area experienced a period with continuously shallow PBLs during the lockdown. This unprecedented event provided an experiment showcasing the role of meteorology, in particular aerosol‐PBL interactions in affecting air quality.
Recognizing (Unit) Interval Graphs by Zigzag Graph Searches

Corneil, Olariu, and Stewart [SODA 1998; SIAM Journal on Discrete Mathematics 2009] presented a recognition algorithm for interval graphs by six graph searches. Li and Wu [Discrete Mathematics \& Theoretical Computer Science 2014] simplified it to only four. The great simplicity of the latter algorithm is however eclipsed by the complicated and long proofs. The main purpose of this paper is to present a new and significantly short proof for Li and Wu's algorithm, as well as a simpler implementation. We also give a self-contained simpler interpretation of the recognition algorithm of Corneil [Discrete Applied Mathematics 2004] for unit interval graphs, based on three sweeps of graph searches. Moreover, we show that two sweeps are already sufficient. Toward the proofs of the main results, we make several new structural observations that might be of independent interests.
Tensor completion-based 5G positioning with partial channel measurements

5G mmWave communication systems have promising properties for high precision user localization and environment mapping. Such information is of high value for emerging applications such as connected automated driving (CAD), and it has also potential to be explored to substantially improve efficiency and reliability of mmWave communications itself. However, the acquisition of such information cannot come with too large overhead in the system. Existing studies have so far relied on complete channel measurements, implying a prohibitive channel training overhead. In this paper, we exploit the possibility of 5G positioning using partial channel measurements. We utilize a tensor completion technique to recover the complete channel information from low rank channel measurements. Simulation results demonstrate the trade-off between user positioning accuracy and channel training overhead, and show that sub-meter precision with negligible performance loss is feasible at sample ratio reductions of at least 30%, and meter level precision is achievable with sample ratio reduction of 50%.
Learning a Cost-Effective Annotation Policy for Question Answering

State-of-the-art question answering (QA) relies upon large amounts of training data for which labeling is time consuming and thus expensive. For this reason, customizing QA systems is challenging. As a remedy, we propose a novel framework for annotating QA datasets that entails learning a cost-effective annotation policy and a semi-supervised annotation scheme. The latter reduces the human effort: it leverages the underlying QA system to suggest potential candidate annotations. Human annotators then simply provide binary feedback on these candidates. Our system is designed such that past annotations continuously improve the future performance and thus overall annotation cost. To the best of our knowledge, this is the first paper to address the problem of annotating questions with minimal annotation cost. We compare our framework against traditional manual annotations in an extensive set of experiments. We find that our approach can reduce up to 21.1% of the annotation cost.