Miracle-dz/UniFAR-FTUs huggingface dataset:
Pragmatic Comparison Analysis of Alternative Option Pricing Models

In this paper, we price European Call three different option pricing models, where the volatility is dynamically changing i.e. non constant. In stochastic volatility (SV) models for option pricing a closed form approximation technique is used, indicating that these models are computationally efficient and have the same level of performance as existing ones. We show that the calibration of SV models, such as Heston model and the High Order Moment based Stochastic Volatility (MSV) is often faster and easier. On 15 different datasets of index options, we show that models which incorporates stochastic volatility achieves accuracy comparable with the existing models. Further, we compare the In Sample and Out Sample pricing errors of each model on each date. Lastly, the pricing of models is compared among three different market to check model performance in different markets. Keywords: Option Pricing Model, Simulations, Index Options, Stochastic Volatility Models, Loss Function http://www.sci-int.com/pdf/638279543859822650.pdf
Sometimes you just can’t: within-person variation in working memory capacity moderates negative affect reactivity to stressor exposure

ABSTRACT The executive hypothesis of self-regulation places cognitive information processing at the center of self-regulatory success/failure. While the hypothesis is well supported by cross-sectional studies, no study has tested its primary prediction, that temporary lapses in executive control underlie moments of self-regulatory failure. Here, we conducted a naturalistic experiment investigating whether short-term variation in executive control is associated with momentary self-regulatory outcomes, indicated by negative affect reactivity to everyday stressors. We assessed working memory capacity (WMC) through ultra-brief, ambulatory assessments on smart phones five times per day in a 7-day ecological momentary assessment (EMA) study involving college-aged adults. We found that participants exhibited more negative affect reactivity to stressor exposures during moments when they exhibited lower than usual WMC. Contrary to previous findings, we found no between-person association between WMC and average stress reactivity. We interpret these findings as reflecting the role of executive control in determining one’s effective capacity to self-regulate.
A Critical Analysis of Internal Reliability for Uncertainty Quantification of Dense Image Matching in Multi-view Stereo

Abstract. Nowadays, photogrammetrically derived point clouds are widely used in many civilian applications due to their low cost and flexibility in acquisition. Typically, photogrammetric point clouds are assessed through reference data such as LiDAR point clouds. However, when reference data are not available, the assessment of photogrammetric point clouds may be challenging. Since these point clouds are algorithmically derived, their accuracies and precisions are highly varying with the camera networks, scene complexity, and dense image matching (DIM) algorithms, and there is no standard error metric to determine per-point errors. The theory of internal reliability of camera networks has been well studied through first-order error estimation of Bundle Adjustment (BA), which is used to understand the errors of 3D points assuming known measurement errors. However, the measurement errors of the DIM algorithms are intricate to an extent that every single point may have its error function determined by factors such as pixel intensity, texture entropy, and surface smoothness. Despite the complexity, there exist a few common metrics that may aid the process of estimating the posterior reliability of the derived points, especially in a multi-view stereo (MVS) setup when redundancies are present. In this paper, by using an aerial oblique photogrammetric block with LiDAR reference data, we analyze several internal matching metrics within a common MVS framework, including statistics in ray convergence, intersection angles, DIM energy, etc. We associate these metrics to the per-point errors evaluated through LiDAR reference data and discuss their potential contributions in estimating internal reliabilities of point clouds derived from DIM algorithms. The experimental results show that ray convergence and DIM energy are relevant indicators for the accuracy of the generated point clouds. Initial investigation shows that these two indicators could be further utilized to infer the measurement errors without reference data, which could potentially estimate the reliabilities of point clouds through error propagation.
Atomistic modelling of electron propagation and radiation emission in oriented bent ultra-thin Si and Ge crystals

Computational modelling of passage of high-energy electrons through crystalline media is carried out by means of the relativistic molecular dynamics. The results obtained are compared with the experimental data for 855 MeV electron beam incident on oriented bent ultra-thin (15 microns) silicon and germanium crystals. The simulations have been performed for the geometries of the beam--crystal orientation that correspond (i) to the channeling regime and (ii) to the volume reflection. A comparison with the experiment is carried out in terms of angular distributions of the electrons deflected by the crystals bent with different curvature radii as well as of the spectra of the emitted radiation. For both crystals a good agreement between the simulated and experimentally measured data is reported. The origin of remaining minor discrepancies between theory and experiment is discussed.
Uplink Power Control for Distributed Massive MIMO with 1-Bit ADCs

We consider the problem of uplink power control for distributed massive multiple-input multiple-output systems where the base stations (BSs) are equipped with 1-bit analog-to-digital converters (ADCs). The scenario with a single user equipment (UE) is first considered to provide insights into the signal-to-noise-and-distortion ratio (SNDR). With a single BS, the SNDR is a unimodal function of the UE transmit power. With multiple BSs, the SNDR at the output of the joint combiner can be made unimodal by adding properly tuned dithering at each BS. As a result, the UE can be effectively served by multiple BSs with 1-bit ADCs. Considering the signal-to-interference-plus-noise-and-distortion ratio (SINDR) in the multi-UE scenario, we aim at optimizing the UE transmit powers and the dithering at each BS based on the min-power and max-min-SINDR criteria. To this end, we propose three algorithms with different convergence and complexity properties. Numerical results show that, if the desired SINDR can only be achieved via joint combining across multiple BSs with properly tuned dithering, the optimal UE transmit power is imposed by the distance to the farthest serving BS (unlike in the unquantized case). In this context, dithering plays a crucial role in enhancing the SINDR, especially for UEs with significant path loss disparity among the serving BSs.
Exact trapped $N$-soliton solutions of the nonlinear Schr\"odinger equation using the inverse problem method

In this work, we show the application of the ``inverse problem'' method to construct exact $N$ trapped soliton-like solutions of the nonlinear Schr\"odinger or Gross-Pitaevskii equation (NLSE and GPE, respectively) in one, two, and three spatial dimensions. This method is capable of finding the external (confining) potentials which render specific assumed waveforms exact solutions of the NLSE for both attractive ($g<0$) and repulsive ($g>0$) self-interactions. For both signs of $g$, we discuss the stability with respect to self-similar deformations and translations. For $g<0$, a critical mass $M_c$, or equivalently the number of particles, for instabilities to arise can often be found analytically. On the other hand, for the case with $g>0$ corresponding to repulsive self interactions which is often discussed in the atomic physics realm of Bose-Einstein condensates (BEC), the bound solutions are found to be always stable. For $g<0$, we also determine the critical mass numerically by using linear stability or Bogoliubov-de Gennes analysis, and compare these results with our analytic estimates. Various analytic forms for the trapped $N$-soliton solutions are discussed, including sums of Gaussians or higher-order eigenfunctions of the harmonic oscillator Hamiltonian.
Golden Standard: a complete standard, portable, and interoperative MoClo tool for model and non-model proteobacteria

Abstract Modular cloning has become a benchmark technology in synthetic biology. However, a notable disparity exists between its remarkable development and the need for standardization to facilitate seamless interoperability among systems. The field is thus impeded by an overwhelming proliferation of organism-specific systems that frequently lack compatibility. To overcome these issues, we present Golden Standard (GS), a Type IIS assembly method underpinned by the Standard European Vector Architecture. GS unlocks modular cloning applications for most bacteria, and delivers combinatorial multi-part assembly to create genetic circuits of up to twenty transcription units (TUs). Reliance on MoClo syntax renders GS fully compatible with many existing tools and it sets the path towards efficient reusability of available part libraries and assembled TUs. GS was validated in terms of DNA assembly, portability, interoperability and phenotype engineering in α-, β-, γ- and δ-proteobacteria. Furthermore, we provide a computational pipeline for parts characterization that was used to assess the performance of GS parts. To promote community-driven development of GS, we provide a dedicated web-portal including a repository of parts, vectors, and Wizard and Setup tools that guide users in designing constructs. Overall, GS establishes an open, standardized framework propelling the progress of synthetic biology as a whole.
A Concise Overview of Safety Aspects in Human-Robot Interaction

As of today, robots exhibit impressive agility but also pose potential hazards to humans using/collaborating with them. Consequently, safety is considered the most paramount factor in human-robot interaction (HRI). This paper presents a multi-layered safety architecture, integrating both physical and cognitive aspects for effective HRI. We outline critical requirements for physical safety layers as service modules that can be arbitrarily queried. Further, we showcase an HRI scheme that addresses human factors and perceived safety as high-level constraints on a validated impact safety paradigm. The aim is to enable safety certification of human-friendly robots across various HRI scenarios.
GABA Release From Central Amygdala Neurotensin Neurons Differentially Modulates Reward and Consummatory Behavior in Male and Female Mice

The central nucleus of the amygdala is known to play key roles in alcohol use and affect. Neurotensin neurons in the central nucleus of the amygdala have been shown to regulate alcohol drinking in male mice. However, little is known about which neurotransmitters released by these cells drive alcohol consumption or whether these cells drive alcohol consumption in female mice. Here we show that knockdown of GABA release from central amygdala neurotensin neurons using a Nts-cre-dependent vGAT-shRNA-based AAV strategy reduces alcohol drinking in male, but not female, mice. This manipulation did not impact avoidance behavior, except in a fasted novelty-suppressed feeding test, in which vGAT shRNA mice demonstrated increased latency to feed on a familiar high-value food reward, an effect driven by male mice. In contrast, vGAT shRNA female mice showed heightened sensitivity to thermal stimulation. These data show a role for GABA release from central amygdala neurotensin neurons in modulating consumption of rewarding substances in different motivational states.
Template-assisted fabrication of moon-shaped channels for protein breakthrough analysis

Cylindrical column with packed stationary phase is the workhorse of liquid chromatography systems. These stationary phases are commonly classified on the basis of different form factors namely, beads and monoliths for protein chromatography. Monolithic rods are one of the important geometries derived from polymers through complex polymerization schemes with additional requirements such as cross-linkers and specific reaction conditions. To address these practical difficulties and enable ease of fabrication at laboratory scale, acrylic copolymers are hypothesized to perform as a monolithic stationary phase suitable for protein chromatography. The present work proposes a rapid fabrication technique to obtain monolithic rods that could be reconditioned without any of the above additional steps. It is characterized with monolith diameter that could be controlled using acrylic copolymer concentration. Formation of the copolymeric stationary phase inside microchannel led to annular geometry and in turn, demonstrated fabrication of moon-shaped channels (MSCs) for the first time in literature. An online monitoring system facilitated tracer breakthrough analysis with MSCs to report sharp peak front and an estimate of channel void volume. Breakthrough curves with single protein validated the selection of blue dextran as tracer and indicated retention of proteins due to electrostatic interactions on the functional copolymer surface.
Distilling HuBERT with LSTMs via Decoupled Knowledge Distillation

Much research effort is being applied to the task of compressing the knowledge of self-supervised models, which are powerful, yet large and memory consuming. In this work, we show that the original method of knowledge distillation (and its more recently proposed extension, decoupled knowledge distillation) can be applied to the task of distilling HuBERT. In contrast to methods that focus on distilling internal features, this allows for more freedom in the network architecture of the compressed model. We thus propose to distill HuBERT's Transformer layers into an LSTM-based distilled model that reduces the number of parameters even below DistilHuBERT and at the same time shows improved performance in automatic speech recognition.
Internal Physical Features of a Land Surface Model Employing a Tangent Linear Model

The Earth's land surface, including its biomass, is an integral part of the Earth's weather and climate system. Land surface heterogeneity, such as the type and amount of vegetative covering. , has a profound effect on local weather variability and therefore on regional variations of the global climate. Surface conditions affect local weather and climate through a number of mechanisms. First, they determine the re-distribution of the net radiative energy received at the surface, through the atmosphere, from the sun. A certain fraction of this energy increases the surface ground temperature, another warms the near-surface atmosphere, and the rest evaporates surface water, which in turn creates clouds and causes precipitation. Second, they determine how much rainfall and snowmelt can be stored in the soil and how much instead runs off into waterways. Finally, surface conditions influence the near-surface concentration and distribution of greenhouse gases such as carbon dioxide. The processes through which these mechanisms interact with the atmosphere can be modeled mathematically, to within some degree of uncertainty, on the basis of underlying physical principles. Such a land surface model provides predictive capability for surface variables including ground temperature, surface humidity, and soil moisture and temperature. This information is important for agriculture and industry, as well as for addressing fundamental scientific questions concerning global and local climate change. In this study we apply a methodology known as tangent linear modeling to help us understand more deeply, the behavior of the Mosaic land surface model, a model that has been developed over the past several years at NASA/GSFC. This methodology allows us to examine, directly and quantitatively, the dependence of prediction errors in land surface variables upon different vegetation conditions. The work also highlights the importance of accurate soil moisture information. Although surface variables are predicted imperfectly due to inherent uncertainties in the modeling process, our study suggests how satellite observations can be combined with the model, through land surface data assimilation, to improve their prediction.
Torwards Infinite-State Verification and Planning with Linear Temporal Logic Modulo Theories (Extended Abstract)

In this extended abstract, we discuss about Linear Temporal Logic Modulo Theories over finite traces ( LTL MTf ), a temporal logic that we recently introduced with the goal of providing an equilibrium between generality of the formalism and decidability of the logic. After recalling its distinguishing features, we discuss some future applications
From intent to implementation: Factors affecting public involvement in life science research

Public involvement is key to closing the gap between research production and research use, and the only way to achieving ultimate transparency in science. The majority of life science research is not public-facing, but is funded by the public and impacts communities. We undertook an exploratory survey of researchers within the life sciences to better understand their views and perceived challenges to involving the public in their research. As survey response rate could not be determined, interpretation of the results must be cautious. We had a valid response cohort of n = 110 researchers, of whom 90% were primarily laboratory based. Using a mixed methods approach, we demonstrate that a top-down approach is key to motivate progression of life scientists from feeling positive towards public involvement to actually engaging in it. Researchers who viewed public involvement as beneficial to their research were more likely to have direct experience of doing it. We demonstrate that the systemic flaws in the way life sciences research enterprise is organised, including the promotion system, hyper-competition, and time pressures are major barriers to involving the public in the scientific process. Scientists are also apprehensive of being involuntarily involved in the current politicized climate; misinformation and publicity hype surrounding science nowadays makes them hesitant to share their early and in-progress research. The time required to deliberate study design and relevance, plan and build relationships for sustained involvement, provide and undertake training, and improve communication in the current research environment is often considered nonpragmatic, particularly for early career researchers. In conclusion, a top-down approach involving institutional incentives and infrastructure appears most effective at transitioning researchers from feeling positive towards public involvement to actually implementing it.
The Relation between Coping Strategies and Social Support among Patients with Schizophrenia

Schizophrenia is a debilitating long term disorder characterized by episodes of acute psychotic symptoms and periods of remission often accompanied by alteration in social and psychological functioning [1-3]. It has been proposed that patients with schizophrenia often have stress-prone life styles that generate surplus life events as a result of factors such as limited social support networks, coping abilities, and unemployment. In addition, people with schizophrenia often experience distressing mental symptoms such as hearing internal voices not heard by others, or believing that other people are reading their minds, controlling their thoughts, or plotting to harm them [3,4]. Research suggested that stressful life events that are psychosocial in nature, appear to play a major role in triggering schizophrenic episodes, increasing the likelihood of relapse rate, chronicity and prolong the duration of hospitalization of patients with schizophrenia [5-7]. Since the course of illness is considered a major source of stress in schizophrenia it raises the need for coping [8]. According to the cognitive-transactional theory of stress, coping has been defined as “a constantly changing one’s cognitive and behavioral effort to manage the internal and external demands of a personenvironment transaction that is considered taxing or exceeding one’s resources” [9]. Coping strategies are categorized into three main styles namely task, emotion, and avoidance-oriented [10].
Interactive Direct Volume Rendering of Curvilinear and Unstructured Data

Methods for interactive volume rendering of nonrectilinear 3D scientific data sets, such as those generated by the finite element method, are investigated. We focus on the use of projection methods, in particular splatting algorithms, for volume rendering curvilinear and irregular data. The data is rendered without interpolating it to a rectilinear mesh. The goal is interactive performance even when the data sets are very large. To achieve this, we investigate parallelization, graphics hardware support, a suite of splatting approximations, and mesh filtration. Using these techniques, we have generated images of nonrectilinear data sets with over 1,000,000 cells interactively (in less than 15-30 seconds). Using filtering methods this performance is possible for even larger data sets. Various optical models are discussed as a theoretical basis for volume rendering. A new theoretical model for interactive volume rendering is introduced. An algorithm is presented that generates a visibility ordering of an acyclic convex set of meshed convex polyhedra. This algorithm takes time linear in the size of the mesh. Modifications to this algorithm and/or preprocessing techniques are described which permit nonconvex cells, nonconvex meshes (meshes with cavities and/or voids), meshes with cycles, and sets of disconnected meshes to be ordered. It is shown how the ordering algorithms can be used for domain decomposition of finite element meshes for parallel processing, and how the data structures used by these algorithms can be used to solve the spatial point location problem. The effects of cyclically obstructing polyhedra are discussed, and methods for their elimination are described,including the use of the Delaunay triangulation. Methods for converting nonconvex meshes into convex meshes are described. A suite of splatting approximations are presented, which along with other more accurate methods discussed, form a hierarchy of rendering methods that tradeoff image accuracy/quality and generation time. Parallel volume rendering algorithms that include visibility ordering for both convex and nonconvex irregular meshes are investigated and results are given for several versions of parallel algorithms. A performance analysis of one of these algorithms on a high performance MIMD 3D graphics workstation is presented.
Measuring the cognitive cost of downward monotonicity by controlling for negative polarity

Our goal in this study was to behaviorally characterize the property (or properties) that render negative quantifiers more complex in processing compared to their positive counterparts (e.g. the pair few/many). We examined two sources: (i) negative polarity; (ii) entailment reversal (aka downward monotonicity). While negative polarity can be found in other pairs in language such as dimensional adjectives (e.g. the pair small/large), only in quantifiers does negative polarity also reverse the entailment pattern of the sentence. By comparing the processing traits of negative quantifiers with those of non-monotone expressions that contain negative adjectives, using a verification task and measuring reaction times, we found that negative polarity is cognitively costly, but in downward monotone quantifiers it is even more so. We therefore conclude that both negative polarity and downward monotonicity contribute to the processing complexity of negative quantifiers.