Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of UAV-Assisted Multiuser Terrestrial-Satellite Communication System Over Mixed FSO/RF Channels

In this letter, performance of a multiantenna multiuser unmanned aerial vehicle (UAV)-assisted terrestrial-satellite communication system over mixed free space optics (FSO)/radiofrequency (RF) channels is analyzed. Downlink transmission from the satellite to the UAV is completed through FSO link which follows gamma–gamma distribution with pointing error impairments. Both the heterodyne detection and intensity modulation direct detection techniques are considered at the FSO receiver. To avail the antenna diversity, multiple transmit antennas are considered at the UAV. Selective decode-and-forward scheme is assumed at the UAV and opportunistic user scheduling is performed while considering the practical constraints of outdated channel state information (CSI) during the user selection and transmission phase. The RF links are assumed to follow Nakagami-m distribution due to its versatile nature. In this context, for the performance analysis, analytical expressions of outage probability, asymptotic outage probability, ergodic capacity, effective capacity, and generalized average symbol-error-rate expressions of various quadrature amplitude modulation (QAM) schemes such as hexagonal-QAM, cross-QAM, and rectangular QAM are derived. A comparison of various modulation schemes is presented. Further, the impact of pointing error, number of antennas, delay constraint, fading severity, and imperfect CSI are highlighted on the system performance. Finally, all the analytical results are verified through the Monte–Carlo simulations.
Coherent states for fractional powers of the harmonic oscillator Hamiltonian

Inspired by special and general relativistic systems that can have Hamiltonians involving square roots, or more general fractional powers, in this article we address the question how a suitable set of coherent states for such systems can be obtained. This becomes a relevant topic if the semiclassical sector of a given quantum theory wants to be analysed. As a simple setup we consider the toy model of a deparametrised system with one constraint that involves a fractional power of the harmonic oscillator Hamiltonian operator and we discuss two approaches for finding suitable coherent states for this system. In the first approach we consider Dirac quantisation and group averaging that have been used by Ashtekar et. al. but only for integer powers of operators. Our generalisation to fractional powers yields in the case of the toy model a suitable set of coherent states. The second approach is inspired by coherent states based on a fractional Poisson distribution introduced by Laskin, which however turn out not to satisfy all properties to yield good semiclassical results for the operators considered here and in particular do not satisfy a resolution of identity as claimed. Therefore, we present a generalisation of the standard harmonic oscillator coherent states to states involving fractional labels, which approximate the fractional operators in our toy model semiclassically more accurately and satisfy a resolution of identity. In addition, motivated by the way the proof of the resolution of identity is performed, we consider these kind of coherent states also for the polymerised harmonic oscillator and discuss their semiclassical properties
Extract, Integrate, Compete: Towards Verification Style Reading Comprehension

In this paper, we present a new verification style reading comprehension dataset named VGaokao from Chinese Language tests of Gaokao. Different from existing efforts, the new dataset is originally designed for native speakers' evaluation, thus requiring more advanced language understanding skills. To address the challenges in VGaokao, we propose a novel Extract-Integrate-Compete approach, which iteratively selects complementary evidence with a novel query updating mechanism and adaptively distills supportive evidence, followed by a pairwise competition to push models to learn the subtle difference among similar text pieces. Experiments show that our methods outperform various baselines on VGaokao with retrieved complementary evidence, while having the merits of efficiency and explainability. Our dataset and code are released for further research.
Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework

Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper we meticulously create a large amount of data connected with E-manuals and develop suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40% in ROUGE-L F1 scores over the most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared at https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website is https://sites.google. com/view/emanualqa/home.
Uncovering Main Causalities for Long-tailed Information Extraction

Information Extraction (IE) aims to extract structural information from unstructured texts. In practice, long-tailed distributions caused by the selection bias of a dataset may lead to incorrect correlations, also known as spurious correlations, between entities and labels in the conventional likelihood models. This motivates us to propose counterfactual IE (CFIE), a novel framework that aims to uncover the main causalities behind data in the view of causal inference. Specifically, 1) we first introduce a unified structural causal model (SCM) for various IE tasks, describing the relationships among variables; 2) with our SCM, we then generate counterfactuals based on an explicit language structure to better calculate the direct causal effect during the inference stage; 3) we further propose a novel debiasing approach to yield more robust predictions. Experiments on three IE tasks across five public datasets show the effectiveness of our CFIE model in mitigating the spurious correlation issues.
BCL2 translocation in high grade B cell lymphoma (NOS, DH/TH) is associated with reduced progression free survival

Abstract High Grade B Cell Lymphoma, NOS, and High Grade B Cell Lymphoma with Dual Hit or Triple Hit have been recently recategorized in the 2016 revision of the WHO classification of lymphoid neoplasms. In this study we have characterized the genetic, histopathological, and clinical features of a series of this type of lymphoid neoplasia (17 HGBCL NOS and 53 HGBCL DH/TH). HGBCL NOS showed better response to first line treatment than HGBCL with DH/TH but no significant differences in PFS or OS were found between the two categories. Survival analysis in the whole cohort of cases found that only the presence of BCL2 translocation was significantly associated with PFS. Other clinical features such as IPI, LDH or stage were equivalent in both categories. Furthermore, both high grade and DLBCL morphological patterns showed equivalent PFS and OS in this set of High grade BCL NOS/DH/TH. Key points BCL2 translocation in High Grade B Cell Lymphoma NOS and High Grade B Cell Lymphoma with DH/TH is associated with reduced progression free survival. Both high grade and DLBCL morphological patterns showed equivalent outcome regarding PFS and OS in HGBCL.
Overexpression of GDP dissociation inhibitor 1 gene associates with the invasiveness and poor outcomes of colorectal cancer

ABSTRACT GDP dissociation inhibitor (GDI) regulates the GDP/GTP exchange reaction of most Rab proteins by inhibiting GDP dissociation. This study evaluated the potential prognostic and predictive value of GDI1 in colorectal cancer (CRC). To address the prognostic power of GDI1, we performed individual and pooled survival analyses on six independent CRC microarray gene expression datasets. GDI1-enriched signatures were also analyzed. Kaplan–Meier and Cox proportional analyses were employed for survival analysis. An immunohistochemistry (IHC) analysis was performed to validate the clinical relevance and prognostic significance of the GDI1 protein level in CRC tissue samples. The results revealed that GDI1 mRNA level was significantly linked with the aggressiveness of CRC, which is compatible with gene set enrichment analysis. A meta-analysis and pooled analysis demonstrated that a higher mRNA GDI1 expression was dramatically correlated with a worse survival in a dose-dependent manner in CRC patients. Further IHC analysis validated that the protein expression of GDI1 in both cytoplasm and membrane also significantly impacted the outcome of CRC patients. In CRC patients with stage III, chemotherapy significantly reduced the relative risk of death in low-GDI1 subgroup (hazard ratio (HR) = 0.22; 95% confidence interval (95% CI) 0.09–0.56, p = 0.0003), but not in high-GDI1 subgroup (HR = 0.63; 95% CI 0.35–1.14, p = 0.1137). Therefore, both high mRNA and protein levels of GDI1 were significantly related to poor outcomes in CRC patients. GD11 may serve as a prognostic biomarker for CRC.
Self-efficacy in Insomnia Symptom Management after Digital CBT-I Mediates Insomnia Severity during the COVID-19 Pandemic

ABSTRACT Study Objectives Digital cognitive behavioral therapy for insomnia (dCBT-I) can reduce acute insomnia and depressive symptoms and prevent symptom recurrence. The current study evaluated self-efficacy in managing insomnia symptoms as a potential mediator of the relationship between prior dCBT-I and subsequent insomnia and depressive symptoms assessed during the coronavirus 2019 (COVID-19) pandemic. Method Participants were 208 adults who completed a randomized controlled trial of dCBT-I versus sleep education in 2016–2017 and also completed self-report assessments of insomnia, depression, and self-efficacy in managing insomnia symptoms. Data were collected in May 2020, five weeks into state-wide COVID-19 stay-at-home orders. Regression and mediation analyses were used to evaluate the extent to which self-efficacy accounted for the relationship between treatment condition and improvement in insomnia and depressive symptoms from pre-treatment to COVID-19 follow-up. Results Prior dCBT-I predicted greater self-efficacy in managing insomnia symptoms. Self-efficacy accounted for 49% and 67% of the protective effect of dCBT-I against COVID-era insomnia and depressive symptoms, respectively. Conclusions This study affirms the importance of self-efficacy as a key intervention outcome and potential mechanism by which dCBT-I predicts future sleep and mental health. Future studies that evaluate the role of self-efficacy in treatment effectiveness and resilience can provide additional clues about how to optimize dCBT-I for maximum benefit to public health.
Intensive care unit professionals during the COVID-19 pandemic in Spain: social and work-related variables, COVID-19 symptoms, worries, and generalized anxiety levels

Background The severe acute respiratory syndrome coronavirus 2 outbreak has been identified as a pandemic and global health emergency. It presents as a severe acute respiratory disease. The rapid dissemination of the disease created challenges for healthcare systems and forced healthcare workers (HCWs) to deal with many clinical and nonclinical stresses. The aim of our research is to describe work conditions, symptoms experienced by HCWs, worries about contagion, and generalized anxiety symptoms and compare those findings across regions in Spain. Methods This cross-sectional study was conducted using an online survey. Critical care units throughout Spain were included. The sample comprised HCWs working in intensive care units from March to May 2020. We assessed work variables, physical symptoms, worries about contagion, and anxiety (generalized anxiety disorder-7 questionnaire). Results The final sample comprised 448 surveys. Among the respondents, 86.9% (n=389) were nursing professionals, and 84.8% (n=380) were women. All participants cared for coronavirus disease 2019 (COVID-19) patients during the study period. Workload during the pandemic in Madrid was judged to be higher than in other regions (P<0.01). The availability of personal protective equipment was found to be higher in Cataluña. The most frequently experienced symptom was headaches (78.1%). Worries about self-infection and the possibility of infecting others received mean scores of 3.11 and 3.75, respectively. Mean scores for generalized anxiety levels were 11.02, with 58.7% of the professionals presenting with generalized anxiety syndrome during the assessment. Conclusions In this study, we found high levels of anxiety among HCWs caring directly for COVID-19 patients, which could produce long-term psychological alterations that still need to be assessed.
MURAL: Multimodal, Multitask Retrieval Across Languages

Both image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages. We use both types of pairs in MURAL (MUltimodal, MUltitask Representations Across Languages), a dual encoder that solves two tasks: 1) image-text matching and 2) translation pair matching. By incorporating billions of translation pairs, MURAL extends ALIGN (Jia et al. PMLR'21)--a state-of-the-art dual encoder learned from 1.8 billion noisy image-text pairs. When using the same encoders, MURAL's performance matches or exceeds ALIGN's cross-modal retrieval performance on well-resourced languages across several datasets. More importantly, it considerably improves performance on under-resourced languages, showing that text-text learning can overcome a paucity of image-caption examples for these languages. On the Wikipedia Image-Text dataset, for example, MURAL-base improves zero-shot mean recall by 8.1% on average for eight under-resourced languages and by 6.8% on average when fine-tuning. We additionally show that MURAL's text representations cluster not only with respect to genealogical connections but also based on areal linguistics, such as the Balkan Sprachbund.
Silver nanoparticles, a promising treatment against clinically important fluconazole‐resistant Candida glabrata

Resistance to azole antifungal agents is a challenging limitation in Candida glabrata treatment. It is associated with decreased intracellular concentrations of antifungal agents as a result of overexpression of efflux pumps on the cellular plasma membranes. This work evaluates the potential of silver nanoparticles (AgNPs) to reverse the resistance of fungal cells to fluconazole. Silver nanoparticles were prepared using wet chemical method and characterised by UV‐Vis spectrophotometry, dynamic light scattering, and zeta potential. Broth microdilution and pour plates methods were used to study the anticandidal activity using two C. glabrata fluconazole‐resistant strains (DSY565 and CBS138) known to overexpress active efflux pumps, and a standard fluconazole sensitive strain ATCC 22553. Silver nanoparticles–fluconazole combinations decreased concentrations of fluconazole substantially without compromising the activity. These findings suggest that AgNPs enhance the efficacy of fluconazole and offer a promising application in therapy of C. glabrata infections.
PAC Mode Estimation using PPR Martingale Confidence Sequences

We consider the problem of correctly identifying the \textit{mode} of a discrete distribution $\mathcal{P}$ with sufficiently high probability by observing a sequence of i.i. d. samples drawn from $\mathcal{P}$. This problem reduces to the estimation of a single parameter when $\mathcal{P}$ has a support set of size $K = 2$. After noting that this special case is tackled very well by prior-posterior-ratio (PPR) martingale confidence sequences \citep{waudby-ramdas-ppr}, we propose a generalisation to mode estimation, in which $\mathcal{P}$ may take $K \geq 2$ values. To begin, we show that the"one-versus-one"principle to generalise from $K = 2$ to $K \geq 2$ classes is more efficient than the"one-versus-rest"alternative. We then prove that our resulting stopping rule, denoted PPR-1v1, is asymptotically optimal (as the mistake probability is taken to $0$). PPR-1v1 is parameter-free and computationally light, and incurs significantly fewer samples than competitors even in the non-asymptotic regime. We demonstrate its gains in two practical applications of sampling: election forecasting and verification of smart contracts in blockchains.
Optimal hypergeometric confidence sets are (almost) always intervals

We present an efficient method of calculating exact confidence intervals for the hypergeometric parameter. The method inverts minimum-width acceptance intervals after shifting them to make their endpoints nondecreasing while preserving their level. The resulting set of confidence intervals achieves minimum possible average width, and even in comparison with confidence sets not required to be intervals it attains the minimum possible cardinality most of the time, and always within 1. The method compares favorably with existing methods not only in the size of the intervals but also in the time required to compute them. The available R package hyperMCI implements the proposed method.
OSS Effort Estimation Using Software Features Similarity and Developer Activity-Based Metrics

Software development effort estimation (SDEE) generally involves leveraging the information about the effort spent in developing similar software in the past. Most organizations do not have access to sufficient and reliable forms of such data from past projects. As such, the existing SDEE methods suffer from low usage and accuracy. We propose an efficient SDEE method for open source software, which provides accurate and fast effort estimates. The significant contributions of our article are (i) novel SDEE software metrics derived from developer activity information of various software repositories, (ii) an SDEE dataset comprising the SDEE metrics’ values derived from approximately 13,000 GitHub repositories from 150 different software categories, and (iii) an effort estimation tool based on SDEE metrics and a software description similarity model. Our software description similarity model is basically a machine learning model trained using the PVA on the software product descriptions of GitHub repositories. Given the software description of a newly envisioned software, our tool yields an effort estimate for developing it. Our method achieves the highest standardized accuracy score of 87.26% (with Cliff’s δ = 0.88 at 99.999% confidence level) and 42.7% with the automatically transformed linear baseline model. Our software artifacts are available at https://doi.org/10.5281/zenodo.5095723.
Searching for axionlike particles from core-collapse supernovae with 
Fermi
 LAT’s low-energy technique

Light axion-like particles (ALPs) are expected to be abundantly produced in core-collapse supernovae (CCSNe), resulting in a ∼10-second long burst of ALPs. These particles subsequently undergo conversion into gamma-rays in external magnetic fields to produce a long gamma-ray burst (GRB) with a characteristic spectrum peaking in the 30–100-MeV energy range. At the same time, CCSNe are invoked as progenitors of ordinary long GRBs, rendering it relevant to conduct a comprehensive search for ALP spectral signatures using the observations of long GRB with the Fermi Large Area Telescope (LAT). We perform a data-driven sensitivity analysis to determine CCSN distances for which a detection of an ALP signal is possible with the LAT’s low-energy (LLE) technique which, in contrast to the standard LAT analysis, allows for a a larger effective area for energies down to 30 MeV. Assuming an ALP mass ma . 10−10 eV and ALP-photon coupling gaγ = 5.3 × 10−12 GeV−1, values considered and deduced in ALP searches from SN1987A, we find that the distance limit ranges from ∼0.5 to ∼10 Mpc, depending on the sky location and the CCSN progenitor mass. Furthermore, we select a candidate sample of twenty-four GRBs and carry out a model comparison analysis in which we consider different GRB spectral models with and without an ALP signal component. We find that the inclusion of an ALP contribution does not result in any statistically significant improvement of the fits to the data. We discuss the statistical method used in our analysis and the underlying physical assumptions, the feasibility of setting upper limits on the ALP-photon coupling, and give an outlook on future telescopes in the context of ALP searches.
Finding and Combining Indicable Subgroups of Big Mapping Class Groups

We explicitly construct new subgroups of the mapping class groups of an uncountable collection of infinite-type surfaces, including, but not limited to, right-angled Artin groups, free groups, Baumslag-Solitar groups, mapping class groups of other surfaces, and a large collection of wreath products. For each such subgroup $H$ and surface $S$, we show that there are countably many non-conjugate embeddings of $H$ into $\text{Map}(S)$; in certain cases, there are uncountably many such embeddings. The images of each of these embeddings cannot lie in the isometry group of $S$ for any hyperbolic metric and are not contained in the closure of the compactly supported subgroup of $\text{Map}(S)$. In this sense, our construction is new and does not rely on previously known techniques for constructing subgroups of mapping class groups. Notably, our embeddings of $\text{Map}(S')$ into $\text{Map}(S)$ are not induced by embeddings of $S'$ into $S$. Our main tool for all of these constructions is the utilization of special homeomorphisms of $S$ called shift maps, and more generally, multipush maps.