Miracle-dz/UniFAR-FTUs huggingface dataset:
What Uncertainties Matter to Cryptocurrencies?

We contribute to financial literature by identifying economic uncertainties salient to the price dynamics of cryptocurrencies. To measure this relationship, we examine the common stochastic trends between cryptocurrencies and major text-based uncertainty indices — categorical and broad — from the US and major developed economies from 2015 to 2021. Results from static factor tests using high-dimensional stochastic volatility factor models indicate trivial associations between global uncertainties and the crypto-sphere. Further investigation from dynamic implied correlation matrices, however, suggest that this phenomenon has undergone several changes over time. In fact, ties between US-based monetary policy uncertainties were non-trivial between 2015 to 2016. Following the post-Trump election bull run, the association faded, before reappearing upon the advent of the COVID-19 pandemic. Uncertainties surrounding some decisions by US government register minor significance. Uncertainties from European countries and China show some influence, with Japan registering an inverse relationship. Causality tests largely approbate these results, while underscoring an important point that while the pricing dynamics of cryptocurrencies may be independent from global uncertainties, their second moments remain attached to global trends. In sum, volatility in the crypto market is impacted by global uncertainties;prices are less so.
Idea Recommendation in Open Innovation Platforms: A Design Science Approach

Collaborative crowdsourcing communities help firms obtain ideas generated by the public at a lower cost compared to those generated in-house. However, the growth of these communities has led to a large influx of ideas of mixed quality, which has made it difficult for firm experts to select and implement ideas. In this paper we propose a novel theoretical framework to (1) extract important features from a collaborative crowdsourcing community and (2) apply them to the practice of recommending ideas that are most likely to be implemented in the future. More specifically, we adopt the design science research paradigm, introduce the knowledge persuasion model as the kernel theory, operate users’ persuasive attempts and firm experts’ persuasive coping, and identify a rich set of features as predictors of the likelihood of idea implementation. We test our prediction framework on a large-scale collaborative crowdsourcing community. The results of our data analysis show that the proposed framework is effective and efficient in predicting the likelihood of idea implementation. To increase the interpretability of the prediction model, we also implement the SHapley Additive exPlanations (SHAP) analysis and discuss the relationships between important features and idea implementation. We conclude by discussing the theoretical and practical implications of these findings.
About Factor Investing: Analysis of This Industry, Financial Literature, Strategies, and Key Trends

Many equity investors looking for higher returns at a lower cost have turned to factor investing. After identifying other risk factors in addition to the market component, alternative portfolio construction techniques were created to compensate for the inefficiencies of market-cap strategies. By analyzing the current literature and examining how well factor strategies performed, this research article seeks to provide a formal foundation for this important theme in asset management.
Challenges in Applying Audio Classiﬁcation Models to Datasets Containing Crucial Biodiversity Information

The acoustic signature of a natural soundscape can reveal consequences of climate change on biodiversity. Hardware costs, human labor time, and expertise dedicated to labeling audio are impediments to conducting acoustic surveys across a representative portion of an ecosystem. These barriers are quickly eroding away with the advent of low-cost, easy to use, open source hardware and the expansion of the machine learning ﬁeld providing pre-trained neural networks to test on retrieved acoustic data. One consistent challenge in passive acoustic monitoring (PAM) is a lack of reliability from neural networks on audio recordings collected in the ﬁeld that contain crucial biodiversity information that otherwise show promising results from publicly available training and test sets. To demonstrate this challenge, we tested a hybrid recurrent neural network (RNN) and convolutional neural network (CNN) binary classiﬁer trained for bird presence/absence on two Peruvian bird audiosets. The RNN achieved an area under the receiver operating characteristics (AUROC) of 95% on a dataset collected from Xeno-canto and Google’s AudioSet ontology in contrast to 65% across a stratiﬁed random sample of ﬁeld recordings collected from the Madre de Dios region of the Peruvian Amazon. In an attempt to alleviate this discrepancy, we applied various audio data augmentation techniques in the network’s training process which led to an AUROC of 77% across the ﬁeld recordings.
An Adaptive Large Neighbourhood Search for Real-Time Waste Collection Inventory Routing with Autonomous Vessels

The city of Amsterdam has recently installed environmental zones to reduce air pollution. The zones prevent diesel engines from entering, which caused a surge of heavy electric garbage trucks in Amsterdam [1]. Unfortunately, these heavy vehicles induce subsidence of the city’s ’grachtengordels’. To prevent this, the city of Amsterdam is exploring other methods of transportation [2]. In [3], Daub presents a solution for the household waste collection, using the autonomous vessels developed by the roboat project to pick-up containers situated at 283 predefined locations 1. Resembling the current pick-up schedule, Amsterdam was divided into sections, with a weekly pick-up day assigned to each section. In addition to household waste, companies in the hospitality sector located near the canals also generate waste. This waste however, is often collected by third party companies [4]. Two consequences of this system are: (i) no data is gathered and (ii) the third party companies continue using the simpler heavier garbage truck. In this paper, we consider the waste generated by hotels along the canals in order to address this. We formulate a real-time waste collection inventory routing problem and propose an efficient Adaptive large Neighbourhood Search (ALNS) algorithm for the solution. In this problem, the vessels have a capacity of three full containers, totalling a maximum weight of one ton. A single depot exists, where the vessels are emptied and recharged. When a vessel arrives to pick-up a container, it is swapped with an empty one. In this system, each container measures the amount of waste it contains and the time since it was last served. Both parameters are then combined into a score, i.e., the score increases with the waste level and with the time since last visit. This score is minimised by the ALNS algorithm to optimise the waste collection problem. In Figure (1), a single vessel’s solution for an arbitrary network is illustrated. In this example, the vessel recently picked up container B, which is now on-
Synchronous Parallelism Between Di(cid:11)erent Grammar Formalisms

Coordination has posed a problem in TAG-related formalisms, with a recent proposal to solve it suggesting that constituency and dependency be treated separately within a synchronously parallel formalism. This leads to `tangled' trees| graphs|and it is not clear how these structures satisfy the de(cid:12)nition of syn-chronised TAGs. Naive alternatives which do not result in tangled trees, however, do not satisfy the weak language preservation property (WLPP). This paper shows how the idea of synchronised parallelisation of grammars can be extended so that grammars of di(cid:11)erent types are synchronised, how this extension has the WLPP, and how this relates to coordination.
Disrupting Human Rights: A Social Work Response to the Lockdown of Social Housing Residents

Abstract The article probes the disproportionate impact on marginalised populations to reduce the spread of COVID-19 (COVID-19 is an acronym that stands for coronavirus disease of 2019). It explores this problematic through research with refugees residing in social housing in Melbourne, Australia. The focus is on the specific pressures facing this cohort with the 2020 deployment, without notice, of armed police to enforce lockdown in the central Melbourne housing high rise tower estates. Our research methodology comprises narrating experiences of a community leader who had direct contact with residents and is a co-author of this article; accounts arising from an African community forum and a review of media sources that are attentive to voice. From a thematic analysis, we found consistency of narrative for a cohort whose voices had previously been excluded from the public domain. The themes were in three key areas: representation and employment of Culturally and Linguistically Diverse social workers and community workers; restoration of human rights to those experiencing state-sanctioned violence; and the application of critical multicultural social work practice. We apply theorising derived from Helen Taylor and Jacques Derrida, and argue that responses to crises should be led by the wisdom of affected communities, in keeping with critical social work theories and practices.
Conversion of Polyethylene Waste into Gaseous Hydrocarbons via Integrated Tandem Chemical–Photo/Electrocatalytic Processes

The chemical inertness of polyethylene makes chemical recycling challenging and motivates the development of new catalytic innovations to mitigate polymer waste. Current chemical recycling methods yield a complex mixture of liquid products, which is challenging to utilize in subsequent processes. Here, we present an oxidative depolymerization step utilizing diluted nitric acid to convert polyethylene into organic acids (40% organic acid yield), which can be coupled to a photo- or electrocatalytic decarboxylation reaction to produce hydrocarbons (individual hydrocarbon yields of 3 and 20%, respectively) with H2 and CO2 as gaseous byproducts. The integrated tandem process allows for the direct conversion of polyethylene into gaseous hydrocarbon products with an overall hydrocarbon yield of 1.0% for the oxidative/photocatalytic route and 7.6% for the oxidative/electrolytic route. The product selectivity is tunable with photocatalysis using TiO2 or carbon nitride, yielding alkanes (ethane and propane), whereas electrocatalysis on carbon electrodes produces alkenes (ethylene and propylene). This two-step recycling process of plastics can use sunlight or renewable electricity to convert polyethylene into valuable, easily separable, gaseous platform chemicals.
Radiographic interpretation using high-resolution Cbct to diagnose degenerative temporomandibular joint disease

The objective of this study was to use high-resolution cone-beam computed images (hr- CBCT) to diagnose degenerative joint disease in asymptomatic and symptomatic subjects using the Diagnostic Criteria for Temporomandibular Disorders DC/TMD imaging criteria. This observational study comprised of 92 subjects age-sex matched and divided into two groups: clinical degenerative joint disease (c-DJD, n = 46) and asymptomatic control group (n = 46). Clinical assessment of the DJD and high-resolution CBCT images (isotropic voxel size of 0.08mm) of the temporomandibular joints were performed for each participant. An American Board of Oral and Maxillofacial Radiology certified radiologist and a maxillofacial radiologist used the DC/TMD imaging criteria to evaluate the radiographic findings, followed by a consensus of the radiographic evaluation. The two radiologists presented a high agreement (Cohen’s Kappa ranging from 0.80 to 0.87) for all radiographic findings (osteophyte, erosion, cysts, flattening, and sclerosis). Five patients from the c- DJD group did not present radiographic findings, being then classified as arthralgia. In the asymptomatic control group, 82.6% of the patients presented radiographic findings determinant of DJD and were then classified as osteoarthrosis or overdiagnosis. In conclusion, our results showed a high number of radiographic findings in the asymptomatic control group, and for this reason, we suggest that there is a need for additional imaging criteria to classify DJD properly in hr-CBCT images.
Induced endothelial cells from peripheral arterial disease patients and neonatal fibroblasts have comparable angiogenic properties

Induced endothelial cells (iECs) generated from neonatal fibroblasts via transdifferentiation have been shown to have pro-angiogenic properties and are a potential therapy for peripheral arterial disease (PAD). It is unknown if iECs can be generated from fibroblasts collected from PAD patients and whether these cells are pro-angiogenic. In this study fibroblasts were collected from four PAD patients undergoing carotid endarterectomies. These cells, and neonatal fibroblasts, were transdifferentiated into iECs using modified mRNA. Endothelial phenotype and pro-angiogenic cytokine secretion were investigated. NOD-SCID mice underwent surgery to induce hindlimb ischaemia in a murine model of PAD. Mice received intramuscular injections with either control vehicle, or 1 × 106 neonatal-derived or 1 × 106 patient-derived iECs. Recovery in perfusion to the affected limb was measured using laser Doppler scanning. Perfusion recovery was enhanced in mice treated with neonatal-derived iECs and in two of the three patient-derived iEC lines investigated in vivo. Patient-derived iECs can be successfully generated from PAD patients and for specific patients display comparable pro-angiogenic properties to neonatal-derived iECs.
Social Work in the Time of COVID-19: A Case Study from the Global South

Abstract This article explores the experiences of social workers at a non-governmental organisation (NGO) involved in disaster responses to COVID-19 in a rural and resource-challenged region of Cambodia. The views of Khmer and international social workers in the NGO were gathered through an internal auditing process utilising survey and structured conversation methods. Key themes related to the importance of prioritising the safety of staff and clients, effective communication methods, responsiveness of case management systems, public health responses and adapting to emerging needs. To ensure responsiveness to future disaster events consideration should be given in strengthening partnerships, ensuring case management systems are effective for current use but able to be adapted in new circumstances, and that preparation incorporates a focus on diversified funding streams and open communication channels between staff and management. These elements will enable social workers to continue their practice, reassured and with the flexibility required in the post-disaster context.
Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care—A meta-analysis

Background Diabetic retinopathy (DR) affects 10–24% of patients with diabetes mellitus type 1 or 2 in the primary care (PC) sector. As early detection is crucial for treatment, deep learning screening methods in PC setting could potentially aid in an accurate and timely diagnosis. Purpose The purpose of this meta-analysis was to determine the current state of knowledge regarding deep learning (DL) screening methods for DR in PC. Data sources A systematic literature search was conducted using Medline, Web of Science, and Scopus to identify suitable studies. Study selection Suitable studies were selected by two researchers independently. Studies assessing DL methods and the suitability of these screening systems (diagnostic parameters such as sensitivity and specificity, information on datasets and setting) in PC were selected. Excluded were studies focusing on lesions, applying conventional diagnostic imaging tools, conducted in secondary or tertiary care, and all publication types other than original research studies on human subjects. Data extraction The following data was extracted from included studies: authors, title, year of publication, objectives, participants, setting, type of intervention/method, reference standard, grading scale, outcome measures, dataset, risk of bias, and performance measures. Data synthesis and conclusion The summed sensitivity of all included studies was 87% and specificity was 90%. Given a prevalence of DR of 10% in patients with DM Type 2 in PC, the negative predictive value is 98% while the positive predictive value is 49%. Limitations Selected studies showed a high variation in sample size and quality and quantity of available data.
A comparison of the efficiency of RNA extraction from extracellular vesicles using the Qiagen RNeasy MinElute versus Enzymax LLC RNA Tini Spin columns and qPCR of miRNA

Abstract One consequence of the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is an interruption to the supply of laboratory consumables, particularly those used for RNA extraction. This category includes column-based RNA extraction kits designed to retain short RNA species (defined as having fewer than 200 nucleotides), from small sample volumes, e.g. exosomes or extracellular vesicles (EVs). Qiagen manufactures several kits for the extraction and enrichment of short RNA species, such as microRNA (miRNA), which contain silica-membrane columns called “RNeasy MinElute Spin Columns. ” These kits, which also contain buffers and collection tubes, range in price from USD380 to greater than USD1000 and have been subject to fulfillment delays. Scientists seeking to reduce single-use plastics and costs may wish to order the columns separately; however, Qiagen does not sell the RNeasy MinElute Spin Columns (in reasonable quantities) as an individual item. Thus, we sought an alternative product and found RNA Tini Spin columns from Enzymax LLC. We conducted a systematic comparison of the efficiency of RNA extraction for miRNA quantitative real-time PCR (qPCR) using the Qiagen RNeasy MinElute Spin Columns and Enzymax LLC RNA Tini Spin columns and the Qiagen total RNA extraction protocol that enriches for short RNA species. We compared the efficiency of extraction of five spike-in control miRNAs, six sample signal miRNAs, and nine low- to medium-abundance miRNAs by qPCR. The RNA was extracted from EV preparations purified from human plasma using CD81 immunoprecipitation. We report no statistically significant differences in extraction efficiencies between the two columns for any of the miRNAs examined. Therefore, we conclude that the Enzymax RNA Tini Spin columns are adequate substitutes for the Qiagen RNeasy MinElute Spin Columns for short RNA species enrichment and downstream qPCR from EVs in the miRNAs we examined.
Optimum rate of nitrogen fertilization for drip-irrigated wheat under semi-arid conditions

ABSTRACT Limited water availability in arid and semi-arid wheat production systems increases the need of applying efficient drip irrigation systems. However, there is little information available about the optimum level of nitrogen (N) fertilization for drip-irrigated wheat. A two-years field study in the semi-arid region of Upper Egypt was carried out in a randomized complete block design to investigate the response of drip-irrigated wheat to three levels of N fertilization (N120 = 120, N180 = 180, and N240 = 240 kg ha−1). N240 increased the uptake of N, P, and K by 66.3, 48.6, and 43.5%, respectively, as compared to N120. The application of N240 increased the grain yield by 28.4 and 40.4% and water use efficiency by 27.6 and 41.8% the first and second season, respectively, as compared to N120. Based on the obtained results, it is recommended to fertilize drip-irrigated wheat by 240 kg ha−1.
The origin of the soft X-ray excess in the narrow-line Seyfert 1 galaxy SBS 1353 + 564

We present for the first time the timing and spectral analyses for a narrow-line Seyfert 1 galaxy, SBS 1353 + 564, using XMM-Newton and Swift multiband observations from 2007 to 2019. Our main results are as follows: (1) The temporal variability of SBS 1353+564 is random, while the hardness ratio is relatively constant over a time span of 13 yr; (2) We find a prominent soft X-ray excess feature below 2 keV that cannot be well described by a simple blackbody component; (3) After comparing the two most prevailing models for interpreting the origin of the soft X-ray excess, we find that the relativistically smeared reflection model is unable to fit the data above 5 keV well and the X-ray spectra do not show any reflection features, such as the Fe K α emission line. However, the warm corona model can obtain a good fitting result. For the warm corona model, we try to use three different sets of spin values to fit the data and derive different best-fitting parameter sets; (4) We compare the UV/optical spectral data with the extrapolated values of the warm corona model to determine which spin value is more appropriate for this source, and we find that the warm corona model with non-spin can sufficiently account for the soft X-ray excess in SBS 1353 + 564.
Deep Pairwise Learning To Rank For Search Autocomplete

Autocomplete (a.k.a"Query Auto-Completion","AC") suggests full queries based on a prefix typed by customer. Autocomplete has been a core feature of commercial search engine. In this paper, we propose a novel context-aware neural network based pairwise ranker (DeepPLTR) to improve AC ranking, DeepPLTR leverages contextual and behavioral features to rank queries by minimizing a pairwise loss, based on a fully-connected neural network structure. Compared to LambdaMART ranker, DeepPLTR shows +3.90% MeanReciprocalRank (MRR) lift in offline evaluation, and yielded +0.06% (p<0.1) Gross Merchandise Value (GMV) lift in an Amazon's online A/B experiment.
An Energy-based, always Index ≤1 and Structurally Amenable Electrical Circuit Model

Combining three themes: port-Hamiltonian energy-based modelling, structural analysis as used in the circuit world, and structural analysis of general differential-algebraic equations, we form a new model for electrical circuits, the compact port-Hamiltonian equations. They have remarkable simplicity and symmetry, and always have index at most 1 and other good numerical properties. The method has been implemented in Matlab. We give proofs and numerical results.
Accelerating Iterated Persistent Homology Computations with Warm Starts

Persistent homology is a topological feature used in a variety of applications such as generating features for data analysis and penalizing optimization problems. We develop an approach to accelerate persistent homology computations performed on many similar filtered topological spaces which is based on updating associated matrix factorizations. Our approach improves the update scheme of Cohen-Steiner, Edelsbrunner, and Morozov for permutations by additionally handling addition and deletion of cells in a filtered topological space and by processing changes in a single batch. We show that the complexity of our scheme scales with the number of elementary changes to the filtration which as a result is often less expensive than the full persistent homology computation. Finally, we perform computational experiments demonstrating practical speedups in several situations including feature generation and optimization guided by persistent homology.