Miracle-dz/UniFAR-FTUs huggingface dataset:
Restructuring of littoral fish assemblages after drought differs in two lakes at the terminus of a heavily regulated river

Drought significantly shifts the mix of functional groups present in river ecosystems and abundances of many aquatic fauna decline. The consequences of drought for fish often include the decline of ecological specialists and the predominance of generalist species. However, factors influencing the restructuring of littoral fish assemblages following prolonged drought in heavily regulated rivers are poorly understood. We compared the restructuring of littoral fish assemblages in 2 large connected lakes, which differ in hydrology and habitat availability, after 15 years of drought in their catchment. The once‐abundant and diverse fish assemblages showed limited signs of postdrought recovery after 5 years. There were differences between the 2 lakes related to the species present, their abundances, and the functional groups within fish assemblages. The functional groups present also shifted through time from alien‐dominated to freshwater generalist‐dominated. Only 2 of 5 native fish functional groups (freshwater generalist and diadromous) had increasing abundances (used as a proxy for recovery) following the drought, and these increases only occurred in the lake with the greater connectivity and diversity in habitat and hydrology. In contrast to patterns observed for natives, abundances of alien fishes in the littoral zone of the lakes declined after an initial spike in numbers associated with substantial river flows immediately following drought. This study shows that the recovery of native fish populations following prolonged drought in a heavily regulated semiarid river occurs over a time scale of years and recovery of some extant ecological specialists was incomplete after as many as 5 years.
Prediction of crime occurrence from multi-modal data using deep learning

In recent years, various studies have been conducted on the prediction of crime occurrences. This predictive capability is intended to assist in crime prevention by facilitating effective implementation of police patrols. Previous studies have used data from multiple domains such as demographics, economics, and education. Their prediction models treat data from different domains equally. These methods have problems in crime occurrence prediction, such as difficulty in discovering highly nonlinear relationships, redundancies, and dependencies between multiple datasets. In order to enhance crime prediction models, we consider environmental context information, such as broken windows theory and crime prevention through environmental design. In this paper, we propose a feature-level data fusion method with environmental context based on a deep neural network (DNN). Our dataset consists of data collected from various online databases of crime statistics, demographic and meteorological data, and images in Chicago, Illinois. Prior to generating training data, we select crime-related data by conducting statistical analyses. Finally, we train our DNN, which consists of the following four kinds of layers: spatial, temporal, environmental context, and joint feature representation layers. Coupled with crucial data extracted from various domains, our fusion DNN is a product of an efficient decision-making process that statistically analyzes data redundancy. Experimental performance results show that our DNN model is more accurate in predicting crime occurrence than other prediction models.
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers

High-dimensional statistical inference deals with models in which the the number of parameters p is comparable to or larger than the sample size n. Since it is usually impossible to obtain consistent procedures unless p/n → 0, a line of recent work has studied models with various types of structure (e.g., sparse vectors; block-structured matrices; low-rank matrices; Markov assumptions). In such settings, a general approach to estimation is to solve a regularized convex program (known as a regularized M-estimator) which combines a loss function (measuring how well the model fits the data) with some regularization function that encourages the assumed structure. The goal of this paper is to provide a unified framework for establishing consistency and convergence rates for such regularized M-estimators under high-dimensional scaling. We state one main theorem and show how it can be used to re-derive several existing results, and also to obtain several new results on consistency and convergence rates. Our analysis also identifies two key properties of loss and regularization functions, referred to as restricted strong convexity and decomposability, that ensure the corresponding regularized M-estimators have fast convergence rates.
Learning to rank in person re-identification with metric ensembles

We propose an effective structured learning based approach to the problem of person re-identification which outperforms the current state-of-the-art on most benchmark data sets evaluated. Our framework is built on the basis of multiple low-level hand-crafted and high-level visual features. We then formulate two optimization algorithms, which directly optimize evaluation measures commonly used in person re-identification, also known as the Cumulative Matching Characteristic (CMC) curve. Our new approach is practical to many real-world surveillance applications as the re-identification performance can be concentrated in the range of most practical importance. The combination of these factors leads to a person re-identification system which outperforms most existing algorithms. More importantly, we advance state-of-the-art results on person re-identification by improving the rank-1 recognition rates from 40% to 50% on the iLIDS benchmark, 16% to 18% on the PRID2011 benchmark, 43% to 46% on the VIPeR benchmark, 34% to 53% on the CUHK01 benchmark and 21% to 62% on the CUHK03 benchmark.
The VGLC: The Video Game Level Corpus

Levels are a key component of many different video games, and a large body of work has been produced on how to procedurally generate game levels. Recently, Machine Learning techniques have been applied to video game level generation towards the purpose of automatically generating levels that have the properties of the training corpus. Towards that end we have made available a corpora of video game levels in an easy to parse format ideal for different machine learning and other game AI research purposes.
Which muscles compromise human locomotor performance with age?

Ageing leads to a progressive decline in human locomotor performance. However, it is not known whether this decline results from reduced joint moment and power generation of all lower limb muscle groups or just some of them. To further our understanding of age-related locomotor decline, we compare the amounts of joint moments and powers generated by lower limb muscles during walking (self-selected), running (4 m s−1) and sprinting (maximal speed) among young, middle-aged and old adults. We find that age-related deficit in ankle plantarflexor moment and power generation becomes more severe as locomotion change from walking to running to sprinting. As a result, old adults generate more power at the knee and hip extensors than their younger counterparts when walking and running at the same speed. During maximal sprinting, young adults with faster top speeds demonstrate greater moments and powers from the ankle and hip joints, but interestingly, not from the knee joint when compared with the middle-aged and old adults. These findings indicate that propulsive deficit of ankle contributes most to the age-related locomotor decline. In addition, reduced muscular output from the hip rather than from knee limits the sprinting performance in older age.
On the Impact of Optimal Modulation and FEC Overhead on Future Optical Networks

The potential of optimum selection of modulation and forward error correction (FEC) overhead (OH) in future wavelength-routed nonlinear optical mesh networks is studied from an information theory perspective. Different network topologies are studied as well as both ideal soft-decision (SD) and hard-decision (HD) FEC based on demap-and-decode (bit wise) receivers. When compared to the somewhat standard assumption of QPSK with 7% OH, the results show large gains in network throughput. When compared to SD-FEC, HD-FEC is shown to cause network throughput losses of 12%, 15%, and 20% for a national, continental, and transcontinental topology, respectively. Furthermore, it is shown that for national and continental network topologies, using one modulation format and only two OHs achieves at least 75% of the maximum theoretical throughput. This is in contrast with the infinite number of OHs required in the ideal case. The obtained optimal OHs are between 5% and 80%, highlighting the advantage of using FEC with high OHs.
Finding twitter communities with common interests using following links of celebrities

One important problem in target advertising and viral marketing on online social networking sites is the efficient identification of communities with common interests in large social networks. Existing methods involve large scale community detection on the entire social network before determining the interests of individuals within these communities. This approach is both computationally intensive and may result in communities without a common interest. We propose an efficient approach for detecting communities that share common interests on Twitter. Our approach involves first identifying celebrities that are representative of an interest category before detecting communities based on linkages among followers of these celebrities. We also study the characteristics of these communities and the effects of deepening or specialization of interest.
A Smartphone App for Scheduling Irrigation on Cotton

Irrigation has become essential to crop production in many agricultural areas of the United States and especially across the cotton producing regions of the USA. As a result, the competition for available fresh water supplies is increasing and in some regions, cotton producers are faced with diminishing water supplies. If irrigated agriculture is to survive in this competitive environment, we must use irrigation water efficiently and more cost-effectively. The goal of the work described here was to develop an interactive ET-based irrigation scheduling tool for cotton which operates on a smartphone platform. The model uses meteorological data to calculate FAO 56 evapotranspiration (ETo) and a phenology-based crop coefficient (Kc) to estimate crop evapotranspiration (ETc). The model uses ETc, precipitation from the meteorological data, and irrigation events to estimate a daily plant-available soil water balance within the crop’s root zone. The model reports a daily root zone plant-available soil water deficit in terms of inches of water and percent of total. We converted the model into an interactive smartphone application (app) for iOS and Android operating systems. The Cotton App provides notifications to
Real-time detection and characterization of nuclear explosion using broadband analyses of regional seismic stations

This preliminary study aims to propose a new method of real-time detection and characterization of nuclear explosions by analyzing broadband seismic waveforms acquired from a network of regional seismic stations. Signal identification generated by a nuclear test was differentiated from natural sources of either earthquakes or other natural seismo-tectonic events by verifying crucial parameters, namely source depth, type of first motion, and P-wave domination of the broadband seismic wavesunder consideration. We examined and analyzed a recently hypothetical nuclear test performed by the North Koreangovernment that occurred on September 3, 2017 as a vital point to study. From spectral analyses, we found that the source of corresponding signals associated with detonations of the latest underground nuclear test was at a much shallower depth below the surface relatively compared with that of natural earthquakes, the suspected nuclear explosions produced compressional waves with radially directed outward from the source for their first motions, and the waves were only dominated by P-components. The results are then discussed in the context of potential uses of the proposed methodology for human-induced disaster early warning system and/or the need of rapid response purposes for minimizing the disaster risks.
Upper and lower bounds on the mean of noisy speech: application to minimax classification

In this paper, we derive upper and lower bounds on the mean of speech corrupted by additive noise. The bounds are derived in the log spectral domain. Also approximate bounds on the first and second order time derivatives are developed. It is also shown how to transform these bounds to the mel frequency cepstral coefficient (MFCC) domain. The proposed bounds are used to define the mismatch neighborhood for minimax classification. It is shown that this parametric neighborhood works quite well for artificially added noise and for a real-life mismatch scenario (moving car environment) which does not fully conform with the theoretical conditions used to derive the bounds. In contrast to traditional neighborhood structure for minimax classification, no empirical tuning of the bounds is required. It is believed that the applicability of the derived bounds is not limited to a minimax setting and can be potentially used to develop various compensation scenarios in the log spectral domain.
Heart rates of northern elephant seals diving at sea and resting on the beach.

Heart rates of northern elephant seals diving at sea and during apnoea on land were monitored to test whether a cardiac response to submergence is an important factor in their ability to make repetitive, long-duration dives. Seven juvenile northern elephant seals were captured at Año Nuevo, CA, instrumented and translocated to release sites around Monterey Bay. Heart rate and dive depth were recorded using custom-designed data loggers and analogue tape monitors during the seals' return to Año Nuevo. Heart rates during apnoea and eupnoea were recorded from four of the seals after they hauled out on the beach. Diving patterns were very similar to those of naturally migrating juveniles. The heart rate response to apnoea at sea and on land was a prompt bradycardia, but only at sea was there an anticipatory tachycardia before breathing commenced. Heart rate at sea declined by 64% from the surface rate of 107 +/- 3 beats min-1 (mean +/- S.D.), while heart rate on land declined by 31% from the eupnoeic rate of 65 +/- 8 beats min-1. Diving heart rate was inversely related to dive duration in a non-linear fashion best described by a continuous, curvilinear model, while heart rate during apnoea on land was independent of the duration of apnoea. Occasionally, instantaneous heart rate fell as low as 3 beats min-1 during diving. Although bradycardia occurs in response to apnoea both at sea and on land, only at sea is heart rate apparently regulated to minimise eupnoeic time and to ration oxygen stores to ensure adequate supplies for the heart and brain not only as the dive progresses normally but also when a dive is abnormally extended.
Genetic landscape of high hyperdiploid childhood acute lymphoblastic leukemia

High hyperdiploid acute lymphoblastic leukemia (ALL) is one of the most common malignancies in children. It is characterized by gain of chromosomes, typically +X, +4, +6, +10, +14, +17, +18, and +21,+21; little is known about additional genetic aberrations. Approximately 20% of the patients relapse; therefore it is clinically important to identify risk-stratifying markers. We used SNP array analysis to investigate a consecutive series of 74 cases of high hyperdiploid ALL. We show that the characteristic chromosomal gains are even more frequent than previously believed, indicating that karyotyping mistakes are common, and that almost 80% of the cases display additional abnormalities detectable by SNP array analysis. Subclonality analysis strongly implied that the numerical aberrations were primary and arose before structural events, suggesting that step-wise evolution of the leukemic clone is common. An association between duplication of 1q and +5 was seen (P = 0.003). Other frequent abnormalities included whole-chromosome uniparental isodisomies (wUPIDs) 9 and 11, gain of 17q not associated with isochromosome formation, extra gain of part of 21q, deletions of ETS variant 6 (ETV6), cyclin-dependent kinase inhibitor 2A (CKDN2A) and paired box 5 (PAX5), and PAN3 poly(A) specific ribonuclease subunit homolog (PAN3) microdeletions. Comparison of whole-chromosome and partial UPID9 suggested different pathogenetic outcomes, with the former not involving CDKN2A. Finally, two cases had partial deletions of AT rich interactive domain 5B (ARID5B), indicating that acquired as well as constitutional variants in this locus may be associated with pediatric ALL. Here we provide a comprehensive characterization of the genetic landscape of high hyperdiploid childhood ALL, including the heterogeneous pattern of secondary genetic events.
Time-resolved spectroscopy of GRB 021004 reveals a clumpy extended wind

A high-resolution spectroscopy of GRB 021004 revealed a wealth of absorption lines from several intermediate-ionization species. The velocity structure of the absorber is complex and material with velocity up to ≥3000 km s -1 is observed. Since only the blueshifted component is observed, the absorber is very likely to be material closely surrounding the gamma-ray burst. We use a time-dependent photoionization code to track the abundance of the ions over time. Thanks to the presence of absorption from intermediate-ionization states at long times, we can estimate the location and mass of the components of the absorber. We interpret those constraints within the hypernova scenario showing that the mass-loss rate of the progenitor must have been ∼10 -4 M ⊙ yr -1 , suggestive of a very massive star. In addition, the wind termination shock must lie at a distance of at least 100 pc, implying a low-density environment. The velocity structure of the absorber also requires clumping of the wind at those large distances.
Platelet coagulant activities and hemostasis: a hypothesis.

Well, yes: he may have done all that, but there’s no proof that he did. I am beginning to believe that nothing can ever be proved. These are reasonable hypotheses which take the facts into account: but I am only too well aware that they come from me, that they are simply a way of unifying my own knowledge. Not a single Platelets have recently been shown to participate in reactions with blood coagulation factors at every stage of intrinsic clotting, from contact activation to fibrin formation. Platelets can trigger intrinsic coagulation by two alternative pathways, the first of which involves factors XII and Xl and adenosine diphosphate, and the second of which bypasses factor XII, pro
Neural Correlates of High-Gamma Oscillations (60–200 Hz) in Macaque Local Field Potentials and Their Potential Implications in Electrocorticography

Recent studies using electrocorticographic (ECoG) recordings in humans have shown that functional activation of cortex is associated with an increase in power in the high-gamma frequency range (∼60–200 Hz). Here we investigate the neural correlates of this high-gamma activity in local field potential (LFP). Single units and LFP were recorded with microelectrodes from the hand region of macaque secondary somatosensory cortex while vibrotactile stimuli of varying intensities were presented to the hand. We found that high-gamma power in the LFP was strongly correlated with the average firing rate recorded by the microelectrodes, both temporally and on a trial-by-trial basis. In comparison, the correlation between firing rate and low-gamma power (40–80 Hz) was much smaller. To explore the potential effects of neuronal firing on ECoG, we developed a model to estimate ECoG power generated by different firing patterns of the underlying cortical population and studied how ECoG power varies with changes in firing rate versus the degree of synchronous firing between neurons in the population. Both an increase in firing rate and neuronal synchrony increased high-gamma power in the simulated ECoG data. However, ECoG high-gamma activity was much more sensitive to increases in neuronal synchrony than firing rate.
Hypothesis of K+-Recycling Defect Is Not a Primary Deafness Mechanism for Cx26 (GJB2) Deficiency

K+-recycling defect is a long-standing hypothesis for deafness mechanism of Connexin26 (Cx26, GJB2) mutations, which cause the most common hereditary deafness and are responsible for >50% of nonsyndromic hearing loss. The hypothesis states that Cx26 deficiency may disrupt inner ear gap junctions and compromise sinking and recycling of expelled K+ ions after hair cell excitation, causing accumulation of K+-ions in the extracellular space around hair cells producing K+-toxicity, which eventually induces hair cell degeneration and hearing loss. However, this hypothesis has never been directly evidenced, even though it has been widely referred to. Recently, more and more experiments demonstrate that this hypothesis may not be a deafness mechanism underlying Cx26 deficiency. In this review article, we summarized recent advances on the K+-recycling and mechanisms underlying Cx26 deficiency induced hearing loss. The mechanisms underlying K+-sinking, which is the first step for K+-recycling in the cochlea, and Cx26 deficiency induced cochlear developmental disorders, which are responsible for Cx26 deficiency induced congenital deafness and associated with disruption of permeability of inner ear gap junctional channels to miRNAs, are also summarized and discussed.
High-resolution computed tomographic characteristics in acute farmer's lung and in its follow-up.

High resolution computed tomography (HRCT) scans are increasingly used in hypersensitivity pneumonitis (HP). This study looked at HRCT findings at different phases of farmer's lung (FL), a common form of HP. A cross sectional analysis of 95 HRCT scans of FL cases (20 acute, 75 with a history of FL, 48 still in contact (Ex +) (dairy farm), and 27 who had ceased contact (Ex-)) was made. All scans were read independently by two, and if needed by three, radiologists blinded to the category. The lungs were divided into six regions (fives lobes + lingula), and read for attenuation/mosaic, ground-glass, micronodules, fibrosis, and emphysema. A score of 0-3 was given for each region and each variable: 0 = absence, 1 =<25% of the surface, 2 = 25-50%, 3 =>50%. Mediastinal lymphadenopathy was also noted. Ground glass, predominating in the lower lobes, was the most frequent feature in the acute and Ex+ cases. Other abnormalities had no preferential distribution. Ex+ had more ground-glass than the Ex- (p=0.0025). Emphysema was more frequently seen than interstitial fibrosis (p=0.004). Mediastinal lymphadenopathy was present in 26 cases (9 acute, 10 Ex+ and 7 Ex-). In conclusion, in farmer's lung: 1) ground-glass predominates in the lower lobes while the other abnormalities have no anatomic predilection; 2) contact avoidance allows a better resolution of computed tomography abnormalities than continued exposure; 3) emphysema is a more frequent finding than interstitial fibrosis; and 4) the presence of mediastinal lymphadenopathy has no negative diagnostic value.