Miracle-dz/UniFAR-FTUs huggingface dataset:
Life as we know it

This paper presents a heuristic proof (and simulations of a primordial soup) suggesting that life—or biological self-organization—is an inevitable and emergent property of any (ergodic) random dynamical system that possesses a Markov blanket. This conclusion is based on the following arguments: if the coupling among an ensemble of dynamical systems is mediated by short-range forces, then the states of remote systems must be conditionally independent. These independencies induce a Markov blanket that separates internal and external states in a statistical sense. The existence of a Markov blanket means that internal states will appear to minimize a free energy functional of the states of their Markov blanket. Crucially, this is the same quantity that is optimized in Bayesian inference. Therefore, the internal states (and their blanket) will appear to engage in active Bayesian inference. In other words, they will appear to model—and act on—their world to preserve their functional and structural integrity, leading to homoeostasis and a simple form of autopoiesis.
Intersectionality of Ethno-cultural Identities and Construal of Distant Suffering Outgroups

In this paper, we explore how white Catholic men talk about the indirect dilemma of non-intervention for black ethnic outgroups. We illustrate how they mobilise global categorisation (all humanity) and use various forms of denial to deal with their non-involvement. Having analyzed representative fragments of their prejudice avoidance talk, we conclude with some observation about the strategic deployment of categories and denial forms as part of identity management talk. In contrast to quantitative research that oversimplifies the ingroup-outgroup distinction, we show how the status and outgroupness levels of the needy appear to be both flexible and intricate, which depends on the often-ignored intersecting cultural factors, like the respondents’ and victims’ ethnic, racial and religious identities. Key Words: Cross-Cultural Relations, Identity, Prejudice Denial, Textual Thematic Analysis.
Forward masking by enhanced components in harmonic complexes.

The ability of a given target component in certain spectral complexes can be considerably increased by exposure to the complex with the target component deleted. This "enhancement effect" can be observed under a wide variety of conditions and presumably reflects frequency-specific adaptation: the frequency region around the target frequency is not adapted during the exposure and hence is relatively more sensitive. Data from the present study indicate that an enhanced component in a harmonic complex produces more forward masking of a sinusoidal probe than when that component is not enhanced, i.e., an enhanced component behaves as if it were physically more intense. This suggests that the adaptation process underlying the enhancement effect produces an increase in gain in the unadapted frequency region. This increase might result from a decrease, due to adaptation, of suppression of the unadapted region.
Learning Evolving Concepts Using Partial Memory Approach

This paper addresses the problem of learning evolving concepts, that is, concepts whose meaning gradually evolves in time. Solving this problem is important to many applications, for example, building intelligent agents for helping users in Internet search, active vision, automatically updating knowledge-bases, or acquiring profiles of users of telecommunication networks. Requirements for a learning architecture supporting such applications include the ability to incrementally modify concept definitions to accommodate new information, fast learning and recognition rates, low memory needs, and the understandability of computer-created concept descriptions. To address these requirements, we propose a learning architecture based on Variable-Valu ed Logic, the Star Methodology, and the AQ algorithm. The method uses a partial-memory approach, which means that in each step of learning, the system remembers the current concept descriptions and specially selected representative examples from the past experience. The developed method has been experimentally applied to the problem of computer system intrusion detection. The results show significant advantages of the method in learning speed and memory requirements with only slight decreases in predictive accuracy and concept simplicity when compared to traditional batch-style learning in which all training examples are provided at once.
A Faster Algorithm for Quasi-convex Integer Polynomial Optimization

We present a faster exponential-time algorithm for integer optimization over quasi-convex polynomials. We study the minimization of a quasiconvex polynomial subject to s quasi-convex polynomial constraints and integrality constraints for all variables. The new algorithm is an improvement upon the best known algorithm due to Heinz (Journal of Complexity, 2005). A lower time complexity is reached through applying a stronger ellipsoid rounding method and applying a recent advancement in the shortest vector problem to give a smaller exponential-time complexity of a Lenstra-type algorithm. For the bounded case, our algorithm attains a time-complexity of s(rlMd) O(1) 2 2n log2(n)+O(n) when M is a bound on the number of monomials in each polynomial and r is the binary encoding length of a bound on the feasible region. In the general case, sl O(1) d O(n) 2 2n log2(n) . In each we assume d 2 is a bound on the total degree of the polynomials and l bounds the maximum binary encoding size of the input.
Beneath the arctic greening: Will soils lose or gain carbon or perhaps 
a little of both?

Abstract. Ecosystem shifts related to climate change are anticipated for the next decades to centuries based on a number of conceptual and experimentally derived models of plant structure and function. Belowground, the potential responses of soil systems are less well known. We used geochemical steady state models, soil density fractionation, and soil radiocarbon data to constrain changes in soil carbon based on measurements from detrital (free light), aggregate-bound (occluded) and complexed or chemically bound (mineral associated) carbon pools and for bulk soil. We explored a space-for-time sequence of soils along a cold-to-warm climatic gradient from Alaskan Black Spruce forest soil with permafrost (Gelisols; 50 cm Mean Annual Temperature −1.5 ºC), Alaskan White Spruce forest soil lacking permafrost (Inceptisols; 50 cm MAT +3 ºC ), and Iowa Grassland soil lacking permafrost (Mollisols; 50 cm MAT +9 ºC) developed on similar geologic substrates (wind-blown loess deposits). These temperature ranges were also representative of temperatures at 50 cm soil depth from model output by the Community Land Model for the years 2014, 2100, and 2300 for Interior Alaska. Fitting an exponential equation to depth trends in soil C down to 2 m depths, we found that depth distributions of organic C were related mainly to depths of rooting and changes in bulk density. Using output from the geochemical steady state model, the direction and magnitude of the C loss or gain upon ecosystem shift was dictated by the C stocks of initial and final ecosystems. Radiocarbon measurements specific to each soil fraction (free light, occluded, and mineral associated) allowed us to constrain the timing of the potential loss or gain of C in each fraction driven by climatic shifts. Thawing from the Gelisol to Inceptisol in loess parent materials from present day to year 2100 resulted in small net gains to soil C, reflecting the net balance between loss of detrital and gain into occluded and mineral associated C. Greater warming and shifts from Inceptisol to Mollisol analogous to predicted warming from circa 2100 to 2300 resulted in net C losses from both occluded and mineral associated C, although small gains to the free light C fraction occurred throughout the depth profile. Gains to occluded and mineral associated C post- thaw likely reflect aggregate formation and physical protection of C as well as formation of organo-mineral compounds that accompany microbial processing. Greater warming and shifts from Inceptisol to Mollisol, which are analogous to predicted warming circa 2100 to 2300, resulted in net C losses from both occluded and mineral associated C resulting from enhanced decomposition, small gains to the free light C fraction occurred throughout the transition to Mollisol reflecting deeper rooting of the tallgrass prairie system.
Social Learning in the Demand for Employer-Sponsored Health Insurance

I use data from the University of California to empirically examine the role of social learning in employees’ choices of health plans. The basic empirical strategy starts with the observation that if social learning is important, decisions should appear to be correlated across employees within the same department. I present evidence of considerable “clustering” on health plans within departments, and estimate discrete choice models in which individuals’ perceptions of the payoffs associated with different alternatives are influenced by peers’ decisions. To distinguish social learning from the potential influence of common unobservable characteristics I propose and conduct a variety of tests, which taken together favor the social learning explanation. For example, I find that newly hired employees are more responsive to their peers’ decisions than existing employees, which I interpret as reflecting their relative lack of private information (and consequently their relative reliance on information communicated from coworkers).
A Composite LMMSE Channel Estimator for Spectrum-Efficient OFDM Transmit Diversity

In this paper, we propose a subcarrier allocation method and a composite linear minimum mean square error (LMMSE) channel estimator to increase spectrum efficiency in orthogonal frequency division multiplexing (OFDM) transmit diversity. The pilot symbols for OFDM transmit (Alamouti) diversity are exclusively allocated in two OFDM symbols in different antennas, which causes serious degradation of spectrum efficiency. To reduce the number of pilot symbols, our subcarrier allocation method uses repetition-coded data symbols, and the proposed channel estimator maintains good bit error rate (BER) performance.
Impact of Software Comprehension in Software Maintenance and Evolution

The need of change is essential for a software system to reside longer in the market. Change implementation is only done through the maintenance and successful software maintenance gives birth to a new software release that is a refined form of the previous one. This phenomenon is known as the evolution of the software. To transfer software from lower to upper or better form, maintainers have to get familiar with the particular aspects of software i.e. source code and documentation. Due to the poor quality of documentation maintainers often have to rely on source code. So, thorough understanding of source code is necessary for effective change implementation. This study explores the code comprehension problems discussed in the literature and prioritizes them according to their severity level given by maintenance personnel in the industry. Along with prioritizing the problems, study also presents the maintenance personnel suggested methodologies for improving code comprehension. Consideration of these suggestions in development might help in shortening the maintenance and evolution time.
Security aspects of analog and mixed-signal circuits

A system's hardware is increasingly recognized as an "Achille's heel" in the context of cyberattacks. This paper summarizes hardware security threats known today, along with applicable countermeasures, and discusses their relevance in the analog and mixed-signal (AMS) domain. It is intended to help AMS designers to realistically assess the exposure of their systems to security threats. Moreover, it provides pointers to available defenses and discusses which of their assumptions and prerequisites are valid for AMS circuits and where new solutions are needed.
Resource Assessment using Market Indices: Toward an Economic Grid Exchange Model

This paper is revisiting computational Grid economy models with an innovative, unrestricted and purely market regulated approach where both the consumer and provider standpoints are considered. The paper starts by outlining the actual Grid model shortcomings in regards to sharing incentive, return on investment, resource usage efficiency and quality of service accountability. Subsequently, a replacement for the traditional utility function is presented in the form of an aggregation of statistical indices computed from previous transactions. While this sharing scheme is based on economic foundations, its intent is not to transform the Grid in an expensive capitalist system. Targeted goal is rather to stimulate sharing by assessing resources dynamically in some form of abstract grid unit fostering supercomputing democratization.
Persistent Monitoring of Stochastic Spatio-temporal Phenomena with a Small Team of Robots

This paper presents a solution for persistent monitoring of real-world stochastic phenomena, where the underlying covariance structure changes sharply across time, using a small number of mobile robot sensors. We propose an adaptive solution for the problem where stochastic real-world dynamics are modeled as a Gaussian Process (GP). The belief on the underlying covariance structure is learned from recently observed dynamics as a Gaussian Mixture (GM) in the low-dimensional hyper-parameters space of the GP and adapted across time using Sequential Monte Carlo methods. Each robot samples a belief point from the GM and locally optimizes a set of informative regions by greedy maximization of the submodular entropy function. The key contributions of this paper are threefold: adapting the belief on the covariance using Markov Chain Monte Carlo (MCMC) sampling such that particles survive even under sharp covariance changes across time; exploiting the belief to transform the problem of entropy maximization into a decentralized one; and developing an approximation algorithm to maximize entropy on a set of informative regions in the continuous space. We illustrate the application of the proposed solution through extensive simulations using an artificial dataset and multiple real datasets from fixed sensor deployments, and compare it to three competing state-of-the-art approaches.
Ih Tunes Theta/Gamma Oscillations and Cross-Frequency Coupling In an In Silico CA3 Model

channels are uniquely positioned to act as neuromodulatory control points for tuning hippocampal theta (4–12 Hz) and gamma (25 Hz) oscillations, oscillations which are thought to have importance for organization of information flow. contributes to neuronal membrane resonance and resting membrane potential, and is modulated by second messengers. We investigated oscillatory control using a multiscale computer model of hippocampal CA3, where each cell class (pyramidal, basket, and oriens-lacunosum moleculare cells), contained type-appropriate isoforms of . Our model demonstrated that modulation of pyramidal and basket allows tuning theta and gamma oscillation frequency and amplitude. Pyramidal also controlled cross-frequency coupling (CFC) and allowed shifting gamma generation towards particular phases of the theta cycle, effected via 's ability to set pyramidal excitability. Our model predicts that in vivo neuromodulatory control of allows flexibly controlling CFC and the timing of gamma discharges at particular theta phases.
CD95 (Fas) expression is regulated by sequestration in the Golgi complex in B‐cell lymphoma

Summary. The CD95 (Fas) molecule transmits apoptotic signals important in B‐cell development and the genesis of B‐cell lymphoma. We have investigated the surface and intracellular expression of CD95 in Burkitt's lymphoma (BL) cells, an important non‐Hodgkin's lymphoma of B‐cell origin. Group I BL cells did not express CD95 at the cell surface, but contained high levels of this receptor in the cytoplasm. In contrast, group III BL cells expressed CD95 intracellularly and at the cell surface. In group I and group III BL cells, cytoplasmic CD95 was localized to the Golgi complex, as assessed by confocal immunofluorescence microscopy and subcellular fractionation followed by immunoblotting. Trafficking through the Golgi complex is regulated by elements within the target protein and cellular sorting mechanisms. CD95 contains candidate signals for interaction with trafficking machinery. Group I BL cells can be induced to upregulate surface expression of CD95 following CD40 ligation and certain group I BL cell lines drift invitro to a group III phenotype, with consequent surface expression of CD95. Taken together, these observations show that CD95 can either be retained in the Golgi complex or exported to the cell surface, and suggest that membrane trafficking has an important and previously unrecognized role in regulating CD95 expression in B lymphocytes.
Mapping of carnallite along with semi-quantitative estimation of potassium content of drill cores using hyperspectral imagery

ABSTRACT Reflectance spectra and hyperspectral images of carnallite, halite, and anhydrite samples from the Mboukoumassi sylvite deposit, Congo, were obtained using an ASDTM spectrometer and a HySpexTM imager, respectively. The spectral absorption features of the ore mineral, carnallite, at 999, 1076, 1206, 1232, 1763, and 1970 nm are highly reproducible and distinct from absorption features of the gangue minerals, halite and anhydrite. The intensity of the approximately 1200 nm absorption feature can be used to map the spatial distribution of carnallite in drill core. We also established an empirical relationship between potassium content estimated through X-ray fluorescence (XRF) measurements and absorption intensity at approximately 1200 nm. Based on this empirical relationship, the potassium content of a core surface was mapped. This technique provides more detailed estimates of potassium content and its spatial distribution in core samples than possible by point sampling, and this is beneficial in assessing the resource potential sylvite deposits.
A Simple Water-Based Synthesis of Au Nanoparticle/PDMS Composites for Water Purification and Targeted Drug Release

AuNP/PDMS nanocomposites have been synthesized in the form of gels, foams, and films with distinctive structure and morphology. A simple in situ process in aqueous medium for the formation of such composite materials is described. The nanoparticles are held firmly within the PDMS while still being chemically accessible to substances soluble in PDMS. We demonstrate the utility of this property for water purification applications such as removing aromatic solvents and sulfur-containing contaminants from water. The contaminants can be freed from the composite with a simple thermal treatment, allowing the material to be reused. We also demonstrate chemically selective uptake and release of a fluorescent dye by the nanocomposite as a drug delivery model system.
GIS-TECHNOLOGIES AND MATHEMATICAL SIMULATION TO PREDICT LIGHTNING-CAUSED FOREST FIRE DANGER

Context. The components of the geoinformation system for monitoring, forecasting and assessment of forest fire danger caused by thunderstorm activity are developed. Objective of the work is to create an embedded software tool for physically based forecasting, monitoring and evaluation of the probability of forest fire occurrence as a result of the impact of a lightning discharge on a tree trunk. Method. Structural analysis is used to design elements and information flows inside and outside of the developed geographic information system. Mathematical modeling is used to determine the parameters of tree ignition by the cloud-to-ground lightning discharge. Mathematically, the process of tree trunk heating is described using a system of non-stationary heat conduction equations with a source part responsible for the heat release according to the Joule-Lenz law in the core of the tree trunk. The finite difference method is used to solve the differential heat equation. Finite-difference analogues are solved by the double-sweep method. Program realization is implemented in the built-in high-level language. The probability theory (conditional probability) is used to develop a probabilistic criterion for forest fire danger estimation. Results. A software tool is developed to estimate the tree ignition delay time as a result of the impact of a cloud-to-ground lightning discharge. The GIS-system component is developed in the high-level programming language Python. We have obtained probability distribution of forest fire occurrences from thunderstorms for the territory of the Timiryazevsky forestry in the Tomsk region is obtained. Conclusions. We have proposed a physically proved method for forecasting, monitoring and assessing forest fire danger caused by thunderstorm activity. The deterministic mathematical model is used to simulate tree ignition by the cloud-to-ground lightning discharge in conjunction with the probabilistic criterion for assessing forest fire danger. We have analysed forest fire danger for a typical territory of the Tomsk region (Timiryazevskiy forestry).
Smart Register Files for High-Performance Microprocessors

Abstract : This report examines how the compiler can more efficiently use a large number of processor registers. The placement of data items into registers, called register allocation, is known to be one of the most important compiler optimizations for high-speed computers because registers are the fastest storage devices in the computer system. However, register allocation has been limited in scope because of aliasing in the memory system. To break this limitation and allow more data to be placed into registers, new compiler and microarchitecture support is needed.
Echium Oil Reduces Plasma Triglycerides by Increasing Intravascular Lipolysis in apoB100-Only Low Density Lipoprotein (LDL) Receptor Knockout Mice

Echium oil (EO), which is enriched in SDA (18:4 n-3), reduces plasma triglyceride (TG) concentrations in humans and mice. We compared mechanisms by which EO and fish oil (FO) reduce plasma TG concentrations in mildly hypertriglyceridemic male apoB100-only LDLrKO mice. Mice were fed one of three atherogenic diets containing 0.2% cholesterol and palm oil (PO; 20%), EO (10% EO + 10% PO), or FO (10% FO + 10% PO). Livers from PO- and EO-fed mice had similar TG and cholesteryl ester (CE) content, which was significantly higher than in FO-fed mice. Plasma TG secretion was reduced in FO vs. EO-fed mice. Plasma very low density lipoprotein (VLDL) particle size was ordered: PO (63 ± 4 nm) > EO (55 ± 3 nm) > FO (40 ± 2 nm). Post-heparin lipolytic activity was similar among groups, but TG hydrolysis by purified lipoprotein lipase was significantly greater for EO and FO VLDL compared to PO VLDL. Removal of VLDL tracer from plasma was marginally faster in EO vs. PO fed mice. Our results suggest that EO reduces plasma TG primarily through increased intravascular lipolysis of TG and VLDL clearance. Finally, EO may substitute for FO to reduce plasma TG concentrations, but not hepatic steatosis in this mouse model.
THE EFFECT OF A FIXED NON-MINERAL OIL ON THE ACTIVITY OF AMPICILLIN TRIHYDRATE AGAINST RES STRAINS OF STAPHYLOCOCCUS

The effect of King's Vegetable oil, a fixed non-mineral oil, on activity of ampicillin trihydrate, a waterinsoluble form o investigated against resistant clinical strains of Staphylococcus a diffusion method employed, 40% of the resistant clinical strain sensitivity to different oil-dispersed concentrations of ampicillin ranged from 0. 06μg/ml to 1. 25μg/ml. The resistant strains were strains detected with Beta-lactamase. This finding is presented report on the potentiality of employing an oil medium to effect a resistance in staphylococci besides the use of acridine dyes, ultraviolet radiation and other measures.