Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite Element Modelling of Chiasmal Compression

It has long been known that bitemporal hemianopsia is caused by an extrinsic pressure compressing the optic chiasm. What is not agreed upon is the mechanism which discriminates between the nasal crossing fibers and temporal non-crossing fibers, so selectively. It has been proposed that it is the pressure differential caused by the geometric difference between crossing and non-crossing fibers which accurately targets the crossing fibers. That theory has been investigated in this project using finite element modeling of the optic chiasm, tumors and nerves. Using information obtained through literature, models have been prepared and tested, which simulate the forces experienced within the chiasm and nerves due to the growth of a tumor below it. These models have revealed that there is a concentration of pressure in the central chiasm because of its shape, and that the geometric difference between crossing and non-crossing nerves causes an exaduration in the pressure induced within the crossing nerves. Using high resolution diffusion tensor images to identify typical areas of crossing and non-crossing nerves within the chiasm we are able to conclude that a crossing nerve is subject to higher pressure than a non-crossing nerve because of both its location within the chiasm and its geometry. This provides a mechanical mechanism which accurately discriminates between crossing and non-crossing nerves in bitemporal hemianopsia.
Counterfactual Prediction Methods for Causal Inference in Observational Studies with Continuous Treatments

We develop methods for estimation, inference and optimization of causal effects from observational data with continuous treatments. We present a counterfactual prediction method based on the potential outcomes framework that estimates the expected value of a potential outcome given a treatment level and confounders. We show that the method identifies the average generalized treatment effect (AGTE) and the average dose-response function (ADRF) and propose estimators of these functional causal estimands. Our estimators work under high-dimensional confounding and when the treatment takes many distinct values. Under multiple treatments, the method identifies the effect of a single treatment and the joint effect of multiple treatments. Treatment effects can further be estimated from unobserved treatment levels. We provide non-parametric and computationally efficient parametric estimation procedures of uncertainty intervals of the ADRF and AGTE and develop algorithms for implementation of the estimators. Finally, we show that the counterfactual prediction method can be used to estimate the treatment level that maximizes the expected individual and population outcome. (Less)
Linear tides in inspiraling white dwarf binaries: resonance locks

We calculate the tidal response of helium and carbon/oxygen (C/O) white dwarf (WD) binaries inspiraling due to gravitational wave emission. We show that resonance locks, previously considered in binaries with an early-type star, occur universally in WD binaries. In a resonance lock, the orbital and spin frequencies evolve in lockstep, so that the tidal forcing frequency is approximately constant and a particular normal mode remains resonant, producing efficient tidal dissipation and nearly synchronous rotation. Resonance locks in WDs can occur not only with global standing modes, but even when damping is so efficient that the resonant tidal response is a traveling wave. We derive simple analytic formulas for the tidal quality factor Q and tidal heating rate during a g-mode resonance lock, and verify our results numerically. We find that Q ~ 3 * 10^6 for orbital periods ~ 1 - 2 hr in C/O WDs, and Q ~ 3 * 10^8 for P_orb ~ 3 - 10 hr in helium WDs. Typically tidal heating occurs sufficiently close to the surface that the energy should be observable as surface emission. Moreover, near an orbital period of ~ 10 min, the tidal heating rate reaches ~ 10^{-2} L_\sun, rivaling the luminosities of our fiducial WD models. Recent observations of the 13-minute double-WD binary J0651 are roughly consistent with our theoretical predictions. Tides naturally tend to generate differential rotation; however, we show that the fossil magnetic field strength of a typical WD can maintain solid-body rotation down to at least P_orb ~ 10 min even in the presence of a tidal torque concentrated near the WD surface.
Spectral–Spatial Feature Extraction for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection faces various levels of difficulty due to the high dimensionality of hyperspectral images (HSIs), redundant information, noisy bands, and the limited capability of utilizing spectral–spatial information. In this paper, we address these problems and propose a novel approach, called spectral–spatial feature extraction (SSFE), which is based on two main aspects. In the spectral domain, we assume that the anomalous pixels are rarely present and all (or most) of the samples around the anomalies belong to background (BKG). Using this fact, we introduce a suppression function to construct a discriminative feature space and utilize a deep brief network to learn spectral representation and abstraction automatically that are used as inputs to the Mahalanobis distance (MD)-based detector. In the spatial domain, the anomalies appear as a small area grouped by pixels with high correlation among them compared to BKG. Therefore, the objects appearing as a small area are extracted based on attribute filtering, and a guided filter is further employed for local smoothness. More specifically, we extract spatial features of anomalies only from one single band obtained by fusing all bands in the visible wavelength range. Finally, we detect anomalies by jointly considering the spectral and spatial detection results. Several experiments are performed, which show that our proposed method outperforms the state-of-the-art methods.
H\^older continuity of solutions of second-order non-linear elliptic integro-differential equations

This paper is concerned with H\"older regularity of viscosity solutions of second-order, fully non-linear elliptic integro-differential equations. Our results rely on two key ingredients: first we assume that, at each point of the domain, either the equation is strictly elliptic in the classical fully non-linear sense, or (and this is the most original part of our work) the equation is strictly elliptic in a non-local non-linear sense we make precise. Next we impose some regularity and growth conditions on the equation. These results are concerned with a large class of integro-differential operators whose singular measures depend on $x$ and also a large class of equations, including Bellman-Isaacs Equations.
Optimal Design of Flux Diverter Using Genetic Algorithm for Axial Short Circuit Force Reduction in HTS Transformers

The appealing advantages of high-temperature superconducting (HTS) power transformers over conventional ones have attracted transformer manufacturing companies, power companies, research institutes, and universities worldwide to conduct research and development in this field. Unfortunately, HTS transformers are more vulnerable to mechanical stresses than conventional transformers. The results of the interaction between current carrying windings and leakage magnetic fluxes are the electromagnetic forces, which act on transformer windings. Under short circuit events, these forces are remarkable, and, therefore, catastrophic failure of transformer may arise. Flux-diverter applications have been reported in earlier literatures for increasing critical current or decreasing ac losses in HTS transformers. In this paper, a genetic algorithm based method is employed for the optimal design of flux diverter to minimize the axial short circuit force, in design stage of a sample 132/13.8 kV, 50 MVA three-phase core type HTS transformer. In this paper, the optimal dimensions, placement, and permeability of a flux diverter have been determined. It has been shown that utilizing this optimized flux diverter, the axial short circuit forces have been reduced significantly. Electromagnetic modeling and simulations, by the application of finite element method, have been employed for the verification of the analytical method results. A high degree of consistency has been observed between the analytical results and the simulation results.
Global asymptotic stability analysis for neutral-type complex-valued neural networks with random time-varying delays

ABSTRACT This paper investigates the global asymptotic stability problem for a class of neutral-type complex-valued neural networks with random time-varying delays. By introducing a stochastic variable with Bernoulli distribution, the information of time-varying delay is assumed to be random time-varying delays. By constructing an appropriate Lyapunov–Krasovskii functional and employing inequality technique, several sufficient conditions are obtained to ensure the global asymptotically stability of equilibrium point for the considered neural networks. The obtained stability criterion is expressed in terms of complex-valued linear matrix inequalities, which can be simply solved by effective YALMIP toolbox in MATLAB. Finally, three numerical examples are given to demonstrate the efficiency of the proposed main results.
Local Li-Yau's estimates on $RCD^*(K,N)$ metric measure spaces

In this paper, we will study the (linear) geometric analysis on metric measure spaces. We will establish a local Li-Yau's estimate for weak solutions of the heat equation and prove a sharp Yau's gradient gradient for harmonic functions on metric measure spaces, under the Riemannian curvature-dimension condition $RCD^*(K,N). $
Supporting the Teaching of Information Literacy with First Year Bsc Nursing Students: The Case for a Printed Workbook

ABSTRACT Information literacy (IL) is increasingly considered to be an important life skill and there are a number of challenges that librarians face in implementing an effective IL program. This article examines the experience of library staff at Birmingham City University (BCU) and how they developed a printed workbook to be embedded within sessions attached to a first year Nursing degree academic skills module. Previous approaches to IL input with Nursing students will be discussed. The changes that were made and the impact of these changes on students, academic and library staff are then considered. Since the implementation of the workbook, feedback from students and staff has been encouraging concerning its purpose and value. Possible future developments are then suggested.
University of Birmingham Probabilistic planning with formal performance guarantees for mobile service robots

We present a framework for mobile service robot task planning and execution, based on the use of probabilistic veriﬁcation techniques for the generation of optimal policies with attached formal performance guarantees. Our approach is based on a Markov decision process model of the robot in its environment, encompassing a topological map where nodes represent relevant locations in the environment, and a range of tasks that can be executed in different locations. The navigation in the topological map is modelled stochastically for a speciﬁc time of day. This is done by using spatio-temporal models that provide, for a given time of day, the probability of successfully navigating between two topological nodes, and the expected time to do so. We then present a methodology to generate cost optimal policies for tasks speciﬁed in co-safe linear temporal logic. Our key contribution is to address scenarios in which the task may not be achievable with probability one. We formalise a task progression metric and present an approach to generate policies that are formally guaranteed to, in decreasing order of priority: maximise the probability of ﬁnishing the task; maximise progress towards completion, if this is not possible; and minimise the expected time or cost required. We illustrate and evaluate our approach with a scalability evaluation in a simulated scenario, and reporting on its implementation in a robot performing service tasks in an ofﬁce environment for long periods of time.
Highly sensitive PDMS-filled Fabry-Perot interferometer temperature sensor based on the Vernier effect.

A highly sensitive temperature sensor is demonstrated experimentally, which is fabricated based on a Fabry-Perot interferometer (FPI) filled with polydimethylsiloxane (PDMS). The sensor's sensitivity is -0.653 nm/°C by utilizing the thermal expansion effect of PDMS, which has been greatly improved compared to that of the traditional FPI temperature sensor. Moreover, in order to further improve the sensitivity, a scheme where two parallel FPI structures are used to form the Vernier effect is proposed, which are a sensing FPI and reference FPI, respectively. Such a temperature sensor based on the FPI filled with PDMS and the Vernier effect exhibits a high temperature sensitivity of 17.758 nm/°C. Meanwhile, the proposed sensors show the advantages of high sensitivity, simplicity, and low cost.
From diffusion to reaction via Gamma-convergence

We study the limit of high activation energy of a special Fokker-Planck equation, known as Kramers-Smoluchowski (K-S) equation. This equation governs the time evolution of the probability density of a particle performing a Brownian motion under the influence of a chemical potential H/e. We choose H having two wells corresponding to two chemical states A and B. We prove that after a suitable rescaling the solution to (K-S) converges, in the limit of high activation energy (e -> 0), to the solution of a simple system modeling the diffusion of A and B, and the reaction A B. The aim of this paper is to give a rigorous proof of Kramer's formal derivation and to embed chemical reactions and diffusion processes in a common variational framework which allows to derive the former as a singular limit of the latter, thus establishing a connection between two worlds often regarded as separate. The singular limit is analysed by means of Gamma-convergence in the space of finite Borel measures endowed with the weak-* topology.
Segmentation Convolutional Neural Networks for Automatic Crater Detection on Mars

Machine learning segmentation techniques show great promise for automating historically tedious tasks for planetary scientists. One such task is crater counting, which is commonly used by the planetary science community to study the absolute and relative ages of planetary bodies. Developing effective segmentation neural networks for tasks such as crater counting involves multiple design choices in the network architecture and training set preparation. Here, the authors evaluate two target types, measure the impact of hyperparameters (kernel size, filters), and vary the amount of data used to train the models from using 3 to 15 of the 24 tiles. (Each tile is 30° by 30° and is within ±30° latitude.) The algorithm is trained using annotations of 2- to 32-km-radius Martian craters and THEMIS Daytime IR images. Pixel-based machine learning metrics like loss and accuracy are used during training and validation. In addition, crater count metrics such as the recall (the match ratio), the precision, and the F1 score are used to evaluate the performance and for model selection. The results enumerate how incorporating machine learning into the crater counting process is beneficial to planetary geologists, for example, by creating a list of craters in a region or suggesting potential degraded craters for further analysis. A segmentation network using convolutional neural networks is successfully implemented to find 65%–76% of craters in common with a human annotated dataset.
Strongly interacting multi-solitons with logarithmic relative distance for the gKdV equation

We consider the following class of equations of (gKdV) type with mass sub-critical (2p>5). We prove the existence of two-solitary wave solutions with logarithmic relative distance, i.e. solutions u(t) satisfying where 0$ ? >c=c(p)>0 is a fixed constant, σ=−1 in sub-critical cases and σ=1 in super-critical cases. This regime corresponds to strong attractive interactions. For sub-critical p, it was known that opposite-sign traveling waves are attractive. For super-critical p, we derive from our computations that same-sign traveling waves are attractive.
Early Quintessence in Light of the Wilkinson Microwave Anisotropy Probe

We examine the cosmic microwave background (CMB) anisotropy for signatures of early quintessence dark energy—a nonnegligible quintessence energy density during the recombination and structure formation eras. Only very recently does the quintessence overtake the dark matter and push the expansion into overdrive. Because the presence of early quintessence exerts an influence on the clustering of dark matter and the baryon-photon fluid, we may expect to find trace signals in the CMB and the mass fluctuation power spectrum. In detail, we demonstrate that suppressed clustering power on small length scales, as suggested by the combined Wilkinson Microwave Anisotropy Probe/CMB/large-scale structure data set, is characteristic of early quintessence. We identify a set of concordant models and map out directions for further investigation of early quintessence.
On stable fixed points under several kinds of strong perturbations

This gives new results on stable fixed points related to several kinds of strong perturbations in references. It is shown that a strong stable set of fixed points has a robust stable property. For a robust stable fixed point set of a correspondence, in its neighborhood, there is a strong stable set for any small perturbation of the correspondence. There exists a robust stable set for a correspondence, if there is at least one fixed point for the correspondence. These generalize the corresponding results in recent references and give an application in the existence of strong stable economy equilibria.
Critical two-point function for long-range models with power-law couplings: The marginal case for $d\ge d_c$.

Consider the long-range models on $\mathbb{Z}^d$ of random walk, self-avoiding walk, percolation and the Ising model, whose translation-invariant 1-step distribution/coupling coefficient decays as $|x|^{-d-\alpha}$ for some $\alpha>0$. In the previous work (Ann. Probab., 43, 639--681, 2015), we have shown in a unified fashion for all $\alpha\ne2$ that, assuming a bound on the "derivative" of the $n$-step distribution (the compound-zeta distribution satisfies this assumed bound), the critical two-point function $G_{p_c}(x)$ decays as $|x|^{\alpha\wedge2-d}$ above the upper-critical dimension $d_c\equiv(\alpha\wedge2)m$, where $m=2$ for self-avoiding walk and the Ising model and $m=3$ for percolation. In this paper, we show in a much simpler way, without assuming a bound on the derivative of the $n$-step distribution, that $G_{p_c}(x)$ for the marginal case $\alpha=2$ decays as $|x|^{2-d}/\log|x|$ whenever $d\ge d_c$ (with a large spread-out parameter $L$). This solves the conjecture in the previous work, extended all the way down to $d=d_c$, and confirms a part of predictions in physics (Brezin, Parisi, Ricci-Tersenghi, J. Stat. Phys., 157, 855--868, 2014). The proof is based on the lace expansion and new convolution bounds on power functions with log corrections.
Hyper-Raman lines emission concomitant with high-order harmonic generation

Hyper-Raman lines (HRL) resulting from strong-field light–matter interaction have been predicted theoretically in the 1990s but never identified in high-order harmonic generation experiments. Here, we use a combination of 800 and 400 nm laser pulses to control independently the two processes required for the hyper-Raman emission: creation of a coherence between two electronic states and laser-dressing of these states. As a result we observe simultaneously high-order harmonics, XUV free induction decay and HRL. We investigate experimentally and numerically the properties of this novel emission source. It can be of high interest, amongst others, for high-resolution spatio-temporal spectroscopy of excited electronic states in the same fashion high-order harmonics generation provides it for ground state.
Complete framework for memory consistency with applications to the itanium architecture

Multiprocessor computers are now common in the marketplace and will soon be on every desktop. A vital part of a multiprocessor computer's architecture is a definition of how the processors access common storage, that is, its memory consistency model. In order for programmers to write code that makes full use of the power of these computers they must comprehend completely the memory consistency model. The memory consistency model is usually complex, difficult to describe completely, and very difficult for programmers to use. The memory consistency models of the various architectures are often described using very diverse language. Attempts to simplify the language that describe the memory consistency models so that they are either easier for the programmer to use or so that similar descriptions can be used for various architectures generally fail to model all components of the system. In this thesis a previous uniform framework for describing memory consistency models is extended, allowing a general description of the models of any architecture that completely captures the memory features. It can model the relationship between programs, written in whatever language the programmer chooses, and the computations that arise from executions of these programs on the chosen architecture. Also, it precisely models dependency relationships. Using this framework two systems are defined; Itanium+A which is stronger than and Itanium+B which is weaker than the specifications of the memory consistency model from Intel's Itanium architecture. Itanium+A and Itanium+B differ very little. Itanium+A and Itanium+B are used to define the requirements for processor coordination on Itanium and to define and prove coordination algorithms for Itanium. Outside of the framework, Sun's Sparc architecture and Intel's Itanium architecture are compared, using descriptions that follow each architecture's specification. This found similarities when resticted to very few memory access instructions, but general incomparability.
IMAGE INTENSITY OF A GAUSSIAN ROUGH-SURFACE IN ATMOSPHERIC TURBULENCE

Based on Huygens-Fresnel principle, a general expression for the average image intensity of a heterodyne and a direct-detection imaging system in turbulent media is derived under the assumption of a Gaussian rough-surface model. From the formulation, we find that the object size, turbulence strength, wavelength, and object roughness affect image intensity dramatically in the image plane.