Miracle-dz/UniFAR-FTUs huggingface dataset:
SLIDER: Fast and Efficient Computation of Banded Sequence Alignment

Motivation: The ability to generate massive amounts of sequencing data continues to overwhelm the processing capacity of existing algorithms and compute infrastructures. In this work, we explore the use of hardware/software co-design and hardware acceleration to significantly reduce the exe-cution time of short sequence alignment, a crucial step in analyzing sequenced genomes. We in-troduce SLIDER, a highly parallel and accurate pre-alignment filter that remarkably reduces the need for computationally-costly dynamic programming algorithms. The first key idea of our pro-posed pre-alignment filter is to provide high filtering accuracy by correctly detecting all common subsequences shared between two given sequences. The second key idea is to design a hardware accelerator design that adopts modern FPGA (field-programmable gate array) architectures to fur-ther boost the performance of our algorithm. Results: SLIDER significantly improves the accuracy of pre-alignment filtering by up to two orders of magnitude compared to the state-of-the-art pre-alignment filters, GateKeeper and SHD. Our FPGA accelerator is up to three orders of magnitude faster than the equivalent CPU implementa-tion of SLIDER. Using a single FPGA chip, we benchmark the benefits of integrating SLIDER with five state-of-the-art sequence aligners, designed for different computing platforms. The addition of SLIDER as a pre-alignment step reduces the execution time of five state-of-the-art sequence align-ers by up to 18.8x. SLIDER can be adopted for any bioinformatics pipeline that performs sequence alignment for verification. Unlike most existing methods that aim to accelerate sequence align-ment, SLIDER does not sacrifice any of the aligner capabilities, as it does not modify or replace the alignment step.
Planning Solar in Energy-Managed Cellular Networks

Recently, there has been a lot of interest on the energy efficiency and environmental impact of wireless networks. Given that the base stations are the network elements that use most of this energy, much research has dealt with ways to reduce the energy used by the base stations by turning them off during periods of low load. In addition to this, installing a solar harvesting system made up of solar panels, batteries, charge controllers, and inverters is another way to further reduce the network environmental impact, and some research has been dealing with this for individual base stations. In this paper, we show that both techniques are tightly coupled. We propose a mathematical model that captures the synergy between solar installation over a network and the dynamic operation of energy-managed base stations. We study the interactions between the two methods for networks of hundreds of base stations and show that the order in which each method is introduced into the system does make a difference in terms of cost and performance. We also show that installing solar is not always the best solution even when the unit cost of the solar energy is smaller than the grid cost. We conclude that planning the solar installation and energy management of the base stations has to be done jointly.
Comparison of temperature measurements in esophagus and urinary bladder in comatose patients after cardiac arrest undergoing mild therapeutic hypothermia.

BACKGROUND Mild therapeutic hypothermia (MTH) is a recommended method of treatment for comatose out-of-hospital cardiac arrest (OHCA) survivors. However, the proper site of temperature measurement in MTH is still not defined. The aim of this study was to compare temperature measurements in the esophagus and urinary bladder in comatose post-OHCA patients treated with MTH. METHODS This temperature comparison protocol was a part of a prospective, observational, multicenter cohort study. The study population included 36 unconscious patients after resuscitation for OHCA. The patient's core temperature was independently measured every hour during MTH in the urinary bladder and in the esophagus. RESULTS The mean temperature was lower in the esophagus (differences during induction phase: 1.04 ± 0.92ºC, p < 0.0001; stabilization phase: 0.54 ± 0.39ºC, p < 0.0001; rewarming phase: 0.40 ± 0.47ºC, p < 0.0001). Nevertheless, a strong correlation between both sites was found (R² = 0.83, p < 0.001). The decrease in temperature observed in the esophagus during the induction phase was faster when compared with the urinary bladder (1.09 ± 0.71°C/h vs. 0.83 ± 0.41°C/h; p = 0.002). As a consequence, time to reach temperature < 34.0°C was longer when temperature was measured in the urinary bladder (the difference between medians of the time 1.0 [0-1.5] h, p < 0.001). CONCLUSIONS Urinary bladder temperature measurements may lag behind temperature changes measured in the esophagus.Monitoring temperature simultaneously in the esophagus and in the urinary bladder is an accessible and reliable combination, although esophageal measurements seem to better reflect the dynamics of temperature changes, thus it seems to be more appropriate for MTH control. ClinicalTrials.gov Identifier: NCT02611934.
Dynamic Environment Mapping for Augmented Reality Applications on Mobile Devices

Augmented Reality is a topic of foremost interest nowadays. Its main goal is to seamlessly blend virtual content in real-world scenes. Due to the lack of computational power in mobile devices, rendering a virtual object with high-quality, coherent appearance and in real-time, remains an area of active research. In this work, we present a novel pipeline that allows for coupled environment acquisition and virtual object rendering on a mobile device equipped with a depth sensor. While keeping human interaction to a minimum, our system can scan a real scene and project it onto a two-dimensional environment map containing RGB+Depth data. Furthermore, we define a set of criteria that allows for an adaptive update of the environment map to account for dynamic changes in the scene. Then, under the assumption of diffuse surfaces and distant illumination, our method exploits an analytic expression for the irradiance in terms of spherical harmonic coefficients, which leads to a very efficient rendering algorithm. We show that all the processes in our pipeline can be executed while maintaining an average frame rate of 31Hz on a mobile device.
Predicting Argumenthood of English Preposition Phrases

Distinguishing between core and non-core dependents (i.e., arguments and adjuncts) of a verb is a longstanding, nontrivial problem. In natural language processing, argumenthood information is important in tasks such as semantic role labeling (SRL) and preposition phrase (PP) attachment disambiguation. In theoretical linguistics, many diagnostic tests for argumenthood exist but they often yield conflicting and potentially gradient results. This is especially the case for syntactically oblique items such as PPs. We propose two PP argumenthood prediction tasks branching from these two motivations: (1) binary argument/adjunct classification of PPs in VerbNet, and (2) gradient argumenthood prediction using human judgments as gold standard, and report results from prediction models that use pretrained word embeddings and other linguistically informed features. Our best results on each task are (1) $acc. =0.955$, $F_1=0.954$ (ELMo+BiLSTM) and (2) Pearson's $r=0.624$ (word2vec+MLP). Furthermore, we demonstrate the utility of argumenthood prediction in improving sentence representations via performance gains on SRL when a sentence encoder is pretrained with our tasks.
Linking microbial co‐occurrences to soil ecological processes across a woodland‐grassland ecotone

Abstract Ecotones between distinct ecosystems have been the focus of many studies as they offer valuable insights into key drivers of community structure and ecological processes that underpin function. While previous studies have examined a wide range of above‐ground parameters in ecotones, soil microbial communities have received little attention. Here we investigated spatial patterns, composition, and co‐occurrences of archaea, bacteria, and fungi, and their relationships with soil ecological processes across a woodland‐grassland ecotone. Geostatistical kriging and network analysis revealed that the community structure and spatial patterns of soil microbiota varied considerably between three habitat components across the ecotone. Woodland samples had significantly higher diversity of archaea while the grassland samples had significantly higher diversity of bacteria. Microbial co‐occurrences reflected differences in soil properties and ecological processes. While microbial networks were dominated by bacterial nodes, different ecological processes were linked to specific microbial guilds. For example, soil phosphorus and phosphatase activity formed the largest clusters in their respective networks, and two lignolytic enzymes formed joined clusters. Bacterial ammonia oxidizers were dominant over archaeal oxidizers and showed a significant association (p < 0.001) with potential nitrification (PNR), with the PNR subnetwork being dominated by Betaproteobacteria. The top ten keystone taxa comprised six bacterial and four fungal OTUs, with Random Forest Analysis revealing soil carbon and nitrogen as the determinants of the abundance of keystone taxa. Our results highlight the importance of assessing interkingdom associations in soil microbial networks. Overall, this study shows how ecotones can be used as a model to delineate microbial structural patterns and ecological processes across adjoining land‐uses within a landscape.
Predicting the Programming Language of Questions and Snippets of StackOverflow Using Natural Language Processing

Stack Overflow is the most popular Q&A website among software developers. As a platform for knowledge sharing and acquisition, the questions posted in Stack Overflow usually contain a code snippet. Stack Overflow relies on users to properly tag the programming language of a question and it simply assumes that the programming language of the snippets inside a question is the same as the tag of the question itself. In this paper, we propose a classifier to predict the programming language of questions posted in Stack Overflow using Natural Language Processing (NLP) and Machine Learning (ML). The classifier achieves an accuracy of 91.1% in predicting the 24 most popular programming languages by combining features from the title, body and the code snippets of the question. We also propose a classifier that only uses the title and body of the question and has an accuracy of 81.1%. Finally, we propose a classifier of code snippets only that achieves an accuracy of 77.7%. These results show that deploying Machine Learning techniques on the combination of text and the code snippets of a question provides the best performance. These results demonstrate also that it is possible to identify the programming language of a snippet of few lines of source code. We visualize the feature space of two programming languages Java and SQL in order to identify some special properties of information inside the questions in Stack Overflow corresponding to these languages.
FFT Convolutions are Faster than Winograd on Modern CPUs, Here is Why

Winograd-based convolution has quickly gained traction as a preferred approach to implement convolutional neural networks (ConvNet) on various hardware platforms because it requires fewer floating point operations than FFT-based or direct convolutions. This paper compares three highly optimized implementations (regular FFT--, Gauss--FFT--, and Winograd--based convolutions) on modern multi-- and many--core CPUs. Although all three implementations employed the same optimizations for modern CPUs, our experimental results with two popular ConvNets (VGG and AlexNet) show that the FFT--based implementations generally outperform the Winograd--based approach, contrary to the popular belief. To understand the results, we use a Roofline performance model to analyze the three implementations in detail, by looking at each of their computation phases and by considering not only the number of floating point operations, but also the memory bandwidth and the cache sizes. The performance analysis explains why, and under what conditions, the FFT--based implementations outperform the Winograd--based one, on modern CPUs.
Feasibility Analysis of Oxygen-Glucose Deprivation-Nutrition Resumption on H9c2 Cells In vitro Models of Myocardial Ischemia-Reperfusion Injury

Background: Oxygen-glucose deprivation-nutrition resumption (OGD-NR) models on H9c2 cells are commonly used in vitro models of simulated myocardial ischemia-reperfusion injury (MIRI), but no study has assessed whether these methods for establishing in vitro models can effectively imitate the characteristics of MIRI in vivo. This experiment was designed to analyze the feasibility of six OGD-NR models of MIRI. Methods: By searching the PubMed database using the keywords “myocardial reperfusion injury H9c2 cells,” we obtained six commonly used OGD-NR in vitro models of MIRI performed on H9c2 cells from more than 400 published papers before January 30, 2017. For each model, control (C), simulated ischemia (SI), and simulated ischemia-reperfusion (SIR) groups were assigned, and cell morphology, lactate dehydrogenase (LDH) release, adenosine triphosphate (ATP) levels, reactive oxygen species (ROS), mitochondrial membrane potential (MMP), and inflammatory cytokines were examined to evaluate the characteristics of cell injury. Subsequently, a coculture system of cardiomyocyte-endothelial-macrophage was constructed. The coculture system was dealt with SI and SIR treatments to test the effect on cardiomyocytes survival. Results: For models 1, 2, 3, 4, 5, and 6, SI treatment caused morphological damage to cells, and subsequent SIR treatment did not cause further morphological damage. In the models 1, 2, 3, 4, 5 and 6, LDH release was significantly higher in the SI groups than that in the C group (P < 0.05), and was significantly lower in the SIR groups than that in the SI groups (P < 0.05), except for no significant differences in the LDH release between C, SI and SIR groups in model 6 receiving a 3-h SI treatment. In models 1, 2, 3, 4, 5, and 6, compared with the C group, ATP levels of the SI groups significantly decreased (P < 0.05), ROS levels increased (P < 0.05), and MMP levels decreased (P < 0.05). Compared with the SI group, ATP level of the SIR groups was significantly increased (P < 0.05), and there was no significant ROS production, MMP collapse, and over inflammatory response in the SIR groups. In a coculture system of H9c2 cells-endothelial cells-macrophages, the proportion of viable H9c2 cells in the SIR groups was not reduced compared with the SI groups. Conclusion: All the six OGD-NR models on H9c2 cells in this experiment can not imitate the characteristics of MIRI in vivo and are not suitable for MIRI-related study.
Time‐lapse cameras reveal latitude and season influence breeding phenology durations in penguins

Abstract Variation in the phenology of avian taxa has long been studied to understand how a species reacts to environmental changes over both space and time. Penguins (Sphenicidae) serve as an important example of how biotic and abiotic factors influence certain stages of seabird phenology because of their large ranges and the extreme, dynamic conditions present in their Southern Ocean habitats. Here, we examined the phenology of gentoo (Pygoscelis papua) and chinstrap penguins (Pygoscelis antarctica) at 17 sites across the Scotia arc, including the first documented monitoring of phenology on the South Sandwich Islands, to determine which breeding phases are intrinsic, or rather vary across a species range and between years. We used a novel method to measure seabird breeding phenology and egg and chick survival: time‐lapse cameras. Contrary to the long‐standing theory that these phases are consistent between colonies, we found that latitude and season had a predominant influence on the length of the nest establishment, incubation, and guard durations. We observe a trend toward longer incubation times occurring farther south, where ambient temperatures are colder, which may indicate that exposure to cold slows embryo growth. Across species, in colonies located farther south, parents abandoned nests later when eggs were lost or chicks died and the latest record of eggs or chicks in the nest occurred earlier during the breeding period. The variation in both space and time observed in penguin phenology provides evidence that the duration of phases within the annual cycle of birds is not fundamental, or genetic, as previously understood. Additionally, the recorded phenology dates should inform field researchers on the best timing to count colonies at the peak of breeding, which is poorly understood.
Intractable Likelihood Regression for Covariate Shift by Kernel Mean Embedding

Simulation plays an essential role in comprehending a target system in many fields of social and industrial sciences. A major task in simulation is the estimation of parameters, and optimal parameters to express the observed data need to directly elucidate the properties of the target system as the design of the simulator is based on the expert's domain knowledge. However, skilled human experts struggle to find the desired parameters.Data assimilation therefore becomes an unavoidable task in simulator design to reduce the cost of simulator optimization. Another necessary task is extrapolation; in many practical cases, the prediction based on simulation results will be often outside of the dominant range of the given data area, and this is referred to as the covariate shift. This paper focuses on the regression problem with the covariate shift. While the parameter estimation for the covariate shift has been studied thoroughly in parametric and nonparametric settings, conventional statistical methods of parameter searching are not applicable in the data assimilation of the simulation owing to the properties of the likelihood function: intractable or nondifferentiable. To address these problems, we propose a novel framework of Bayesian inference based on kernel mean embedding that comprises an extended kernel approximate Bayesian computation (ABC) of the importance weighted regression, kernel herding, and the kernel sum rule. This framework makes the prediction available in covariate shift situations, and its effectiveness is evaluated in both synthetic numerical experiments and a widely used production simulator.
A cross-sectional study of the welfare of calves raised in smallholder dairy farms in Meru, Kenya, 2017

Aim: This study was aimed at describing calf comfort and determining the individual and pen level factors that affect comfort status (in particular, calf leg hygiene scores) of smallholder dairy farms in Meru County, Kenya. Materials and Methods: A cross-sectional study was carried out on 52 calves that were up to 1 year old in 38 dairy farms (mean±standard deviation: Herd size=1.71±0.7 milking cows and milk production=6.7±3.1 L/day) in Meru, Kenya, in 2017, with the intention to describe their comfort and determine the factors associated with leg hygiene as a critical parameter for calf comfort assessment. Calves’ biodata, health status, and leg hygiene were assessed, along with pen characteristics such as area, hygiene, and knee impact and knee wetness scores, while a questionnaire was administered to the farmers to gather information regarding calf housing management practices in the farm. Results: The calves had a mean body weight of 85.2±32.8 kg and average daily weight gain of 0.50±0.45 kg per day. 71% of calves had a good body condition score (≥2.5), and the mean space allowance per calf was 2.52±1.56 m2. Approximately 75% of the calves (39/52) were kept in pens, and the rest were reared outdoors. For 39 calves kept indoors, 26% (10/39) of them had wooden or concrete floors while 74% (29/39) had dirt floors. Nearly two-thirds (62%) of indoor calves (26/39) were reared in pens with bedding, and 23% (9/39) and 33% (13/39) of the calves reared indoors were kept in pens displaying a failed knee impact test and failed knee wetness test. Indoor housed calves had an increased probability of having dirty calf legs (cleanliness score of >2.5) by 8.6 times (p=0.031), compared to outdoor-housed calves. In the final multivariable logistic regression model of 39 calves in pens, concrete or wood floors (odds ratio [OR]=7.9, p=0.047), poor body condition (OR=17.1, p=0.020) and use of bedding (OR=12.5, p=0.046) appeared to be positively correlated with dirtiness of calf legs, compared to dirt floors, good body condition, and no bedding, respectively. Conclusion: Overall, some calf comfort aspects were covered for the majority of calves examined, but 69% of the pens were categorized as dirty, especially those with wooden or concrete floors and poor bedding management. Smallholder dairy farmers in Kenya should be trained on calf housing management to improve calf comfort and productivity.
CuII(atsm) Attenuates Neuroinflammation

Background: Neuroinflammation and biometal dyshomeostasis are key pathological features of several neurodegenerative diseases, including Alzheimer’s disease (AD). Inflammation and biometals are linked at the molecular level through regulation of metal buffering proteins such as the metallothioneins. Even though the molecular connections between metals and inflammation have been demonstrated, little information exists on the effect of copper modulation on brain inflammation. Methods: We demonstrate the immunomodulatory potential of the copper bis(thiosemicarbazone) complex CuII(atsm) in an neuroinflammatory model in vivo and describe its anti-inflammatory effects on microglia and astrocytes in vitro. Results: By using a sophisticated in vivo magnetic resonance imaging (MRI) approach, we report the efficacy of CuII(atsm) in reducing acute cerebrovascular inflammation caused by peripheral administration of bacterial lipopolysaccharide (LPS). CuII(atsm) also induced anti-inflammatory outcomes in primary microglia [significant reductions in nitric oxide (NO), monocyte chemoattractant protein 1 (MCP-1), and tumor necrosis factor (TNF)] and astrocytes [significantly reduced NO, MCP-1, and interleukin 6 (IL-6)] in vitro. These anti-inflammatory actions were associated with increased cellular copper levels and increased the neuroprotective protein metallothionein-1 (MT1) in microglia and astrocytes. Conclusion: The beneficial effects of CuII(atsm) on the neuroimmune system suggest copper complexes are potential therapeutics for the treatment of neuroinflammatory conditions.
A statisztika módszertani lehetőségeinek alkalmazása az üzleti elemzési eljárásokban

A gazdasagi esemenyek osszetettsege, az adatsokasagok oriasi merete egyre jobban megkoveteli azokat a matematikai es statisztikai modszertani eljarasokat, eszkozoket, amelyekkel az ilyen elemzesekben fel- tarhatok lesznek azok az informaciok, amelyek tenylegesen tamogatjak a vezetes donteseit. A cikkben a szerzok bemutatjak a mintavetelezes fo alapelveit es a fobb mintavetelezesi modokat, azok elonyeit es hat- ranyit annak erdekeben, hogy megkonnyitsek a felhasznaloi gyakorlati munkat a controllerek szamara.