Miracle-dz/UniFAR-FTUs huggingface dataset:
Variational Minimax Estimation of Discrete Distributions under KL Loss

We develop a family of upper and lower bounds on the worst-case expected KL loss for estimating a discrete distribution on a finite number m of points, given N i.i. d. samples. Our upper bounds are approximation-theoretic, similar to recent bounds for estimating discrete entropy; the lower bounds are Bayesian, based on averages of the KL loss under Dirichlet distributions. The upper bounds are convex in their parameters and thus can be minimized by descent methods to provide estimators with low worst-case error; the lower bounds are indexed by a one-dimensional parameter and are thus easily maximized. Asymptotic analysis of the bounds demonstrates the uniform KL-consistency of a wide class of estimators as c = N/m → ∞ (no matter how slowly), and shows that no estimator is consistent for c bounded (in contrast to entropy estimation). Moreover, the bounds are asymptotically tight as c → 0 or ∞, and are shown numerically to be tight within a factor of two for all c. Finally, in the sparse-data limit c → 0, we find that the Dirichlet-Bayes (add-constant) estimator with parameter scaling like - c log(c) optimizes both the upper and lower bounds, suggesting an optimal choice of the "add-constant" parameter in this regime.
A temporally adaptive classifier for multispectral imagery

This paper presents a new temporally adaptive classification system for multispectral images. A spatial-temporal adaptation mechanism is devised to account for the changes in the feature space as a result of environmental variations. Classification based upon spatial features is performed using Bayesian framework or probabilistic neural networks (PNNs) while the temporal updating takes place using a spatial-temporal predictor. A simple iterative updating mechanism is also introduced for adjusting the parameters of these systems. The proposed methodology is used to develop a pixel-based cloud classification system. Experimental results on cloud classification from satellite imagery are provided to show the usefulness of this system.
GOTrees: Predicting GO Associations from Protein Domain Composition Using Decision Trees

The Gene Ontology (GO) offers a comprehensive and standardized way to describe a protein's biological role. Proteins are annotated with GO terms based on direct or indirect experimental evidence. Term assignments are also inferred from homology and literature mining. Regardless of the type of evidence used, GO assignments are manually curated or electronic. Unfortunately, manual curation cannot keep pace with the data, available from publications and various large experimental datasets. Automated literature-based annotation methods have been developed in order to speed up the annotation. However, they only apply to proteins that have been experimentally investigated or have close homologs with sufficient and consistent annotation. One of the homology-based electronic methods for GO annotation is provided by the InterPro database. The InterPro2GO/PFAM2GO associates individual protein domains with GO terms and thus can be used to annotate the less studied proteins. However, protein classification via a single functional domain demands stringency to avoid large number of false positives. This work broadens the basic approach. We model proteins via their entire functional domain content and train individual decision tree classifiers for each GO term using known protein assignments. We demonstrate that our approach is sensitive, specific and precise, as well as fairly robust to sparse data. We have found that our method is more sensitive when compared to the InterPro2GO performance and suffers only some precision decrease. In comparison to the InterPro2GO we have improved the sensitivity by 22%, 27% and 50% for Molecular Function, Biological Process and Cellular GO terms respectively.
Land cover in Norway based on an area frame survey of vegetation types

ABSTRACT The Norwegian area frame survey of land cover and outfield land resources (AR18X18), completed in 2014, provided unbiased statistics of land cover in Norway. The article reports the new statistics, discusses implications of the data set, and provides potential value in terms of research, management, and monitoring. A gridded sampling design for 1081 primary statistical units of 0.9 km2 at 18 km intervals was implemented in the survey. The plots were mapped in situ, aided by aerial photos, and all areas were coded following a vegetation type system. The results provide new insights into the cover and distribution of vegetation and land cover types. The statistic for mire and wetlands, which previously covered 5.8%, has since been corrected to 8.9%. The survey results can be used for environmental and agricultural management, and the data can be stratified for regional analyses. The survey data can also serve as training data for remote sensing and distribution modelling. Finally, the survey data can be used to calibrate vegetation perturbations in climate change research that focuses on atmospheric–vegetation feedback. The survey documented novel land cover statistics and revealed that the national cover of wetlands had previously been underestimated.
HOMOLOGICAL SENSOR NETWORKS

A sensor is a device which measures some feature of a domain or environment and returns a signal from which information may be extracted. Sensors vary in scope, resolution, and ability. The information they return can be as simple as a binary flag, as with a metal detector which beeps to indicate a detection threshold being crossed. A more complex sensor, such as a video camera, can return a signal requiring sophisticated analysis to extract relevant data. An increasingly common application for sensors is to scan a region for a particular object or substance. For example, one might wish to determine the existence and location of an outbreak of fire in a national forest. Questions of more interest to national security involve detection of radiological or biological hazards, hidden mines and munitions, or specific individuals in a crowd. All of these scenarios pose difficult and challenging data management problems. Numerous strategies exist, aided by the fact that sensor technology provides an expansive array of available hardware. A fundamental dichotomy exists in the approach to sensing an environment based on the number and complexity of sensors. For a fixed cost (monetary, or, perhaps, ‘total complexity’), one can deploy a small number of sophisticated ‘global’ sensors with high signal complexity and precise readings. In contrast, one can deploy a large number of small, coarse, ‘local’ devices which may have large uncertainties in their readings. Different strategies are appropriate for different tasks. The human body contains examples of sensor systems with a small multiplicity of highly complex devices (for sight) as well as vast networks of local sensors (for touch).
Restricted Boltzmann Machine based detection system for DDoS attack in Software Defined Networks

Software Defined Network is an innovative network architecture which provides network control through software logic. It decouples control and data plane to customize the network according to the user needs. OpenFlow, a standardized network protocol acts as an interface between controllers and switches. The softwarized controllers are highly vulnerable for Distributed Denial of Service attacks. The proposed detection system uses an unsupervised stochastic Restricted Boltzmann Machine algorithm to self-learn the reliable network metrics. This algorithm detects and classifies the type of DDoS attacks in a dynamic network environment by framing a new context. The results prove that RBM based DDoS detection system achieves higher accuracy than the existing methods.
On the Distribution of Software Faults

The Pareto principle is often used to describe how faults in large software systems are distributed over modules. A recent paper by Andersson and Runeson again confirmed the Pareto principle of fault distribution. In this paper, we show that the distribution of software faults can be more precisely described as the Weibull distribution.
DEFAULTABLE GAME OPTIONS IN A HAZARD PROCESS MODEL

The valuation and hedging of defaultable game options is studied in a hazard process model of credit risk. A convenient pricing formula with respect to a reference filteration is derived. A connection of arbitrage prices with a suitable notion of hedging is obtained. The main result shows that the arbitrage prices are the minimal superhedging prices with sigma martingale cost under a risk neutral measure.
An approach for calculating all stable PID parameters for time delay systems with uncertain parameters

Abstract There exists a large number of PID tuning rules for LTI systems. However, these rules often use time delay approximations and ignore parameter uncertainties. This work updates the classical parameter space approach to an one step PID tuning approach to guarantee robust stability for LTI time delay systems with explicit consideration of uncertainties in the plant parameters and the time delay. The basic idea is to calculate how the root boundaries changes due variation of system parameters. Bands of root boundaries are determined by this analysis. The challenging task of robust stability of time delay systems is converted to an easier minimum/maximum search to estimate the borders of the root boundary bands. The presented method satisfies robust stability with only small conservatism.
BLITZ: Compositional bounded model checking for real-world programs

Bounded Model Checking (BMC) for software is a precise bug-finding technique that builds upon the efficiency of modern SAT and SMT solvers. BMC currently does not scale to large programs because the size of the generated formulae exceeds the capacity of existing solvers. We present a new, compositional and property-sensitive algorithm that enables BMC to automatically find bugs in large programs. A novel feature of our technique is to decompose the behaviour of a program into a sequence of BMC instances and use a combination of satisfying assignments and unsatisfiability proofs to propagate information across instances. A second novelty is to use the control- and data-flow of the program as well as information from proofs to prune the set of variables and procedures considered and hence, generate smaller instances. Our tool BLITZ outperforms existing tools and scales to programs with over 100,000 lines of code. BLITZ automatically and efficiently discovers bugs in widely deployed software including new vulnerabilities in Internet infrastructure software.
Dizzy: Stochastic Simulation of Large-scale Genetic Regulatory Networks

We describe Dizzy, a software tool for stochastically and deterministically modeling the spatially homogeneous kinetics of integrated large-scale genetic, metabolic, and signaling networks. Notable features include a modular simulation framework, reusable modeling elements, complex kinetic rate laws, multi-step reaction processes, steady-state noise estimation, and spatial compartmentalization.
Material recognition CNNs and hierarchical planning for biped robot locomotion on slippery terrain

In this paper we tackle the problem of visually predicting surface friction for environments with diverse surfaces, and integrating this knowledge into biped robot locomotion planning. The problem is essential for autonomous robot locomotion since diverse surfaces with varying friction abound in the real world, from wood to ceramic tiles, grass or ice, which may cause difficulties or huge energy costs for robot locomotion if not considered. We propose to estimate friction and its uncertainty from visual estimation of material classes using convolutional neural networks, together with probability distribution functions of friction associated with each material. We then robustly integrate the friction predictions into a hierarchical (footstep and full-body) planning method using chance constraints, and optimize the same trajectory costs at both levels of the planning method for consistency. Our solution achieves fully autonomous perception and locomotion on slippery terrain, which considers not only friction and its uncertainty, but also collision, stability and trajectory cost. We show promising friction prediction results in real pictures of outdoor scenarios, and planning experiments on a real robot facing surfaces with different friction.
Wedge Resection versus Stereotactic Body Radiation Therapy for Non-Small Cell Lung Cancer Tumors ≤8 mm

The objective of this study was to evaluate the overall survival of patients with ≤8 mm non-small cell lung cancer (NSCLC) who undergo wedge resection versus stereotactic body radiation therapy (SBRT). Kaplan–Meier analysis, multivariable Cox proportional hazards modeling, and propensity score-matched analysis were performed to evaluate the overall survival of patients with ≤8 mm NSCLC in the National Cancer Database (NCDB) from 2004 to 2017 who underwent wedge resection versus patients who underwent SBRT. The above-mentioned matched analyses were repeated for patients with no comorbidities. Patients who were coded in the NCDB as having undergone radiation because surgery was contraindicated due to patient risk factors (e.g., comorbid conditions, advance age, etc.) and those with a history of prior malignancy were excluded from analysis. Of the 1505 patients who had NSCLC ≤8 mm during the study period, 1339 (89%) patients underwent wedge resection, and 166 (11%) patients underwent SBRT. In the unadjusted analysis, multivariable Cox modeling and propensity score-matched analysis, wedge resection was associated with improved survival when compared to SBRT. These results were consistent in a sensitivity analysis limited to patients with no comorbidities.
MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference

Radio frequency (RF) technology has been applied to enable advanced behavioral sensing in human-computer interaction. Due to its device-free sensing capability and wide availability on Internet of Things devices. Enabling finger gesture-based identification with high accuracy can be challenging due to low RF signal resolution and user heterogeneity. In this paper, we propose MeshID, a novel RF-based user identification scheme that enables identification through finger gestures with high accuracy. MeshID significantly improves the sensing sensitivity on RF signal interference, and hence is able to extract subtle individual biometrics through velocity distribution profiling (VDP) features from less-distinct finger motions such as drawing digits in the air. We design an efficient few-shot model retraining framework based on first component reverse module, achieving high model robustness and performance in a complex environment. We conduct comprehensive real-world experiments and the results show that MeshID achieves a user identification accuracy of 95.17% on average in three indoor environments. The results indicate that MeshID outperforms the state-of-the-art in identification performance with less cost.
Evaluation and comparison of hyperspectral temperature and emissivity separation methods influenced by sensor spectral properties

ABSTRACT In this article, great efforts are focused on the temperature and emissivity separation (TES) from hyperspectral thermal infrared data. Instead of proposing a new TES method, several published TES methods, including iterative spectrally smooth temperature–emissivity separation method (ISSTES), automatic retrieval of temperature and emissivity using spectral smoothness method (ARTEMISS), spectral smoothness method (SpSm), downwelling radiance residual index method (DRRI), and linear spectral emissivity constraint method (LSEC), are reviewed. The performances of these TES methods are analysed and compared under different instrument characteristics, including spectral shifting and full-width half-maximum broadening, with the simulated data. The results show that LSEC has the most robust and accurate performance. DRRI also has a good performance, in which a channel selection procedure is required before the use of this method. ISSTES, ARTEMISS, and SpSm show more sensitivities.
Visualizing large-scale telecommunication networks and services

Visual exploration of massive datasets arising from telecommunication networks and services is a challenge. This paper describes SWIFT-3D, an integrated data visualization and exploration system created at AT&T Labs for large scale network analysis. SWIFT-3D integrates a collection of interactive tools that includes pixel-oriented 2D maps, interactive 3D maps, statistical displays, network topology diagrams and an interactive drill-down query interface. Example applications are described, demonstrating a successful application to analyze unexpected network events (high volumes of unanswered calls), and comparison of usage of an Internet service with voice network traffic and local access coverage.
Differential display competitive polymerase chain reaction: An optimal tool for assaying gene expression

Gene discovery, i.e. detection of genes whose expression is affected in diseases or by different treatments of cells or animals, has become the focus of much genetic research. The technologies that are used to detect changes in expression level include polymerase chain reaction (PCR)‐based subtraction methods, arrays of cDNA clones on chips or filters, serial analysis of gene expression, and differential display. In this paper we show that differential display can be used to investigate global gene expression in situations where a few genes change expression levels such as exposure of MCF7 cells to estradiol, and in more complex situations such as neuronal differentiation of human NTERA2 cells which affects a large number of genes. Furthermore, we show that differential display can replace Northern blotting and RNase protection as a tool to study the expression level of a specific gene in many samples. Results obtained by differential display can be stored in databases, where the identit a band (gene or mRNA name) can be linked with information about the primer combination displaying the band and a gel image showing the band pattern, which is all the information that is needed to compare the expression level of this gene in other samples.
Linking multiple biodiversity informatics platforms with Darwin Core Archives

Abstract We describe an implementation of the Darwin Core Archive (DwC-A) standard that allows for the exchange of biodiversity information contained within the Scratchpads virtual research environment with external collaborators. Using this single archive file Scratchpad users can expose taxonomies, specimen records, species descriptions and a range of other data to a variety of third-party aggregators and tools (currently Encyclopedia of Life, eMonocot Portal, CartoDB, and the Common Data Model) for secondary use. This paper describes our technical approach to dynamically building and validating Darwin Core Archives for the 600+ Scratchpad user communities, which can be used to serve the diverse data needs of all of our content partners.
Immunotherapy Using Dendritic Cells against Multiple Myeloma: How to Improve?

Multiple myeloma (MM) is a good target disease in which one can apply cellular immunotherapy, which is based on the graft-versus-myeloma effect. This role of immune effector cells provides the framework for the development of immune-based therapeutic options that use antigen-presenting cells (APCs) with increased potency, such as dendritic cells (DCs), in MM. Current isolated idiotype (Id), myeloma cell lysates, myeloma dying cells, DC-myeloma hybrids, or DC transfected with tumor-derived RNA has been used for immunotherapy with DCs. Immunological inhibitory cytokines, such as TGF-β, IL-10, IL-6 and VEGF, which are produced from myeloma cells, can modulate antitumor host immune response, including the abrogation of DC function, by constitutive activation of STAT3. Therefore, even the immune responses have been observed in clinical trials, the clinical response was rarely improved following DC vaccinations in MM patients. We are going to discuss how to improve the efficacy of DC vaccination in MM.
Features of REM-related Sleep Disordered Breathing in the Japanese Population.

OBJECTIVE Rapid eye movement (REM)-related sleep disordered breathing (SDB) is an entity in which the cessation or reduction of breathing occurs primarily during the REM period. Most studies have shown that REM-related SDB more frequently affects women, younger people and patients with mild or moderate SDB. The aim of this study was to prospectively investigate the prevalence and features of REM-related SDB in Japanese subjects compared with the findings of previous reports. METHODS A total of 468 patients were evaluated in this study. The diagnosis of SDB was established using polysomnographic monitoring. The patient variables included age, gender, body characteristics, comorbidities, etc. RESULTS REM-related SDB was more prevalent in women than non-REM-related SDB (male ratio; 66.3% vs. 79.5%, p=0.03). Moreover, the patients with REM-related SDB had lower body mass indexes (25.9 ± 6.9 vs. 28.5 ± 7.7; p=0.003), arousal indexes (31.8 ± 10.7 vs. 61.0 ± 29.1; p<0.001), apnea hypopnea indexes (15.0 ± 8.0 vs. 54.9 ± 35.9) and glycosylated hemoglobin (HbA1c) levels (5.5 ± 0.9 vs. 5.9 ± 2.6; p=0.02) than the patients with non-REM-related SDB. However, the overall and female gender prevalence of REM-related SDB among the Japanese subjects was lower than that shown in previous reports. The finding that REM-related SDB was not prevalent in younger individuals or severely obese patients was not consistent with the results of previous studies. CONCLUSION The present findings suggest that REM-related SDB may have different clinical characteristics in the Japanese population than that observed in previous reports.
High‐Severity Burning Increases Jack Pine Seedling Biomass Relative to Low‐Severity Prescribed Fires

Within the state of Wisconsin, there is a gradient of serotiny in Pinus banksiana Lamb. (jack pine). With prescribed burning becoming more common in Wisconsin, understanding the dynamic and variable effects of burning on soil properties and their interactions with post-fire plant communities is critical. Our objective was to isolate and examine how fire effects on soil properties affect P. banksiana seed germination and seedling growth and establishment. We investigated these effects in two pot experiments and an intact paired core experiment, maintained in a greenhouse for 19 to 24 wk. Soils from the O and A horizons were collected from Coon Fork Barrens, Eau Claire County, WI, before and after a prescribed fire, and O horizon soil was burned in the laboratory to mimic a high-severity wildfire. In the intact core experiment, seed germination and seedling aboveground mass were not affected by prescribed burning. In the pot experiments, jack pine seedlings produced more biomass in laboratory-burned soils than prescribed-burned soils, and seedlings from northwest seed lots consistently had a higher frequency of establishment compared to those from central seed lots. For the low-severity prescribed fire considered in this study, effects on measured soil properties were minimal, and did not result in any improvement to seedling establishment. For soil properties to play an important role in promoting jack pine seedling growth, hotter fires may be required, while lower-severity fires will require other effects- such as seed release, changes to moisture dynamics, or competition- to affect jack pine germination and establishment.