Miracle-dz/UniFAR-FTUs huggingface dataset:
“Surface,” “satellite” or “simulation”: Mapping intra‐urban microclimate variability in a desert city

Mapping spatial and temporal variability of urban microclimate is pivotal for an accurate estimation of the ever‐increasing exposure of urbanized humanity to global warming. This particularly concerns cities in arid/semi‐arid regions which cover two fifths of the global land area and are home to more than one third of the world's population. Focusing on the desert city of Be'er Sheva Israel, we investigate the spatial and temporal patterns of urban–rural and intra‐urban temperature variability by means of satellite observation, vehicular traverse measurement, and computer simulation. Our study reveals a well‐developed nocturnal canopy layer urban heat island in Be'er Sheva, particularly in the winter, but a weak diurnal cool island in the mid‐morning. Near surface air temperature exhibits weak urban–rural and intra‐urban differences during the daytime (<1°C), despite pronounced urban surface cool islands observed in satellite images. This phenomenon, also recorded in some other desert cities, is explained by the rapid increase in surface skin temperature of exposed desert soils (in the absence of vegetation or moisture) after sunrise, while urban surfaces are heated more slowly. The study highlights differences among the three methods used for describing urban temperature variability, each of which may have different applications in fields such as urban planning, climate change mitigation, and epidemiological research.
Food Safety Document Classification Using LSTM-based Ensemble Learning

It is a common case that there are huge news reports with different topics in a website (even in a food-related website), food safety news reports are needed to be selected for further analysis, and this is a text (or document) classification problem. In this paper, we propose a food safety document classification method using LSTM (long and short-term memory)-based ensemble learning. Firstly, due to the high cost of human-annotation, the food safety document corpus includes only one-class samples, and the food safety document classification based on such a corpus is a one-class classification problem. We propose an automatic corpus expansion approach which uses a large number of unlabeled news reported online as negative samples (the documents that are not related with food safety), and our food safety document corpus becomes a binary-class-based corpus that has both positive samples and negative samples. Secondly, our automatic corpus expansion brings the following two problems to document corpus: data noise and data unbalance. We choose an ensemble learning method which is based on LSTM(Long Short-Term Memory) for our document classification. Overall, the document classification method based on the LSTM-based ensemble learning method can automatically detect food safety documents from websites with outstanding performances.
Trends in Magnetic Resonance Imaging Research in Dentistry

Trends in Magnetic Resonance Imaging Research in Dentistry Magnetic resonance imaging (MRI) is widely used as an essential imaging modality for diagnosis in dentistry and medicine. MRI is noninvasive and does not require exposure to any ionizing radiation. This is in contrast to X-ray computed tomography, X-ray angiography, and positron emission tomography (PET) by gamma rays. MR can create high-quality images of detailed anatomical structures because of its excellent soft tissue resolution. Applications of MRI include imaging blood vascular systems without a contrast medium, functional imaging of brain activity, spectroscopy, and fractional anisotropy. In clinical dentistry, MRI is used for the diagnosis of oral and maxillofacial diseases, especially tumors or temporomandibular disorders. Research on MRI use in dentistry has emphasized the areas of endodontics, orthodontics, and dental radiology.
Application of Neutron Activation Analysis in the Determination of Trace Elements in Tourmalines

The present work reports a methodology to determine trace elements in tourmaline by neutron activation analysis. The interference effect of B was demonstrated by irradiating samples of different weight from the same tourmaline and plotting a graph of specific activity against the sample weight. The ideal weight with minimal interference effect was obtained in the straight line deviation. Instrumental neutron activation analysis can be applied to the ideal weight with minimal interference effect. Radionuclides Zn, Fe, Co, Cr, Sc, Br, La, Na, K, Sr, Ta, Cs, Sb were detected.
Recommendations For The Generalized Intelligent Framework for Tutoring Based On The Development Of The DeepTutor Tutoring Service

We present in this paper the design of DeepTutor, the first dialogue-based intelligent tutoring system based on Learning Progressions, and its implications for developing a Generalized Framework for Intelligent Tutoring. We also present the design of SEMILAR, a semantic similarity toolkit, that helps researchers investigate and author semantic similarity models for evaluating natural language student inputs in conversatioanl ITSs. DeepTutor has been developed as a web service while SEMILAR is a Java library. Based on our experience with developing DeepTutor and SEMILAR, we contrast three different models for developing a standardized architecture for intelligent tutoring systems: (1) a single-entry web service coupled with XML protocols for queries and data, (2) a bundle of web services, and (3) library-API. Based on the analysis of the three models, recommendations are provided.
Fibrotic Human Lung Extracellular Matrix as a Disease- Specific Substrate for Models of Pulmonary Fibrosis

Idiopathic Pulmonary Fibrosis (IPF) is an irreversible and uniformly fatal lung disease marked by destruction and scarring of the lung parenchyma and progressive loss of respiratory function. IPF affects nearly 3 million people worldwide, and annual mortality in the US alone exceeds 40,000. Nintedanib and pirfenidone, the only drugs approved for the treatment of IPF, slow progression but do not cure the disease. Consequently, there is a pressing need for effective treatments beside lung transplantation. Unfortunately, predictive models of IPF are not available, underscoring the critical need for physiologically relevant in-vitro substrates that enable quantitative and mechanistic studies of human IPF. Here we report the development and characterization of a human pulmonary fibrosisspecific cell culture substrate comprised of intact fibrotic lung extracellular matrix that recapitulates the human IPF disease environment in vitro. We document the activation and disease-specific phenotype of human lung fibroblasts cultured in the IPF disease-specific substrate and establish feasibility of testing antifibrotic agents using this substrate. Altogether, our results demonstrate the applicability of this fibrosis-specific substrate for 3D in-vitro models of IPF and cell-based assays in early-stage drug discovery.
Thermal Expansion Behavior of Hot-Pressed Engineered Matrices

Advanced engineered matrix composites (EMCs) require that the coefficient of thermal expansion (CTE) of the engineered matrix (EM) matches those of the fiber reinforcements as closely as possible in order to reduce thermal compatibility strains during heating and cooling of the composites. The present paper proposes a general concept for designing suitable matrices for long fiber reinforced composites using a rule of mixtures (ROM) approach to minimize the global differences in the thermal expansion mismatches between the fibers and the engineered matrix. Proof-of-concept studies were conducted to demonstrate the validity of the concept. Powder mixtures of seven engineered matrices were formulated based on ROM calculations and hot-pressed for thermal expansion measurements: 10(vol.%)CrSi270%SiC-20%Si3N4 (CrSi2-EM); 10(vol.%)CrMoSi-60%SiC-30%Si3N4 (CrMoSi-EM); 10(vol.%)MoSi270%SiC-20%Si3N4 (MoSi2-EM); 10(vol.%)TiSi2-70%SiC-20%Si3N4 (TiSi2-EM); 10(vol.%)WSi270%SiC-20%Si3N4 (WSi2-EM); 50(vol.%)MoSi2-50%Si3N4 and 20(vol.%)TiSi2-80%Si3N4. Density measurements conducted on the hot-pressed specimens revealed that the volume fractions of total porosity varied between 36 and 43%. Thermal expansion measurements were conducted between room temperature and 1523 K during three heat-cool cycles. The corrected thermal expansion, (∆L/L0)thermal, varied with the absolute temperature, T, as (∆L/L0)thermal = A(T – 293)3 + B(T – 293)2 + C(T – 293) + D where, A, B, C and D are regression constants. The magnitudes of (∆L/L0)thermal for the 50(vol.%)MoSi250%Si3N4 and 20(vol.%)TiSi2-80%Si3N4 specimens increased with increasing thermal cycle due to an increase in the global residual tensile stresses. In contrast, excellent reproducibility was observed for the other five engineered matrix compositions containing SiC after the first heat-up cycle had relieved residual stresses due to hot-pressing. The experimental (∆L/L0)thermal data on these engineered matrices are shown to be close to those for SiC thereby proving the validity of the proposed concept.
TNFα polymorphisms and risk of psoriatic arthritis

Background: Tumour necrosis factor α (TNFα) is a cytokine of critical importance in psoriatic arthritis. Objectives: (1) To examine the association between TNFα promoter gene polymorphisms and psoriatic arthritis in two well characterised Canadian populations with the disease; (2) to carry out a meta-analysis of all TNFα association studies in white psoriatic arthritis populations. Methods: DNA samples were genotyped for five TNF variants by time of flight mass spectrometry using the Sequenom platform. All five single nucleotide polymorphisms were in the 5′ flanking region of TNFα gene at the following positions: −1031 (T→C), −863 (C→A), −857 (C→T), −308 (G→A), and −238 (G→A). Primary analyses were based on logistic regression. Summary estimates of disease/genotype relations from several studies were derived from random effects meta-analyses. Results: 237 psoriatic arthritis subjects and 103 controls from Newfoundland and 203 psoriatic arthritis subjects and 101 controls from Toronto were studied. A combined analysis of data from both populations, showed a significant association between disease status and the −238(A) variant (p = 0.01). The meta-analysis estimate for the −238(A) TNFα variant in eight psoriatic arthritis populations was also significant (odds ratio = 2.29 (95% confidence interval, 1.48 to 3.55)). Conclusions: Analysis of TNFα variants in psoriatic arthritis populations shows that the −238 (A) variant is a significant risk factor for this disease.
Unequal cities: Innovation, Skill Sorting and Inequality

One of the recent developments among American cities is the diverging trend in their skill compositions and measured innovation (e.g., patents, venture capital). Over the past thirty years, skilled cities have become more skilled and more productive, and have experienced faster growth in average wages and housing prices. This geographical divergence has contributed substantially to the rising inequality in America. This paper constructs a multi-city model of spatial skill sorting to explore the causes of this divergence. The key idea is that cities with advantages in innovation attract more productive entrepreneurs and more workers, thereby driving up wages and housing prices. I then show that the changing technologies have reinforced the geographical sorting of skills. Specifically, three types of technological changes have increased the benefits of skill clustering in innovative cities: general productivity increases; improvements in communications technologies; and declines in trade costs.
Subacute electroacupuncture at Baihui (GV 20) and Dazhui (GV 14) promotes post-stroke functional recovery via neurogenesis and astrogliosis in a photothrombotic stroke mouse model.

OBJECTIVE To investigate the optimal timing and underlying mechanism of electroacupuncture (EA) at Baihui (GV 20) and Dazhui (GV 14) for improved long-term functional recovery after focal cerebral ischemia in a photothrombotic stroke mouse model. METHODS Totally 50 adult male C57BL/6J mice were assigned into 5 groups: (a) the control group, sham-operated mice (n = 10); (b) the vehicle group, focal cerebral ischemia induction without EA (n = 10); (c) the acute EA group, mice received EA immediately post-ischemia, followed by once-daily treatments for 7 consecutive days (n = 10); (d) the subacute EA group, mice received EA 4 days post-ischemia, followed by once-daily treatments for 7 consecutive days (n = 10); (e) the delayed EA group. EA stimulation (2 Hz, 2 V for 20 min) was applied to acupuncture points (acupoints), Baihui (GV 20) and Dazhui (GV 14), once a day for 7 consecutive days beginning immediately (acute treatment), 4 d (subacute treatment) and 10 d (delayed treatment) after focal cerebral ischemia in C57BL/6J mice. Behavioral assessments were conducted 21 and 28 d post-ischemia and histopathological analyses were performed 28 days post-ischemia. RESULTS The subacute EA treatment at Baihui (GV 20) and Dazhui (GV 14) significantly improved functional recovery compared to the vehicle group 28 d after ischemic brain injury, although brain atrophy was not reduced. The number of NeuN+ and NeuN+/BrdU+ cells as well as GFAP intensity in the ipsilateral cortex were significantly increased in the subacute group compared to the vehicle group 28 d post-ischemia. We concluded that EA stimulation 4 d post-ischemia (subacute treatment) enhanced neurogenesis and astrogliosis, likely contributing to long-term functional recovery following focal cerebral ischemia. CONCLUSION Our findings suggest that the timing of the EA therapy at Baihui (GV 20) and Dazhui (GV 14) determines the therapeutic effects in mice with focal cerebral ischemia induced by photothrombotic occlusion.
Long-term Variations in Cosmogenic Be-7 Concentrations with the 11-year Cycle of Solar Activity

Be-7 radionuclide is produced by galactic cosmic-ray interactions with atmospheric nuclei. We analyzed long-term variations (1983-2006) in surface Be-7 concentrations in Japan (geomagnetic latitude 25N) to study an influence of the solar activity. Be-7 concentrations in surface air depend on a production rate of Be-7 and a transport process in the atmosphere. The production rate is inversely correlated with the solar activity, while the transport in the atmosphere is affected by air mass motions. The present result indicated that the surface Be-7 concentrations are anticorrelated with the 11year cycle of solar activity and the air mass motions do not essentially affect the Be-7 variations.
Mobilome and Resistome Reconstruction from Genomes Belonging to Members of the Bifidobacterium Genus

Specific members of the genus Bifidobacterium are among the first colonizers of the human/animal gut, where they act as important intestinal commensals associated with host health. As part of the gut microbiota, bifidobacteria may be exposed to antibiotics, used in particular for intrapartum prophylaxis, especially to prevent Streptococcus infections, or in the very early stages of life after the birth. In the current study, we reconstructed the in silico resistome of the Bifidobacterium genus, analyzing a database composed of 625 bifidobacterial genomes, including partial assembled strains with less than 100 genomic sequences. Furthermore, we screened bifidobacterial genomes for mobile genetic elements, such as transposases and prophage-like elements, in order to investigate the correlation between the bifido-mobilome and the bifido-resistome, also identifying genetic insertion hotspots that appear to be prone to horizontal gene transfer (HGT) events. These insertion hotspots were shown to be widely distributed among analyzed bifidobacterial genomes, and suggest the acquisition of antibiotic resistance genes through HGT events. These data were further corroborated by growth experiments directed to evaluate bacitracin A resistance in Bifidobacterium spp. , a property that was predicted by in silico analyses to be part of the HGT-acquired resistome.
LEARNING TO GENERATE JAZZ AND POP PIANO MUSIC FROM AUDIO VIA MIR TECHNIQUES

EXTENDED ABSTRACT Most AI models today use symbolic, score-like music data such as MIDI ﬁles to learn to compose music (e.g., [3, 11]). While exciting progress is being made, the music generated by these models is usually not expressive enough [10]. A main reason is that, as music performance is an artistic and subjective process, a music score mainly speciﬁes what to be played, leaving much space for a musician to decide how to play it. What an AI model can learn from music scores is therefore automatic generation of music scores, which can for example be used in a digital audio workstation (DAW) to assist music composition. However, without performance-level guidance, the AI cannot generate music that is ready to be directly listened to. We explore at the Taiwan AI Labs an alternative approach that learns to compose music from musical audio recordings, by capitalizing state-of-the-art music information retrieval (MIR) techniques. Speciﬁcally, we propose a methodology that is sketched in Figure 1. Given an input audio recording, we ﬁrstly apply blind source separation [8] to isolate out (from the input) the individual musical sources of interest, such as the piano, drums, bass, and vocal. We then apply music transcription [4] to convert the separated sources (which are audio ﬁles) into the symbolic domain by predicting the pitch, onset/offset time (in absolute timing), and velocity (dynamics) of the involved notes. In the meanwhile, we perform beat and downbeat detection [1] over the input audio to get the underlying metrical grid of the music. We then use the machine-transcribed symbolic data, possibly along with other machine-predicted information (such as genre/mood
The Eminence of Co-Expressed Ties in Schizophrenia Network Communities

Exploring gene networks is crucial for identifying significant biological interactions occurring in a disease condition. These interactions can be acknowledged by modeling the tie structure of networks. Such tie orientations are often detected within embedded community structures. However, most of the prevailing community detection modules are intended to capture information from nodes and its attributes, usually ignoring the ties. In this study, a modularity maximization algorithm is proposed based on nonlinear representation of local tangent space alignment (LTSA). Initially, the tangent coordinates are computed locally to identify k-nearest neighbors across the genes. These local neighbors are further optimized by generating a nonlinear network embedding function for detecting gene communities based on eigenvector decomposition. Experimental results suggest that this algorithm detects gene modules with a better modularity index of 0.9256, compared to other traditional community detection algorithms. Furthermore, co-expressed genes across these communities are identified by discovering the characteristic tie structures. These detected ties are known to have substantial biological influence in the progression of schizophrenia, thereby signifying the influence of tie patterns in biological networks. This technique can be extended logically on other diseases networks for detecting substantial gene “hotspots”.
30TH INTERNATIONAL COSMIC RAY CONFERENCE MC Simulation and Layout Studies for a future Cherenkov Telescope Array

Detailed Monte Carlo simulations of possible configurations for a future large-scale installation of Imaging Atmospheric Cherenkov Telescopes, the CTA (Cherenkov Telescope Array), have been carried out. This includes a full treatment of shower fluctuations, night sky background, registration of the signal and reconstruction of the registered showers. Although not representing a detailed design study, the simulations demonstrate that a sensitivity at the level of 1 mCrab can be achieved with existing technology and analysis methods. Spectra of somewhat stronger sources may be measured over more than three orders of magnitude in energy. Combining a large number of IACTs allows to achieve unprecedented levels of hadron rejection and angular resolution. Among the options studied are systems with a modest number of very large telescopes and/or larger numbers of smaller telescopes, of different spacings, pixel sizes, etc. Systems consisting of two or three different telescope sizes may achieve an energy coverage from a few 10 GeV to 100 TeV and more.
The dual of compact ordered spaces is a variety.

In a recent paper (2018), D. Hofmann, R. Neves and P. Nora proved that the dual of the category of compact partially ordered spaces and monotone continuous maps is a quasi-variety - not finitary, but bounded by $\aleph_1$. An open question was: is it also a variety? We show that the answer is affirmative. We describe the variety by means of a set of finitary operations, together with an operation of countably infinite arity, and equational axioms. The dual equivalence is induced by the dualizing object [0,1].
Weak Detection in the Spiked Wigner Model

We consider the weak detection problem in a rank-one spiked Wigner data matrix where the signal-to-noise ratio is small so that reliable detection is impossible. We prove a central limit theorem for the linear spectral statistics of general rank-one spiked Wigner matrices, and based on the central limit theorem, we propose a hypothesis test on the presence of the signal by utilizing the linear spectral statistics of the data matrix. The test is data-driven and does not require prior knowledge about the distribution of the signal or the noise. When the noise is Gaussian, the proposed test is optimal in the sense that its error matches that of the likelihood ratio test, which minimizes the sum of the Type-I and Type-II errors. If the density of the noise is known and non-Gaussian, the error of the test can be lowered by applying an entrywise transformation to the data matrix.
Discovery of 8-Amino-Substituted 2-Phenyl-2,7-Naphthyridinone Derivatives as New c-Kit/VEGFR-2 Kinase Inhibitors

The 2,7-naphthyridone scaffold has been proposed as a novel lead structure of MET inhibitors by our group. To broaden the application of this new scaffold, a series of 8-amino-substituted 2-phenyl-2,7-naphthyridin-1(2H)-one derivatives were designed and synthesized. Preliminary biological screening resulted in the discovery of a new lead of c-Kit and VEGFR-2 kinase inhibitors. Compound 9k exhibited excellent c-Kit inhibitory activity, with an IC50 value of 8.5 nM, i.e., it is 38.8-fold more potent than compound 3 (IC50 of 329.6 nM). Moreover, the compounds 10l and 10r exhibited good VEGFR-2 inhibitory activity, with IC50 values of 56.5 and 31.7 nM, respectively, i.e., they are 5.0–8.8-fold more potent than compound 3 (IC50 of 279.9 nM). Molecular docking experiments provided further insight into the binding interactions of the new lead compounds with c-Kit and VEGFR-2 kinase. In this study, an 8-amino-substituted 2-phenyl-2,7-naphthyridin-1(2H)-one scaffold was identified as the new lead structure of c-Kit and VEGFR-2 kinase inhibitors.
Harnessing Inﬁnite-Horizon Off-Policy Evaluation: Double Robustness via Duality

Inﬁnite horizon off-policy policy evaluation is a highly challenging task due to the excessively large variance of typical importance sampling (IS) estimators. Recently, Liu et al. (2018a) proposed an approach that substantially reduces the variance of inﬁnite-horizon off-policy evaluation by estimating the stationary density ratio, but at the cost of introducing potentially high biases due to the error in density ratio estimation. In this paper, we develop a bias-reduced augmentation of their method, which can take advantage of a learned value function to improve accuracy. Our method is doubly robust in that the bias vanishes when either the density ratio or value function estimation is perfect. In general, when either of them is accurate, the bias can also be reduced. Both theoretical and empirical results show that our method yields signiﬁcant advantages over previous methods.