Miracle-dz/UniFAR-FTUs huggingface dataset:
GARSA: genomic analysis resources for sequence annotation

SUMMARY Growth of genome data and analysis possibilities have brought new levels of difficulty for scientists to understand, integrate and deal with all this ever-increasing information. In this scenario, GARSA has been conceived aiming to facilitate the tasks of integrating, analyzing and presenting genomic information from several bioinformatics tools and genomic databases, in a flexible way. GARSA is a user-friendly web-based system designed to analyze genomic data in the context of a pipeline. EST and GGS data can be analyzed using the system since it accepts (1) chromatograms, (2) download of sequences from GenBank, (3) Fasta files stored locally or (4) a combination of all three. Quality evaluation of chromatograms, vector removing and clusterization are easily performed as part of the pipeline. A number of local and customizable Blast and CDD analyses can be performed as well as Interpro, complemented with phylogeny analyses. GARSA is being used for the analyses of Trypanosoma vivax (GSS and EST), Trypanosoma rangeli (GSS, EST and ORESTES), Bothrops jararaca (EST), Piaractus mesopotamicus (EST) and Lutzomyia longipalpis (EST). AVAILABILITY The GARSA system is freely available under GPL license (http://www.biowebdb.org/garsa/). For download requests visit http://www.biowebdb. org/garsa/ or contact Dr Alberto Dávila.
Lost and not Found: An Investigation of Recovery Methods for Multi-Factor Authentication

Multi-Factor Authentication is intended to strengthen the security of password-based authentication by adding another factor, such as hardware tokens or one-time passwords using mobile apps. However, this increased authentication security comes with potential drawbacks that can lead to account and asset loss. If users lose access to their additional authentication factors for any reason, they will be locked out of their accounts. Consequently, services that provide Multi-Factor Authentication should deploy procedures to allow their users to recover from losing access to their additional factor that are both secure and easy-to-use. To the best of our knowledge, we are the first to first-hand investigate the security and user experience of deployed Multi-Factor Authentication recovery procedures. We first evaluate the official help and support pages of 1,303 websites that provide Multi-Factor Authentication and collect documented information about their recovery procedures. Second, we select a subset of 71 websites, create accounts, set up Multi-Factor Authentication, and perform an in-depth investigation of their recovery procedure security and user experience. We find that many websites deploy insecure Multi-Factor Au-thentication recovery procedures and allowed us to circumvent and disable Multi-Factor Authentication when having access to the accounts’ associated email addresses. Furthermore, we commonly observed discrepancies between our in-depth analysis and the official help and support pages, implying that information meant to aid users is often either incorrect or outdated.
Integrating UIMA Annotators in a Web-based Text Processing Framework

The Unstructured Information Management Architecture (UIMA) [1] framework is a growing platform for natural language processing (NLP) applications. However, such applications may be difficult for non-technical users deploy. This project presents a web-based framework that wraps UIMA-based annotator systems into a graphical user interface for researchers and clinicians, and a web service for developers. An annotator that extracts data elements from lung cancer radiology reports is presented to illustrate the use of the system. Annotation results from the web system can be exported to multiple formats for users to utilize in other aspects of their research and workflow. This project demonstrates the benefits of a lay-user interface for complex NLP applications. Efforts such as this can lead to increased interest and support for NLP work in the clinical domain.
An assessment of the applicability of dung count to estimate the wild boar population density in a forest environment

Wild boar is a native species in the fauna of the Czech Republic. It is becoming a problematic game species both in the Czech Republic and in other European countries due to its harmful impacts. So far no suitable method of its sustainable management has been devised. Yet, a correct abundance estimate is essential for proper management, field crop damage prevention and proper hunting planning. This paper employed faecal pellet group count to estimate the wild boar density in a forest environment. The research was conducted in a study area covering 12.83 km 2 , which is surrounded by farmland. The count was done under ideal conditions in early spring, after snow had melted. The study area was organised into several differing environments in which different dung densities were expected. The presence of feeding sites, where wild boars concentrated, was taken into account. The conducted count confirmed differences in the utilization of different habitats within the study area. The resulting wild boar abundance was estimated at 64.3 (± 8.9; 95% CI) indd·km -2 .
Geodynamic inversion to constrain the non-linear rheology of the lithosphere

A common method to determine the strength of the lithosphere is through estimating its effective elastic thickness from the coherence between gravity and topography. This method assumes a priori that the lithosphere is a thin elastic plate floating on a viscous mantle. Whereas this seems to work well with oceanic plates, it has given controversial results in continental collision zones. Usually, continental collisions zones are well-studied areas for which additional geophysical datasets such as receiver functions and seismic tomography exist that constrain the geometry of the lithosphere and often show that it is rather complex. Yet, lithospheric geometry by itself is insufficient to understand the dynamics of the lithosphere, as this also requires knowledge of the rheology of the lithosphere. Experimental results show significant variability between various rock types and there are large uncertainties in extrapolating laboratory values to nature, which leaves room for speculation.
DeepInSAR - A Deep Learning Framework for SAR Interferometric Phase Restoration and Coherence Estimation

Over the past decade, using Interferometric Synthetic Aperture Radar (InSAR) remote sensing technology for ground displacement detection has become very successful. However, during the acquisition stage, microwave signals reflected from the ground and received by the satellite are contaminated, for example, due to undesirable material reflectance and atmospheric factors, and there is no clean ground truth to discriminate these noises, which adversely affect InSAR phase computation. Accurate InSAR phase filtering and coherence estimation are crucial for subsequent processing steps. Current methods require expert supervision and expensive runtime to evaluate the quality of intermediate outputs, limiting the usability and scalability in practical applications, such as wide area ground displacement monitoring and predication. We propose a deep convolutional neural network based model DeepInSAR to intelligently solve both phase filtering and coherence estimation problems. We demonstrate our model’s performance using simulated and real data. A teacher-student framework is introduced to handle the issue of missing clean InSAR ground truth. Quantitative and qualitative evaluations show that our teacher-student approach requires less input but can achieve better results than its stack-based teacher method even on new unseen data. The proposed DeepInSAR also outperforms three other top non-stack based methods in time efficiency without human supervision.
Overcommitment in Cloud Services: Bin Packing with Chance Constraints

This paper considers a traditional problem of resource allocation: scheduling jobs on machines. One such recent application is cloud computing; jobs arrive in an online fashion with capacity requirements and need to be immediately scheduled on physical machines in data centers. It is often observed that the requested capacities are not fully utilized, hence offering an opportunity to employ an overcommitment policy, that is, selling resources beyond capacity. Setting the right overcommitment level can yield a significant cost reduction for the cloud provider while only inducing a very low risk of violating capacity constraints. We introduce and study a model that quantifies the value of overcommitment by modeling the problem as bin packing with chance constraints. We then propose an alternative formulation that transforms each chance constraint to a submodular function. We show that our model captures the risk pooling effect and can guide scheduling and overcommitment decisions. We also develop a family of online algorithms that are intuitive, easy to implement, and provide a constant factor guarantee from optimal. Finally, we calibrate our model using realistic workload data and test our approach in a practical setting. Our analysis and experiments illustrate the benefit of overcommitment in cloud services and suggest a cost reduction of 1.5% to 17%, depending on the provider’s risk tolerance. The online appendices are available at https://doi.org/10.1287/mnsc.2018. 3091 . This paper was accepted by Yinyu Ye, optimization.
Investigating Environmental Flows for Riparian Vegetation Recruitment Using System Dynamics Modelling

System dynamics modelling is an underutilized tool that can provide flexible and system‐response analyses of environmental flows. Because system dynamics models are able to incorporate causal connections between social, economic and environmental aspects of a system, they are well suited to study water management problems. In addition, system dynamics models are excellent tools for incorporating expert and stakeholder feedback, which is an important component of any environmental flow study. Given the benefits of using a system dynamics modelling approach, our objective was to develop and demonstrate a stochastic system dynamics modelling framework to evaluate environmental flow alternatives. Specifically, our research examined the influence of flow alternatives on cottonwood recruitment and reservoir storage within the Rio Chama basin, New Mexico, USA. We used the ‘recruitment box model’ to investigate the impact of three alternatives on cottonwood recruitment within the project reach. The recruitment box model attributes seedling survival to floodplain elevation, annual timing of peak flows and river stage declines that match seedling root growth. We also demonstrate how flow alternatives can be evaluated using comparative metrics, which allow water resource managers to more easily evaluate alternatives before incorporating environmental flows into existing operations. Copyright © 2014 John Wiley & Sons, Ltd.
Constraints on aerosol sources using GEOS‐Chem adjoint and MODIS radiances, and evaluation with multisensor (OMI, MISR) data

We present a new top‐down approach that spatially constrains the amount of aerosol emissions using satellite (Moderate Resolution Imaging Spectroradiometer (MODIS)) observed radiances with the adjoint of a chemistry transport model (GEOS‐Chem). This paper aims to demonstrate the approach through applying it to a case study that yields the following emission estimates over China for April 2008: 1.73 Tg for SO2, 0.72 Tg for NH3, 1.38 Tg for NOx, 0.10 Tg for black carbon, and 0.18 Tg for organic carbon from anthropogenic sources, which reflects, respectively, a reduction of 33.5%, 34.5%, 18.8%, 9.1%, and 15% in comparison to the prior bottom‐up inventories of INTEX‐B 2006. The mineral dust emission from the online dust entrainment and mobilization module is reduced by 56.4% of 19.02 to 8.30 Tg. Compared to the prior simulation, the posterior simulation shows a much better agreement with the following independent measurements: aerosol optical depth (AOD) measured by AERONET sun‐spectrophotometers and retrieved from Multi‐angle Imaging SpectroRadiometer (MISR), atmospheric NO2 and SO2 columnar amount retrieved from Ozone Monitoring Instrument (OMI), and in situ data of sulfate‐nitrate‐ammonium and PM10 (particular matter with aerodynamic diameter less than 10 µm) mass concentrations over both anthropogenic pollution and dust source regions. Assuming the bottom‐up (prior) anthropogenic emissions are the best estimates for their base year of 2006, the overwhelming reduction in the posterior (top‐down) estimate indicates less emission in April 2008 especially for the SO2 tracer in the central and eastern parts of China, and/or an overestimation in the prior emission. The former is supported by the AOD change detected by MODIS and MISR sensors, while the latter is likely the case for NOx and NH3 emissions because no evidence shows that their atmospheric concentration has declined over China. With the promising results shown in this study, continuous efforts are needed toward a holistic and comprehensive inversion of emission using multisensor remote sensing data (of trace gases and aerosols) for constraining aerosol primary and precursor emissions at various temporal and spatial scales.
A near-optimal stochastic MIMO signal detection with a mixture of t-distribution prior

—Multiple-input multiple-output (MIMO) systems are a promising key technology for future wireless communication. However, improving their signal detection performance is still challenging to further increase the wireless transmission efﬁ-ciency. To address this challenge, we propose to intentionally extend the discrete signal detection problem in MIMO systems to a continuous one and to utilize the Hamiltonian Monte Carlo method, a type of efﬁcient Markov chain Monte Carlo (MCMC). We already presented the use of a mixture of normal distribution for the prior distribution based on the same approach. This paper proposes the application of a mixture of t -distribution that further improves detection performance. We show that the proposed method can achieve near-optimal signal detection with a polynomial order computational complexity through theoretical analysis and computer simulation. The proposed high-performance and pragmatic MIMO signal detection should signiﬁcantly contribute to developing the 6th generation mobile network.
Are Out-of-Distribution Detection Methods Reliable?

This paper establishes a novel evaluation framework for assessing the performance of out-of-distribution (OOD) detection in realistic settings. Our goal is to expose the short-comings of existing OOD detection benchmarks and encourage a necessary research direction shift toward satisfying the requirements of real-world applications. We expand OOD detection research by introducing new OOD test datasets CIFAR-10-R, CIFAR-100-R, and MVTec-R, which allow researchers to benchmark OOD detection performance under realistic distribution shifts. We also introduce a generalizability score to measure a method’s ability to generalize from standard OOD detection test datasets to a realistic setting. Contrary to existing OOD detection research, we demonstrate that further performance improvements on standard benchmark datasets do not increase the usability of such models in the real world. State-of-the-art (SOTA) methods tested on our realistic distributionally-shifted datasets drop in performance for up to 45%. This setting is critical for evaluating the reliability of OOD models before they are deployed in real-world environments.
On the role of dissipation in haptic systems

Passivity theory has been used for the past decade to derive stability conditions for human/machine interface applications. Demonstrating passivity of the haptic display implies stable and safe interaction for the human user. At the heart of the stability analysis is the physical dissipation provided by the haptic device, as it plays a key role in the design process for all components. This paper will derive the condition that the haptic device must satisfy in order to achieve passivity of the haptic display. These results will be used to investigate a general nonlinear device model.
PROMISE Participative Research labOratory for Multimedia and Multilingual Information Systems Evaluation Deliverable 3 . 6 Semantic Representation and Enrichment of Information Retrieval Experimental Data

Evaluation campaigns contribute considerably to the advancement of information retrieval systems. Structured data as well as unstructured data, in the form of scientiﬁc publications, is produced in this process, in a variety of application areas. In this work we present several steps in the direction of semantically annotating and interlinking these datsets, with the goal of enhancing their interpretation, sharing
START: an automated tool for serial analysis of chromatin occupancy data

UNLABELLED The serial analysis of chromatin occupancy technique (SACO) promises to become a widely used method for the unbiased genome-wide experimental identification of loci bound by a transcription factor of interest. We describe the first web-based automatic tool, termed sequence tag analysis and reporting tool (START), for processing SACO data generated by experiments performed for the yeast, fruit fly, mouse, rat or human genomes. The program uses as input sequences of inserts from a SACO library from which it extracts all SACO tags, maps them to genomic locations and annotates them. START returns detailed information about these tags including the genes, the genomic elements and the miRNA precursors found in their vicinity, and makes use of the MAPPER database to identify putative transcription factor binding sites located close to the tags. AVAILABILITY The program is available at http://bio.chip. org/start/. SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION is available at http://bio.chip. org/doc/start/START-supplementary.pdf
Public perceptions of climate change in Wales: Summary findings of a survey of the Welsh public conducted during November and December 2012

Climate change presents a formidable challenge to societies across the globe. The infrastructure, economy, natural environment, and health and wellbeing of people in Wales are each expected to be affected by climate change in the coming years. In its policies, the Welsh Government has asserted its commitment to urgent and sustained action to cut greenhouse gas emissions. Whilst the case for action on mitigation remains, there is also a clear need to develop effective adaptation approaches in anticipation of consequences, which have become unavoidable in Wales and elsewhere. However, mitigation and adaptation efforts are unlikely to be fully effective without significant awareness, engagement and buy-in from the population of Wales as a whole. This report examines public perceptions of climate change in Wales at the end of the year 2012, using a nationally representative survey (n=1,001) of the Welsh public. The main aims of the survey were to examine people’s views regarding the reality of and human contribution to climate change, their concerns about the impacts of climate change, perspectives on climate change adaptation, and attitudes towards policy and behaviour change. As this survey was conducted at the end of a year when Wales experienced serious levels of high rainfall and associated flooding, the link between people’s experience of flooding and climate change perceptions is also explored, including by the use of an additional oversample in a heavily affected part of Wales (n=100).
The αμ Search Algorithm for the Game of Bridge

{\alpha}{\mu} is an anytime heuristic search algorithm for incomplete information games that assumes perfect information for the opponents. {\alpha}{\mu} addresses the strategy fusion and non-locality problems encountered by Perfect Information Monte Carlo sampling. In this paper {\alpha}{\mu} is applied to the game of Bridge.
Underlay Device-to-Device Communications on Multiple Channels

Since the spectral efficiency of wireless communications is already close to its fundamental bounds, a significant increase in spatial efficiency is required to meet future traffic demands. Device-to-device (D2D) communications provide such an increase by allowing nearby users to communicate directly without passing their packages through the base station. To fully exploit the benefits of this paradigm, proper channel assignment and power allocation algorithms are required. The main limitation of existing schemes, which restrict D2D transmitters to operate on a single channel at a time, is circumvented by the joint channel assignment and power allocation algorithm proposed in this paper. This algorithm relies on convex relaxation to efficiently obtain nearly-optimal solutions to the mixed-integer program arising in this context. Numerical experiments corroborate the merits of the proposed scheme relative to state-of-the art alternatives.
SAR2SAR: A Semi-Supervised Despeckling Algorithm for SAR Images

Speckle reduction is a key step in many remote sensing applications. By strongly affecting synthetic aperture radar (SAR) images, it makes them difficult to analyze. Due to the difficulty to model the spatial correlation of speckle, a deep learning algorithm with semi-supervision is proposed in this article: SAR2SAR. Multitemporal time series are leveraged and the neural network learns to restore SAR images by only looking at noisy acquisitions. To this purpose, the recently proposed noise2noise framework [1] has been employed. The strategy to adapt it to SAR despeckling is presented, based on a compensation of temporal changes and a loss function adapted to the statistics of speckle. A study with synthetic speckle noise is presented to compare the performances of the proposed method with other state-of-the-art filters. Then, results on real images are discussed, to show the potential of the proposed algorithm. The code is made available to allow testing and reproducible research in this field.
Trace fossils as environmental indicators in the Corallian of England and Normandy

The distribution of trace fossils in Corallian (Upper Jurassic) rocks has been studied in three coastal areas in Yorkshire, Dorset, and Normandy. Three trace fossil associations, primarily related to the hydrodynamic conditions and only indirectly to depth, can be distinguished: the high-energy Diplocraterion association, and the low-energy Rhizocorallium and Teichichnus associations. The characteristic members of these associations can be used as environmental indicators throughout the Jurassic.
HILDA: A Discourse Parser Using Support Vector Machine Classification

Discourse structures have a central role in several computational tasks, such as question–answering or dialogue generation. In particular, the framework of the Rhetorical Structure Theory (RST) offers a sound formalism for hierarchical text organization. In this article, we present HILDA, an implemented discourse parser based on RST and Support Vector Machine (SVM) classiﬁcation. SVM classiﬁers are trained and applied to discourse segmentation and relation labeling. By combining labeling with a greedy bottom-up tree building approach, we are able to create accurate discourse trees in linear time complexity with respect to the length of the input text. Importantly, our parser can parse entire texts, whereas the publicly available parser SPADE (Soricut and Marcu, 2003) is limited to sentence level analysis. HILDA outperforms other discourse parsers for tree structure construction and discourse relation labeling. For the discourse parsing task, our system reaches 78.3% of the performance level of human annotators. Compared to a state-of-the-art rule-based discourse parser, our system achieves an performance increase of 11.6%.