Miracle-dz/UniFAR-FTUs huggingface dataset:
News on Stock Market Returns and Conditional Volatility in Nigeria: An EGARCH-in-Mean Approach.

This paper aims at exploring the relationship between news on the stock market returns and conditional volatility in Nigeria. To determine this relationship, the researcher employed the exponential generalized conditional Heteroscedasticity (EGARCH) in mean model since the model accommodates asymmetric and leverage property. The results of the analysis shows that there is a significant relationship between stock market returns and conditional volatility. Secondly, that the persistence of shocks in the market takes a short time to die out, thirdly, that the stock market volatility is less sensitive to market events while asymmetric effect is positive and significant indicating that good news lowers volatility in Nigeria. In the light of the findings, the researcher suggests that Nigeria stock exchange should ensure that company specific information should be reliable with maximum transparency and speedy dissemination. Also, with the already existing good news lowering volatility and cost of capital in the economy, Government should avoid unnecessary modifications of her policies that are capable of changing the market trading pattern. These measures, the researcher believes, will bridge up information asymmetry and enhance the sensitivity of volatility to market events.
Size-corrected Bootstrap Test after Pretesting for Exogeneity with Heteroskedastic or Clustered Data

Pretesting for exogeneity has become a routine in many empirical applications involving instrumental variables to decide whether the ordinary least squares or the two-stage least squares (2SLS) method is appropriate. Guggenberger (2010) shows that the second-stage t-test – based on the outcome of a Durbin-WuHausman type pretest for exogeneity in the first stage – has extreme size distortion with asymptotic size equal to 1 when the standard asymptotic critical values are used. In this paper, we first show that both conditional and unconditional on the data, the standard wild bootstrap procedures are invalid for the two-stage testing and a closely related shrinkage method, and therefore are not viable solutions to such size-distortion problem. Then, we propose a novel size-corrected wild bootstrap approach, which combines certain wild bootstrap critical values along with an appropriate size-correction method. We establish uniform validity of this procedure under either conditional heteroskedasticity or clustering in the sense that the resulting tests achieve correct asymptotic size. Monte Carlo simulations confirm our theoretical findings. In particular, our proposed method has remarkable power gains over the standard 2SLS-based t-test in many settings, especially when the identification is not strong.
Complementarity and Classical Limit of Quantum Mechanics: Energy Measurement aspects

In the present contribution we discuss the role of experimental limitations in the classical limit problem. We studied some simple models and found that Quantum Mechanics does not re-produce classical mechanical predictions, unless we consider the experimental limitations ruled by uncertainty principle. We have shown that the discrete nature of energy levels of integrable systems can be accessed by classical measurements. We have defined a precise limit for this procedure. It may be used as a tool to define the classical limit as far as the discrete spectra of integrable systems are concerned. If a diffusive environment is considered, we conclude that the "lifetime" of discreteness is approximately $1/\kappa$ ($\kappa$ is the diffusion constant), thus it was possible to relate the classical limit of a spectra with the action of an environment and experimental resolution.
Evaluating the Projectivity of Presupposition Triggers in Various Entailment-Canceling Environments

Previous studies investigate the ability of mod-001 els to make pragmatic inferences using pre-002 supposition triggers. However, although pro-003 jection of presuppositions can vary depending 004 on the combination of triggers and environ-005 ments, they evaluate the performance of mod-006 els without human baseline, or include only 007 negative sentences as entailment-canceling en-008 vironments. To evaluate inferences with pre-009 supposition triggers, it is necessary to solicit 010 human judgments as a baseline for model eval-011 uation and use various types of entailment-012 canceling environments. In this study, we in-013 troduce a template-based natural language in-014 ference dataset called Projectivity of Presup-015 position Triggers (PPT), which includes 9,800 016 sentence pairs crossed with six types of pre-017 supposition triggers and four types of syntactic 018 environments. Analysing judgements from 283 019 people on a subset of the dataset, we find that 020 humans take most presupposition patterns as 021 projective, but the projectivity varies depend-022 ing on the combination of triggers and envi-023 ronments. In contrast, models judge some pat-024 terns as non-projective, indicating that the abil-025 ity of the models to process presuppositions 026 may not be human-like. This result highlights 027 that researchers working on model evaluation 028 and dataset creation need to take extra care of 029 the combination of presupposition triggers and 030 environments where they are embedded. 031
Testing of Motor Coordination in Degenerative Neurological Diseases

Parkinson’s disease (PD) is a progressive movement disorder caused by the death of dopamine-producing cells in the midbrain. PD is the most prevalent movement disorder of the central nervous system and affects more than 6.3 million people in the world. The changes in the motor functions of patients are not easy to be clearly and on-time observed by the clinicians and to make the most well-informed decisions for the treatment. The aim of this paper is the monitoring PD by designing, developing, and evaluating a prototype mobile App using a pressure pen, which collects quantitative and objective information about PD patients, thus allowing clinicians to understand better and make assumptions about the severity and the stage of Parkinson’s disease. This study presents a dynamic spiral test that can only be performed with tablet and pen pressure. Furthermore, the handwriting samples by PD patients and healthy controls individuals are collected by a computerized system, and the measurements of Spiral Deviation, Total Time, and Pen Pressure are processed. The results showed an accurate evaluation of the stage of Parkinson’s disease. Thus, the clinician may use the proposed PD telemonitoring system as a screening test, storing the history of all the patient’s measurements.
Fluffy dust forms icy planetesimals by static compression

Context: In planetesimal formation theory, several barriers have been proposed, which are bouncing, fragmentation, and radial drift problems. To understand the structure evolution of dust aggregates is a key in the planetesimal formation. Dust grains become fluffy by coagulation in protoplanetary disks. However, once they become fluffy, they are not sufficiently compressed by collisional compression to form compact planetesimals. Aims: We aim to reveal the pathway of the dust structure evolution from dust grains to compact planetesimals. Methods: Using the compressive strength formula, we analytically investigate how fluffy dust aggregates are compressed by static compression due to ram pressure of the disk gas and self gravity of the aggregates in protoplanetary disks. Results: We reveal the pathway of the porosity evolution from dust grains via fluffy aggregates to form planetesimals, circumventing the barriers in planetesimal formation. The aggregates are compressed by the disk gas to the density of 10^{-3} g/cm^3 in coagulation, which is more compact than the case with collisional compression. Then, they are compressed more by self gravity to 10^{-1} g/cm^3 when the radius is 10 km. Although the gas compression decelerate the growth, they grow enough rapidly to avoid the radial drift barrier when the orbital radius is < 6 AU in a typical disk. Conclusions: We propose fluffy dust growth scenario from grains to planetesimals. It enables the icy planetesimal formation in a wide range beyond the snowline in protoplanetary disks. This result proposes a concrete initial condition of planetesimals for the later stages of the planet formation.
Implications of a Froissart bound saturation of γ * -p deep inelastic scattering. I. Quark distributions at ultra small x

2 as Bjorken x ! 0. The same bound holds for the individual quark distributions. In earlier work, we obtained a very accurate global fit to the combined HERA data on F γp 2 using a fit function which respects the Froissart bound at small x, and is equivalent in its x dependence to the function used successfully to describe all high energy hadronic cross sections, including γp scattering. We extrapolate that fit by a factor of . 3 beyond the HERA region in the natural variable ln(1/x) to the values of x down to x = 10 14 and use the results to derive the quark distributions needed for the reliable calculation of neutrino cross sections at energies up to Eν = 10 17 GeV. These distributions do not satisfy the Feynman “wee parton” assumption, that they all converge toward a common distribution xq(x,Q 2 ) at small x and large Q 2 . This was used in some past calculations to express the dominant neutrino structure function F ν(¯ ν) 2 directly in terms of F γp 2 . We show that the correct distributions nevertheless give results for F ν(¯ ν) 2 which differ only slightly from those obtained assuming that the wee parton limit holds. In two Appendices, we develop simple analytic results for the effects of QCD evolution and operator-product corrections on the distribution functions at small x, and show that these effects amount mainly to shifting the values of ln(1/x) in the initial distributions. PACS numbers:
A Superbubble Feedback Model for Galaxy Simulations

We present a new stellar feedback model that reproduces superbubbles. Superbubbles from clustered young stars evolve quite differently to individual supernovae and are substantially more efficient at generating gas motions. The essential new components of the model are thermal conduction, sub-grid evaporation and a sub-grid multi-phase treatment for cases where the simulation mass resolution is insufficient to model the early stages of the superbubble. The multi-phase stage is short compared to superbubble lifetimes. Thermal conduction physically regulates the hot gas mass without requiring a free parameter. Accurately following the hot component naturally avoids overcooling. Prior approaches tend to heat too much mass, leaving the hot ISM below $10^6$ K and susceptible to rapid cooling unless ad-hoc fixes were used. The hot phase also allows feedback energy to correctly accumulate from multiple, clustered sources, including stellar winds and supernovae. We employ high-resolution simulations of a single star cluster to show the model is insensitive to numerical resolution, unresolved ISM structure and suppression of conduction by magnetic fields. We also simulate a Milky Way analog and a dwarf galaxy. Both galaxies show regulated star formation and produce strong outflows.
On the largest real root of independence polynomials of graphs, an ordering on graphs, and starlike trees

Let $G$ be a simple graph of order $n$. An independent set in a graph is a set of pairwise non-adjacent vertices. The independence polynomial of $G$ is the polynomial $I(G,x)=\sum_{k=0}^{n} s(G,k) x^{k}$, where $s(G,k)$ is the number of independent sets of $G$ of size $k$ and $s(G,0)=1$. Clearly all real roots of $I(G,x)$ are negative. Let $\xi(G)$ be the largest real root of $I(G,x)$. Let $H$ be a simple graph. By $G \succeq H$ we mean that $I(H,x)\geq I(G,x)$ for every $x$ in the interval $[\xi(G),0]$. We note that $G\succeq H$ implies that $\xi(G)\geq \xi(H)$. Also we let $G\succ H$ if and only if $G\succeq H$ and $I(G,x)\neq I(H,x)$. We prove that for every tree $T$ of order $n$, $S_n\succeq T\succeq P_n$, where $S_n$ and $P_n$ are the star and the path of order n, respectively. By $T=T(n_1,\ldots,n_k)$ we mean a tree $T$ which has a vertex $v$ of degree $k$ such that $T\setminus v=P_{n_1-1}+\cdots+P_{n_k-1}$, that is $T\setminus v$ is the disjoint union of the paths $P_{n_1-1},\ldots,P_{n_k-1}$. Let $X=(x_1,\ldots,x_k)$ and $Y=(y_1,\ldots,y_k)$, where $x_1\geq \cdots\geq x_k$ and $y_1\geq\cdots\geq y_k$ are real. By $X\succ Y$, we mean $x_1=y_1,\ldots,x_{t-1}=y_{t-1}$ and $x_t>y_t$ for some $t\in\{1,\ldots,k\}$. We let $X\succ_{d}Y$, if $X\neq Y$ and for every $j$, $1\leq j\leq k$, $\sum_{i=1}^{j}x_i\geq \sum_{i=1}^{j}y_i$. Among all trees with fixed number of vertices, we show that if $(m_1,\ldots,m_k)\succ_{d}(n_1,\ldots,n_k)$, then $T(n_1,\ldots,n_k)\succ T(m_1,\ldots,m_k)$. We conjecture that $T(n_1,\ldots,n_k)\succ T(m_1,\ldots,m_k)$ if and only if $(m_1,\ldots,m_k)\succ (n_1,\ldots,n_k)$, where $\sum_{i=1}^kn_i=\sum_{i=1}^km_i$.
Deep multi feature dynamic adversarial diagnosis approach of rotating machinery

Recent works show that knowledge transfer is an effective strategy to solve cross-domain diagnosis problems. The existing domain adaptation methods considering both global and local distribution between domains do not make the most of the knowledge learned by deep neural network, resulting in low diagnosis accuracy. To solve this problem, a deep multi feature dynamic adversarial diagnosis (DMDAD) method for transfer diagnosis of rotating machinery is presented in this paper. Firstly, the one-dimensional deep convolutional neural network is utilized as the feature encoder to learn the characteristics of vibration signals in different working conditions. The class prediction vector and feature vector are fused by the multilinear mapping. The fused features are conducted for the dynamic discrimination network for adversarial training. At the same time, considering the statistical alignment and adversarial alignment, the domain adaptation is finally realized. The experimental study demonstrates the effectiveness of the DMDAD.
TCAD Simulation of Single Event Effects on Electronic Devices

In this paper, lateral bulk PIN photodiodes designed at FEI University and SOI (Silicon-on-Insulator) PIN photodiodes from UCL are analyzed as photodetectors and SEE (Single Event Effects) sensors. Numerical simulations are used for radiation analysis of heavy ion striking the PIN photodiode using the parameters extracted by real measurements in a non-radiation condition. The occurrence of SEE near-Earth natural environment is unpredictable and is a serious threat that can lead to malfunctions of microprocessors, memory banks and devices that are vital to the correct behavior of a low-orbit or avionic mission. Detecting their occurrence and monitoring the energetic particles in radiation environments is vital and this paper investigates the consequence of SEE on lateral PIN photodiodes through experimentally calibrated TCAD numerical simulations.
PTF10nvg: AN OUTBURSTING CLASS I PROTOSTAR IN THE PELICAN/NORTH AMERICAN NEBULA

During a synoptic survey of the North American Nebula region, the Palomar Transient Factory (PTF) detected an optical outburst (dubbed PTF10nvg) associated with the previously unstudied flat or rising spectrum infrared source IRAS 20496+4354. The PTF R-band light curve reveals that PTF10nvg brightened by more than 5 mag during the current outburst, rising to a peak magnitude of RPTF ≈ 13.5 in 2010 September. Follow-up observations indicate that PTF10nvg has undergone a similar ∼5 mag brightening in the K band and possesses a rich emission-line spectrum, including numerous lines commonly assumed to trace mass accretion and outflows. Many of these lines are blueshifted by ∼175 km s−1 from the North American Nebula's rest velocity, suggesting that PTF10nvg is driving an outflow. Optical spectra of PTF10nvg show several TiO/VO band heads fully in emission, indicating the presence of an unusual amount of dense (>1010 cm−3), warm (1500–4000 K) circumstellar material. Near-infrared spectra of PTF10nvg appear quite similar to a spectrum of McNeil's Nebula/V1647 Ori, a young star which has undergone several brightenings in recent decades, and 06297+1021W, a Class I protostar with a similarly reached near-infrared emission line spectrum. While further monitoring is required to fully understand this event, we conclude that the brightening of PTF10nvg is indicative of enhanced accretion and outflow in this Class-I-type protostellar object, similar to the behavior of V1647 Ori in 2004–2005.
Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering

A common thread of open-domain question answering (QA) models employs a retriever-reader pipeline that first retrieves a handful of relevant passages from Wikipedia and then peruses the passages to produce an answer. However, even state-of-the-art readers fail to capture the complex relationships between entities appearing in questions and retrieved passages, leading to answers that contradict the facts. In light of this, we propose a novel knowledge Graph enhanced passage reader, namely Grape, to improve the reader performance for open-domain QA. Specifically, for each pair of question and retrieved passage, we first construct a localized bipartite graph, attributed to entity embeddings extracted from the intermediate layer of the reader model. Then, a graph neural network learns relational knowledge while fusing graph and contextual representations into the hidden states of the reader model. Experiments on three open-domain QA benchmarks show Grape can improve the state-of-the-art performance by up to 2.2 exact match score with a negligible overhead increase, with the same retriever and retrieved passages. Our code is publicly available at https://github.com/jumxglhf/GRAPE.
Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells

The activity of the grid cell population in the medial entorhinal cortex (MEC) of the mammalian brain forms a vector representation of the self-position of the animal. Recurrent neural networks have been proposed to explain the properties of the grid cells by updating the neural activity vector based on the velocity input of the animal. In doing so, the grid cell system effectively performs path integration. In this paper, we investigate the algebraic, geometric, and topological properties of grid cells using recurrent network models. Algebraically, we study the Lie group and Lie algebra of the recurrent transformation as a representation of self-motion. Geometrically, we study the conformal isometry of the Lie group representation where the local displacement of the activity vector in the neural space is proportional to the local displacement of the agent in the 2D physical space. Topologically, the compact abelian Lie group representation automatically leads to the torus topology commonly assumed and observed in neuroscience. We then focus on a simple non-linear recurrent model that underlies the continuous attractor neural networks of grid cells. Our numerical experiments show that conformal isometry leads to hexagon periodic patterns in the grid cell responses and our model is capable of accurate path integration. Code is available at \url{https://github.com/DehongXu/grid-cell-rnn}.
Lyapunov Function Consistent Adaptive Network Signal Control with Back Pressure and Reinforcement Learning

This research studies the network trafﬁc signal control problem. It uses the Lyapunov control function to derive the back pressure method, which is equal to differential queue lengths weighted by intersection lane ﬂows. Lyapunov control theory is a platform that uniﬁes several current theories for intersection signal control. We further use the theorem to derive the ﬂow-based and other pressure-based signal control algorithms. For example, the Dynamic, Optimal, Real-time Algorithm for Signals (DORAS) algorithm may be derived by deﬁning the Lyapunov function as the sum of queue length. The study then utilizes the back pressure as a reward in the reinforcement learning (RL) based network signal control, whose agent is trained with double Deep Q-Network (Double-DQN). The proposed algorithm is compared with several traditional and RL-based meth-ods under passenger trafﬁc ﬂow and mixed ﬂow with freight trafﬁc, respectively. The numerical tests are conducted on a single corridor and on a local grid network under three trafﬁc demand scenarios of low, medium, and heavy trafﬁc, respectively. The numerical simulation demonstrates that the proposed algorithm outperforms the others in terms of the average vehicle waiting time on the network.
A Catalog of Habitable Zone Exoplanets

The search for habitable planets has revealed many planets that can vary greatly from an Earth analog environment. These include highly eccentric orbits, giant planets, different bulk densities, relatively active stars, and evolved stars. This work catalogs all planets found to reside in the habitable zone (HZ) and provides HZ boundaries, orbit characterization, and the potential for spectroscopic follow-up observations. Demographics of the HZ planets are compared with a full catalog of exoplanets. Extreme planets within the HZ are highlighted, and how their unique properties may affect their potential habitability is discussed. Kepler-296 f is the most eccentric ≤2 R ⊕ planet that spends 100% of its orbit in the HZ. HD 106270 b and HD 38529 c are the most massive planets (≤13 M J) that orbit within the HZ, and are ideal targets for determining the properties of potential hosts of HZ exomoons. These planets, along with the others highlighted, will serve as special edge cases to the Earth-based scenario, and observations of these targets will help test the resilience of habitability outside the standard model. The most promising observational HZ target that is known to transit is GJ 414 A b. Of the transiting, ≤2 R ⊕ HZ planets, LHS 1140 b, TRAPPIST-1 d, and K2-3 d are the most favorable. Of the nontransiting HZ planets, HD 102365 b and 55 Cnc f are the most promising, and the best nontransiting candidates that have ≤2 R ⊕ are GJ 667 C c, Wolf 1061 c, Ross 508 b, Teegarden’s Star b, and Proxima Cen b.
A correlated pseudo-marginal approach to doubly intractable problems

Doubly intractable models are encountered in a number of ﬁelds, e.g. social networks, ecology and epidemiology. Inference for such models requires the evaluation of a likelihood function, whose normalising function depends on the model parameters and is typically computationally intractable. The normalising constant of the posterior distribution and the additional normalising function of the likelihood function result in a so-called doubly intractable posterior, for which it is difﬁcult to directly apply Markov chain Monte Carlo (MCMC) methods. We propose a signed pseudo-marginal Metropolis-Hastings (PMMH) algorithm with an unbiased block-Poisson estimator to sample from the posterior distribution of doubly intractable models. As the estimator can be negative, the algorithm targets the absolute value of the estimated posterior and uses an importance sampling correction to ensure simulation consistent estimates of the posterior mean of any function. The advantages of our estimator over previous approaches are that its form is ideal for correlated pseudo-marginal methods which are well known to dramatically increase sampling efﬁciency. Moreover, we develop analytically derived heuristic guidelines for optimally tuning the hyperparameters of the estimator. We demonstrate the algorithm on the Ising model and a Kent distribution model for spherical data.