Miracle-dz/UniFAR-FTUs huggingface dataset:
The Gran Sasso muon puzzle

We carry out a time-series analysis of the combined data from three experiments measuring the cosmic muon flux at the Gran Sasso laboratory, at a depth of 3800 m.w. e. These data, taken by the MACRO, LVD and Borexino experiments, span a period of over 20 years, and correspond to muons with a threshold energy, at sea level, of around 1.3 TeV. We compare the best-fit period and phase of the full muon data set with the combined DAMA/NaI and DAMA/LIBRA data, which spans the same time period, as a test of the hypothesis that the cosmic ray muon flux is responsible for the annual modulation detected by DAMA. We find in the muon data a large-amplitude fluctuation with a period of around one year, and a phase that is incompatible with that of the DAMA modulation at 5. 2σ. Aside from this annual variation, the muon data also contains a further significant modulation with a period between 10 and 11 years and a power well above the 99.9% C.L threshold for noise, whose phase corresponds well with the solar cycle: a surprising observation for such high energy muons. We do not see this same period in the stratospheric temperature data.
GAz: a genetic algorithm for photometric redshift estimation

We present a new approach to the problem of estimating the redshift of galaxies from photometric data. The approach uses a genetic algorithm combined with non-linear regression to model the 2SLAQ LRG data set with SDSS DR7 photometry. The genetic algorithm explores the very large space of high order polynomials while only requiring optimisation of a small number of terms. We find a $\sigma_{\text{rms}}=0.0408\pm 0.0006$ for redshifts in the range $0.4<z< 0.7$. These results are competitive with the current state-of-the-art but can be presented simply as a polynomial which does not require the user to run any code. We demonstrate that the method generalises well to other data sets and redshift ranges by testing it on SDSS DR11 and on simulated data. For other datasets or applications the code has been made available at this https URL
AMR-based Path Aggregation Graph Network for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is 001 a fine-grained sentiment classification task. 002 Many recent works have used dependency trees 003 to extract the relationship between aspects and 004 contexts and have achieved significant improve-005 ments. However, further improvement is lim-006 ited due to the mismatch between the depen-007 dency tree as a syntactic structure and the sen-008 timent classification as a semantic task. To 009 alleviate this gap, we replace the syntactic de-010 pendency tree with the semantic structure, Ab-011 stract Meaning Representation (AMR) and pro-012 pose a model called AMR-based Path Aggre-013 gation Graph Network (APAGN). Particularly, 014 we design a path aggregation module which 015 collect local information into global informa-016 tion by path to make full use of AMR. APAGN 017 also contains the outer product summary mod-018 ule which transfers the feature from sentence 019 to graph and the relation-enhanced attention 020 mechanism which transfers the feature in the 021 opposite direction. Experimental results on 022 three public datasets demonstrate the effective-023 ness of APAGN in aspect-based sentiment anal-024 ysis when compared with baselines. 1 025
N URSING H OME S TAFFING D ISPARITIES WERE E XACERBATED DURING THE COVID-19 P ANDEMIC IN 2020

Nursing homes are an integral part of the health care system and provide long-term services and supports and short-term post-acute care to almost 4 million Americans each year.1 Adequate staffing is a prerequisite to providing quality care. Staffing is an important predictor of nursing home quality, and the mix of professional staff and staffing stability are important contributors to quality.2-6 Nursing homes rely on approximately 1.2 million health care practitioners and health care support workers,1 with most of the hands-on care provided by certified nursing assistants (CNAs). Even before the COVID-19 pandemic, nursing homes often failed to maintain adequate staffing.7 An important reason for this difficulty is the low wages, limited possibilities for advancement, and difficult working conditions, particularly for CNAs.8
Seq2rel: A sequence-to-sequence-based approach for document-level relation extraction Anonymous ACL Rolling Review submission

Motivated by the fact that many relations cross the sentence boundary, there has been increasing interest in document-level relation extraction (RE). Document-level RE requires integrating information within and across sentences, capturing complex interactions be-tween mentions of interacting entities. Most document-level RE methods proposed to date are pipeline-based, requiring entities as input. However, previous work has demonstrated that jointly learning to extract entities and relations can improve performance and be more efﬁ-cient due to shared parameters and training steps. In this paper, we develop a sequence-to-sequence-based approach that can learn the sub-tasks of document-level RE — entity extraction, coreference resolution and relation extraction — in an end-to-end fashion. We evaluate our approach on several datasets, in some cases exceeding the performance of existing methods. Finally, we demonstrate that, under our model, the end-to-end approach out-performs a pipeline-based approach. 1
DIRECT DETECTIONS OF YOUNG STARS IN NEARBY ELLIPTICAL GALAXIES

Small amounts of star formation in elliptical galaxies are suggested by several results: surprisingly young ages from optical line indices, cooling X-ray gas, and mid-infrared dust emission. Such star formation has previously been difficult to directly detect, but using ultraviolet Hubble Space Telescope Wide Field Camera 3 imaging, we have identified individual young stars and star clusters in four nearby ellipticals. Ongoing star formation is detected in all galaxies, including three ellipticals that have previously exhibited potential signposts of star-forming conditions (NGC 4636, NGC 4697, and NGC 4374), as well as the typical “red and dead” NGC 3379. The current star formation in our closest targets, where we are most complete, is between 2.0 and 9.8 × 10−5 M☉ yr−1. The star formation history was roughly constant from 0.5 to 1.5 Gyr (at (3–5) × 10−4 M☉ yr−1), but decreased by a factor of several in the past 0.3 Gyr. Most star clusters have a mass between 102 and 104 M☉. The specific star formation rates of ∼10−16 yr−1 (at the present day) or ∼10−14 yr−1 (when averaging over the past Gyr) imply that a fraction 10−8 of the stellar mass is younger than 100 Myr and 10−5 is younger than 1 Gyr, quantifying the level of frosting of recent star formation over the otherwise passive stellar population. There is no obvious correlation between either the presence or spatial distribution of postulated star formation indicators and the star formation we detect.
Occurrence of Aspergillus flavus and Aspergillus parasiticus on Stored Maize in Morogoro municipality and Makambako district, Tanzania Archives of Ecotoxicology

Maize is an important cash and food crop grown in Tanzania and other parts of the world. The crop is susceptible to fungal infestation and subsequent mycotoxins contamination that negatively affects human and animal health as well as the socio-economic status of the farmers and respective stakeholders. The study was conducted to determine occurrence of aflatoxigenic fungi ( A. flavus and A. parasiticus ) on maize stored in warehouses found in Morogoro municipality and Makambako district. A total of 226 maize samples were collected from six wards in the selected study areas then analysed for aflatoxigenic fungal infestation. Potato Dextrose Agar (PDA) was used for isolation of the fungi which were then identified to species level via observation of morphological characteristics with aid of taxonomic keys. The proportional occurrence of A. parasiticus and A. flavus with respect to areas where samples were collected was presented using descriptive statistics. The result showed that only 15% (34/226) of the collected samples were contaminated with Aspergillus flavus and Aspergillus parasiticus . In the collected samples, the incidence of A. flavus was higher than that of A. parasiticus whereby 28 samples were contaminated with A. flavus and 6 samples with A. parasiticus . The study elucidated that the methods used in the storage of this major staple food used by majority of Tanzanians makes it vulnerable to infestation and subsequent contamination by aflatoxigenic fungi. We therefore recommend that responsible sectors should implement appropriate intervention strategies designed to reduce occurrence of aflatoxigenic Aspergillus species on stored maize.
Urban Morphometrics: Towards a Science of Urban Evolution

Urban theorists, social reformists and philosophers have considered the city as a living organism since Plato. However, despite extraordinary advancements in evolutionary biology, now being used to explain social and cultural phenomena, a proper science of evolution in cities has never been established since Geddes' work at the dawn of the Town Planning discipline. Commencing in the tradition of Urban Morphology, this research develops and validates a statistically reliable and universally applicable urban taxonomy. The research solidifies existing definitions of built form at the scale of the urban fabric and identifies the constituent elements of form in 40 contemporary UK cities. Quantifiable measurements of these elements allow mathematical descriptions of their organization and mutual relationships. Further, an optimized list of indices with maximum discriminatory potential distinguishes between cases from four historically characterised categories: 1) Historical, 2) Industrial, 3) New Towns, 4) Sprawl. Finally, a dendrogram is produced that shows the tree of similarity between cases, where the great divide between pre and post WWII war urban form is demonstrated. This work shows that: a) it is conceptually sound and viable to measure urban fabric utilizing public, big-data repositories, b) the proposed urban morphometrics system accurately characterises the structure of urban form and clusters cases properly based on their historical origins, c) scientific models of biological evolution can be applied to urban analysis to understand underlying structural similarities.
Nexus between Fiscal Discipline and the Budget Process in Africa: Evidence from Nigeria

This paper examined the nexus between fiscal discipline and the budget process in Nigeria over the period from 1990 to 2020. Findings showed that the level of fiscal discipline in Nigeria as measured by two proxies of fiscal deficit gap and public debt gap is more enhanced under zero-based budgeting than under current incremental budgeting system. The study also established that civilian administrations are more prone to fiscal indiscipline relative to military dispensations. The paper also revealed the significant role of net foreign aid receipts in significantly narrowing fiscal deficit and public debt gaps in the short-run and long-run, respectively, as well as noted the significant widening impact of an increasing government size on the public debt gap in the long run. The study recommends, among others, the urgent need for the Nigerian government to restore fiscal discipline in public affairs through a reversion to zero-based budgeting system.
GSparsity: Unifying Network Pruning and Neural Architecture Search by Group Sparsity

In this paper, we propose a unified approach for network pruning and one-shot neural architecture search (NAS) via group sparsity. We first show that group sparsity via the recent Proximal Stochastic Gradient Descent (ProxSGD) algorithm achieves new state-of-the-art re-sults for filter pruning. Then, we extend this approach to operation pruning , directly yielding a gradient-based NAS method based on group sparsity. Compared to existing gradient-based algorithms such as DARTS, the advantages of this new group sparsity approach are threefold. Firstly, instead of a costly bilevel optimization problem, we formulate the NAS problem as a single-level optimization problem, which can be optimally and efficiently solved using ProxSGD with convergence guarantees. Secondly, due to the operation-level sparsity, discretizing the network architecture by pruning less important operations can be safely done without any performance degradation. Thirdly, the proposed approach finds architectures that are both stable and performant on a variety of search spaces and datasets.
A three-dimensional extinction map of the Galactic anticentre from multiband photometry

We present a three-dimensional extinction map in the r band. The map has a spatial angular resolution, depending on latitude, between 3 and 9 arcmin and covers the entire Xuyi Schmidt Telescope Photometric Survey of the Galactic Anticentre (XSTPS-GAC) survey area of over 6000 deg(2) for Galactic longitude 140 < l < 240 deg and latitude -60 < b < 40 deg. By cross-matching the photometric catalogue of the XSTPS-GAC with those of 2MASS and WISE, we have built a multiband photometric stellar sample of about 30 million stars and applied spectral energy distribution (SED) fitting to the sample. By combining photometric data from the optical to the near-infrared, we are able to break the degeneracy between the intrinsic stellar colours and the amounts of extinction by dust grains for stars with high photometric accuracy, and trace the extinction as a function of distance for low Galactic latitude and thus highly extincted regions. This has allowed us to derive the best-fitting extinction and distance information of more than 13 million stars, which are used to construct the three-dimensional extinction map. We have also applied a Rayleigh-Jeans colour excess (RJCE) method to the data using the 2MASS and WISE colour (H - W2). The resulting RJCE extinction map is consistent with the integrated two-dimensional map deduced using the best-fitting SED algorithm. However for individual stars, the amounts of extinction yielded by the RJCE method suffer from larger errors than those given by the best-fitting SED algorithm.
The Comparison of Mutational Progression in SARS-CoV-2: A Short Updated Overview

The COVID-19 pandemic has impacted the world population adversely, posing a threat to human health. In the past few years, various strains of SARS-CoV-2, each with different mutations in its structure, have impacted human health in negative ways. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations influence the virulence, antibody evasion, and Angiotensin-converting enzyme 2 (ACE2) affinity of the virus. These mutations are essential to understanding how a new strain of SARS-CoV-2 has changed and its possible effects on the human body. This review provides an insight into the spike mutations of SARS-CoV-2 variants. As the current scientific data offer a scattered outlook on the various type of mutations, we aimed to categorize the mutations of Beta (B.1.351), Gamma (P.1), Delta (B.1.612.2), and Omicron (B.1.1.529) systematically according to their location in the subunit 1 (S1) and subunit 2 (S2) domains and summarized their consequences as a result. We also compared the miscellany of mutations that have emerged in all four variants to date. The comparison shows that mutations such as D614G and N501Y have emerged in all four variants of concern and that all four variants have multiple mutations within the N-terminal domain (NTD), as in the case of the Delta variant. Other mutations are scattered in the receptor binding domain (RBD) and subdomain 2 (SD2) of the S1 domain. Mutations in RBD or NTD are often associated with antibody evasion. Few mutations lie in the S2 domain in the Beta, Gamma, and Delta variants. However, in the Omicron variant many mutations occupy the S2 domain, hinting towards a much more evasive virus.
Automatic Detection of Conﬂict Escalation in Spoken Conversation

This paper investigates the automatic recognition of conﬂict escalations during spontaneous conversations. In our previous work, we studied if the level of conﬂict in a segment of conversation can be automatically inferred by means of prosodic and conversational features. This work investigates the possi-bility of automatically recognizing if the conﬂict is increasing, i.e., escalating, or not. The dataset used for the study consists of political debates where short clips are classiﬁed into escalation, de-escalation and constant labels. Results show a Weighted Accuracy (WA) equals to 69.6% and an Unweighted Accuracy (UA) equals to 49.5% thus revealing lower accuracies compared to the simple conﬂict detection task (WA 86.1%, UA 71.1%). While the task appears more difﬁcult compared to conﬂict detection, results are signiﬁcantly better than chance level showing the feasibility of this approach. Furthermore, the paper investigates the use of a speaker diarization algorithm to extract features in a completely automatic fashion highlighting some limitations of diarization system.
Dengue Incidence and Aedes Vector Collections in Relation to COVID-19 Population Mobility Restrictions

Contrary to expectation, dengue incidence decreased in many countries during the period when stringent population movement restrictions were imposed to combat COVID-19. Using a seasonal autoregressive integrated moving average model, we previously reported a 74% reduction in the predicted number of dengue cases from March 2020 to April 2021 in the whole of Sri Lanka, with reductions occurring in all 25 districts in the country. The reduction in dengue incidence was accompanied by an 87% reduction in larval collections of Aedes vectors in the northern city of Jaffna. It was proposed that movement restrictions led to reduced human contact and blood feeding by Aedes vectors, accompanied by decreased oviposition and vector densities, which were responsible for diminished dengue transmission. These findings are extended in the present study by investigating the relationship between dengue incidence, population movement restrictions, and vector larval collections between May 2021 and July 2022, when movement restrictions began to be lifted, with their complete removal in November 2021. The new findings further support our previous proposal that population movement restrictions imposed during the COVID-19 pandemic reduced dengue transmission primarily by influencing human–Aedes vector interaction dynamics.
Revisiting the EKC hypothesis in South Asia: The role of Export Quality Improvement

Global climate change adversities have particularly sparked the urgency in mitigating carbon dioxide emissions across the world. Against this backdrop, the paper attempts to investigate the validity of the carbon dioxide emission-induced Environmental Kuznets Curve (EKC) hypothesis controlling for the impacts of export quality on the economic growth-carbon dioxide emission nexus in the context of selected South Asian economies: Bangladesh, India, Pakistan, Sri Lanka and Nepal. Using annual data from 1972 to 2014, the results from the panel data econometric analyses provide statistical validity to the EKC hypothesis while the country-specific results depict heterogeneity of the findings in this regard. The EKC hypothesis is validated only for Bangladesh and India while in the context of Pakistan the economic growth-carbon dioxide emission nexus portrays a U-shaped association. In contrast, economic growth is found to monotonically decrease carbon dioxide emissions in Sri Lanka and Nepal. Besides, the results from both the panel and time-series analyses suggest that improvement in export quality lead to lower levels of carbon dioxide emissions. Moreover, the statistical significance of the interaction term between economic growth and export quality implies that the overall impacts of economic growth on carbon dioxide emissions are conditional on the quality of the exports. Thus, enhancing the quality of the export products is pertinent with respect to ensuring environmental sustainability across South Asia. The role of Foreign Direct Investments (FDI) inflows on the growth-CO 2 E nexus has also been explored in the existing literature. The relevant studies, along with the aim of investigating the validity of the CO 2 E-induced EKC hypothesis, also focused on the Pollution Haven Hypothesis (PHH) which predicts that rising FDI inflows attribute to higher CO 2 E within the recipient economies. 4 In a corresponding study on 14 selected Latin American countries, Sapkota and Bastola found statistical evidence to validate the existence of the EKC hypothesis while referring FDI inflows to contribute to higher CO 2 E across the concerned economies. Similarly, in the context of 5 South-east Asian nations, Baek concluded FDI inflows to exert adverse impacts on the CO 2 E levels, but no statistical support was found to prove the validity of the EKC hypothesis.
Contrastive Decision Transformers

: Decision Transformers (DT) have drawn upon the success of Trans-1 formers by abstracting Reinforcement Learning as a target-return-conditioned, se-2 quence modeling problem. In our work, we claim that the distribution of DT’s 3 target-returns represents a series of different tasks that agents must learn to han-4 dle. Work in multi-task learning has shown that separating the representations of 5 input data belonging to different tasks can improve performance. We draw from 6 this approach to construct ConDT (Contrastive Decision Transformer). ConDT 7 leverages an enhanced contrastive loss to train a return-dependent transformation 8 of the input embeddings, which we empirically show clusters these embeddings 9 by their return. We find that ConDT significantly outperforms DT in Open-AI 10 Gym domains by 10% and 39% in visually challenging Atari domains. 11
Characterization of Extremely Fresh Biomass Burning Aerosol by Means of Lidar Observations

In this paper, characterization of the optical and microphysical properties of extremely fresh biomass burning aerosol is presented. This work aims to characterize, for the first time to our knowledge, freshly formed smoke particles observed only a few minutes after they were emitted from a nearby forest fire. The smoke particles were detected by combining passive (sun-photometer) and active (Raman lidar) techniques. On 14 August 2021, an EARLINET (European Aerosol Research Lidar Network) multi-wavelength Raman lidar and a co-located AERONET sun-photometer in Potenza, South Italy, observed an extremely fresh smoke plume. The lidar measurements, carried out from 22:27 to 02:16 UTC, revealed a thick biomass burning layer below 2.7 km. The particle depolarization ratio at 532 nm was 0.025, and lidar ratios at 355 and 532 nm were, respectively, 40 and 38 sr. The mean value of the Ångström exponent was 1.5. The derived size distribution was bimodal with a peak at 0.13 µm, an effective radius mean value of 0.15 µm, and a single scattering albedo of 0.96 at all wavelengths. The real part of the refractive index was 1.58 and the imaginary was 0.006. The AERONET measurements at 5:34 UTC confirmed the lidar measurements.