Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Optimization For Choice Data

In this work we introduce a new framework for multi-objective Bayesian optimisation where the multi-objective functions can only be accessed via choice judgements, such as "I pick options x1, x2, x3 among this set of five options x1, x2, . , x5". The fact that the option x4 is rejected means that there is at least one option among the selected ones x1, x2, x3 that I strictly prefer over x4 (but I do not have to specify which one). We assume that there is a latent vector function u for some dimension d which embeds the options into the real vector space of dimension d, so that the choice set can be represented through a Pareto set of non-dominated options. By placing a Gaussian process prior on u and by using a novel likelihood model for choice data, we derive a surrogate model for the latent vector function. We then propose two novel acquisition functions to solve the multi-objective Bayesian optimisation from choice data.
Scikit-FIBERS: An 'OR'-Rule Discovery Evolutionary Algorithm for Risk Stratification in Right-Censored Survival Analyses

In 'time-to-event' problems, such as survival analysis in biomedical research, investigators seek to identify the factors that impact/predict not only whether an event occurred (e.g. death), but also when. Further, in the domain of kidney transplantation, there has been recent interest in determining whether higher resolution features, i.e. amino-acid mismatches (AA-MMs) between donors and recipients, can improve risk stratification for transplant failure and identify subsets of AA-MM positions for evaluating risk in future transplants. Motivated by these problems, the FIBERS algorithm was previously developed, which applies a genetic algorithm to learn a population of feature bins (i.e. OR-rules) for predicting right-censored time-to-event survival outcomes. Here, instances are categorized as high-risk if an AA-MM is present for any AA-MM position specified in a given rule, and low-risk if not. In the present study we focus further on the FIBERS algorithm; (1) implementing it as an accessible scikit-learn compatible machine learning package, and (2) applying a variety of simulated right-censored survival datasets to validate scikit-FIBERS efficacy and examine the limitations of its performance to guide ongoing development. We present preliminary results demonstrating the efficacy of scikit-FIBERS, and the limitations of both the algorithm and survival data simulator to guide future work.
Leveraging Zero-Payload Packets to Detect Mobile Malware based on Network Traffic

The rapid growth of mobile malicious applications (apps) poses a serious threat to cyber security. The vast majority of malicious apps steal user data and launch remote attacks via network communications. Existing traffic-based malware detection methods exhibit unsatisfying resource consumption and user privacy leakage in the malware detection process. In this paper, we propose ZeroTraffic, a lightweight but effective detection approach by taking full advantage of zero-payload packets in the TCP protocol. The key insight of our research is that zero-payload packets can be used to reconstruct the behavior patterns of malicious traffic, which differs significantly from benign traffic in terms of communication periodicity and asymmetry of upstream and downstream data. Hence, we can leverage the statistical characteristics based on zero-payload packets to detect mobile malware. In ZeroTraffic, we first summarize and extract the source-oriented and destination-oriented statistical features to enhance the feature expression ability for lightweight data, and then train supervised learning algorithms to build a detector. Note that our model does not involve any user privacy information during its training and detection process. To evaluate the performance of the proposed model, we conduct experiments on a publicly available Andrubis dataset. Experimental results reveal that our method achieves 94.60% accuracy. Besides, our method is superior to a state-of-the-art method in terms of accuracy, precision, recall, and F1-score.
From few to many: generative appearance-based models for face recognition

A generative appearance-based method is presented for recognizing human faces under variations in lighting and pose. It exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a small number of training images of each face taken with different lighting directions, the shape and reflectance properties of the face are reconstructed. A reconstruction is performed both under the Lambertian reflectance assumption as well as with a more general reflectance function. In turn, this reconstruction serves as a generative model used to render—or synthesize—images of the face under novel illumination conditions, accurately predicting both shading and shadows, and thus synthetically forming the illumination cone. The pose space is then sampled, and for each pose its corresponding illumination cone is approximated by a low-dimensional linear subspace whose basis vectors are estimated using the generative model. The presented recognition algorithm assigns to a test image the identity of the closest approximated illumination cone (based on Euclidean distance within the image space). We test the algorithm on 4050 images from the Yale Face Database B; these images contain 405 viewing conditions (9 poses x 45 illumination conditions) for 10 individuals. The method performs almost without error, except on the most extreme lighting directions, and significantly outperforms popular recognition methods that do not use a generative model. An improvement in recognition results is noted when the face representations are created using reconstructions from an extended reconstruction algorithm that incorporates a non-Lambertian reflectance model.
Dynamic community detection based on network structural perturbation and topological similarity

Community detection in dynamic networks has been extensively studied since it sheds light on the structure-function relation of the overall complex systems. Recently, it has been demonstrated that the structural perturbation in static networks is excellent in characterizing the topology. In order to investigate the perturbation structural theory in dynamic networks, we extend the theory by considering the dynamic variation information between networks of consecutive time. Then a novel similarity is proposed by combing structural perturbation and topological features. Finally, we present an evolutionary clustering algorithm to detect dynamic communities under the temporal smoothness framework. Experimental results on both artificial and real dynamic networks demonstrate that the proposed similarity is promising in dynamic community detection since it improves the clustering accuracy compared with state-of-the-art methods, indicating the superiority of the presented similarity measure.
Multi-scale and multi-GMM pooling based on Fisher Kernel for image representation

Image representation is the key part of image classification, and Fisher kernel has been considered as one of the most effective image feature coding methods. For the Fisher encoding method, there is a critical issue that the single GMM only models features within a rough granularity space. In this paper, we propose a method that is named Multi-scale and Multi-GMM Pooling (MMP), which could effectively represent the image from various granularities. We first conduct pooling using the multi-GMM instead of a single GMM. Then, we introduce multi-scale images to enrich the model’s inputs, which could improve the performance further. Finally, we validate out proposal on PASCAL VOC2007 dataset, and the experimental results show an obvious superiority over the basic Fisher model.
Image based computational modeling of intracardiac flows

With continuous advancements in four-dimensional medical imaging technologies, increasing computational speeds, and widespread availability of high performance computing facilities, computational modeling of intracardiac flows is becoming increasingly viable and has the potential to become a powerful non-invasive diagnostic tool for the diagnosis and treatment of cardiovascular disease. The motive of the current study is to develop a modeling framework that facilitates image-based analysis of intracardiac flows in health as well as disease and to use this framework to gain fundamental insights into intracardiac hemodynamics. A procedure is developed for constructing computational fluid dynamics (CFD) – ready models from in vivo imaging data. The key components of this procedure are the registration and segmentation of the 4D data for several (~20) key frames, template based mapping to ensure surface grid conformality and high-fidelity simulations using a sharpinterface immersed boundary solver. A physiologically representative, kinematic model of the mitral valve is also developed for use in these simulations. As a precursor, a comprehensive quantitative validation of the flow solver is performed using experimental data in a simple model of the left ventricle. A quantitative comparison of the phase-averaged velocity and vorticity fields between the simulation and the experiment shows a reasonable agreement. The detailed assessment of this comparison is used to identify and discuss the key challenges and uncertainties associated in conducting such a validation study. The vast majority of computational investigations of intracardiac flows have focused either on the left or the right ventricles while the corresponding atria were modeled in
A computational model of the attention process in risky choice.

A key component of most decision making theories is the decision weight that reflects the importance of consequences when evaluating an action. In algebraic models such as prospect theory or configural weight theories, these are explicit value multipliers with functional forms fit to empirical data. In process models such as decision field theory, elimination-by-aspects, or decision-by-sampling, they represent the distribution of attention about which simplifying assumptions are often made. Still, little is known about the cognitive processes that produce these weights. The current work presents a computational model that derives decision weights from elementary attention processing mechanisms. The basic idea is that a decision weight corresponds to the proportion of times an outcome is predicted to occur when an action is mentally simulated. We demonstrate the model’s success by (a) coherently explaining several robust phenomena with a single set of parameters, including some that utility theories cannot; (b) fitting common functional forms used in algebraic models such as prospect theory, allowing for cognitive processing reinterpretations of their parameters; and (c) quantitatively fitting the model to extensive data sets on choice and pricing.
Spectroscopy of erbium-doped potassium double tungstate waveguides

We report the spectroscopy of crystalline waveguide amplifiers operating in the telecom C-band. Thin films of erbiumdoped gadolinium lutetium potassium double tungstate, KGdxLuyEr1-x-y (WO4)2, are grown by liquid- phase epitaxy onto undoped potassium yttrium double tungstate (KYW) substrates and micro-structured by Ar+- beam etching. Channel waveguides with erbium concentrations between 0.45–6.35 × 1020 cm-3 are characterized. The transition cross-sections of interest are estimated. The effect of energy-transfer up-conversion (ETU) is experimentally investigated. Microscopic and macroscopic ETU parameters are extracted from a simultaneous analysis of 20 decay curves of luminescence on the transition 4I13/2 → 4I13/2. The correlation between ETU and the doping concentration is studied. Pump excited-state absorption (ESA) on the transition 4I11/2 → 4F7/2 is investigated via a direct ESA measurement using a double-modulation pump-probe technique. The effect of ESA is studied for different pump wavelengths. The pump wavelength of 984.5 nm is found to be favorable for the complete range of erbium concentrations.
A stability estimate for fluid structure interaction problem with non-linear beam

In this work we consider the dynamical response of a non-linear beam with viscous damping, perturbed in both the transverse and axial directions interacting with a potential flow. In particular we show that for a class of boundary conditions (clamped beam) and given inlet velocity flow for the fluid, there exists appropriate energy norm for the parameters of the beam (displacements) and flow (potential) bounded by the inlet boundary condition for the fluid flow.
Auralization of a car pass-by using impulse responses computed with the pseudospectral time-domain method

Car noise is the main environmental noise source in the urban environment. In this paper, a method for auralization of a car pass-by in a street from using a wave-based acoustic prediction method is explored. For the transfer paths between sound source locations and a listener, binaural impulse responses are computed with the pseudospectral time-domain method for various source locations. A dry synthesized car signal is convolved with the binaural impulse responses of the different locations in the street and cross-fade windows are used in order to make the transition between the source positions smooth and continuous. The auralizations are performed for the simplified scenarios where buildings are absent, and for an environment where a long building block is located behind the car. A subjective evaluation was carried out in order to detect the maximum spacing between the discrete source positions that still can produce a perceived continuous car pass-by auralization.
Miniature all-fiber extrinsic Fabry–Pérot interferometric sensor for high-pressure sensing under high-temperature conditions

An extrinsic Fabry–Pérot interferometric (EFPI) sensor created at the tip of an optical fiber is presented for high-pressure measurement. The sensor, with a diameter of 125 µm, is fabricated exclusively in pure silica fiber, which makes it suitable for applications in high-temperature environments. The EFPI cavity of the sensor is formed by fusion splicing, cleaving, polishing, and femtosecond laser micromachining. Experimental studies show that the sensor exhibits high stability and good linearity over a pressure range of 10 MPa under high-temperature conditions (up to 800°C). This sensor offers great potential for use in high-temperature applications.
Partial Moment Momentum

Momentum profits benefit from persistent trends of the market, which can be predicted by market volatility. However, such strategies are unable to distinguish between upside and downside risk. We propose partial moments-based momentum trading strategies and find that they outperform plain momentum and volatility-scaled momentum strategies. We suggest that this greater profitability is due to the unexploited investment opportunities that arise from being able to distinguish between good and bad risk. We find strong outperformance for seven out of eight partial moments-based strategies during states of market downturn. The outperformance is robust across different time periods.
The Potential of Neganov-Luke Ampliﬁed Cryogenic Light Detectors and the Scintillation-Light Quenching Mechanism in CaWO 4 Single Crystals in the Context of the Dark Matter Search Experiment CRESST-II

The matter in universe is dominated by currently unknown elementary particles, the dark matter. Within the CRESST collaboration, it is attempted to directly detect dark matter for the first time. The interaction of this unknown kind of matter in the detector material creates phonons and light and allows, thus, for the detection and identification of these unknown particles. Within the present work, a new method for detecting the created light was investigated and a microscopic theory of the light creation in the detector material was developed as well as confirmed by experiments.
Energy-momentum tensor from Wilson flow in lattice φ4-theory

The energy-momentum tensor (EMT) is the Noether current associated with translations. It is of interest because, first of all, it has physical meaning as it contains the energy density and the momentum density. Moreover, its trace can be related to the beta function so that the scaling behaviour of the theory at hand can be studied. We are particularly interested in the scaling behaviour of strongly coupled theories. To explore the strong coupling regime it is necessary to compute the EMT non-perturbatively, i.e. on the lattice. This complicates matters greatly. On the lattice translation invariance is broken which leads to additional terms in the translation Ward identity from which the EMT is derived. This results in turn in the need to renormalise the EMT on the lattice. In this thesis we extend recent studies on the renormalisation of the EMT in four-dimensional gauge theory to the case of a three-dimensional scalar theory to investigate its divergence structure and the numerical feasibility of the suggested procedure on a more basic level. Furthermore, scalar φ-theory in three dimensions exhibits an infrared fixed point and can thus serve as a toy model to examine mechanisms for building theories beyond the standard model. Our strategy to renormalise the EMT on the lattice is to identify all possible terms that can mix with both sides of the translation Ward identity. The renormalised EMT is a combination of operators of the same or lower dimension obeying the symmetries of the theory. The mixing is determined by requiring that the renormalised EMT satisfies the correct Ward identities. Using different probes in the translation Ward identity one can compute the coefficients of the EMT by solving a linear system of equations. However, contact terms can arise. One solution is the recently introduced Wilson flow. Its renormalisation properties allow for expectation values free of contact terms. That way the Wilson flow
Design of a miniature flow cell for in situ x-ray imaging of redox flow batteries

Flow batteries represent a possible grid-scale energy storage solution, having many advantages such as scalability, separation of power and energy capabilities, and simple operation. However, they can suffer from degradation during operation and the characteristics of the felt electrodes are little understood in terms of wetting, compression and pressure drops. Presented here is the design of a miniature flow cell that allows the use of x-ray computed tomography (CT) to study carbon felt materials in situ and operando, in both lab-based and synchrotron CT. Through application of the bespoke cell it is possible to observe felt fibres, electrolyte and pore phases and therefore enables non-destructive characterisation of an array of microstructural parameters during the operation of flow batteries. Furthermore, we expect this design can be readily adapted to the study of other electrochemical systems.
On the Durbin-Watson Bounds Test in the Case of Regression Through the Origin

The Durbin-Watson bounds test does not apply as it stands to the case of regression through the origin. This is due to the fact that the significance points of the lower bound which relate to this case deviate substantially from those available which relate to the case of regression with a fitted intercept. In the present paper, exact significance points of the lower bound for the former case are given. Also, some evidence on the power of the test is presented. As follows from the results obtained, the performance of the test, for the cases considered, is not very encouraging in samples of small size.
A Hybrid Optimization Algorithm for Learning Deep Models

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural network training, but training a neural network can involve thousands of computers for months. In the present study, basic optimization algorithms in deep learning were evaluated. First, a performance criterion was defined based on a training dataset, which makes an objective function along with an adjustment phrase. In the optimization process, a performance criterion provides the least value for objective function. Finally, in the present study, in order to evaluate the performance of different optimization algorithms, recent algorithms for training neural networks were compared for the segmentation of brain images. The results showed that the proposed hybrid optimization algorithm performed better than the other tested methods because of its hierarchical and deeper extraction.
Effective slip for Stokes flow between two grooved walls with an arbitrary phase shift

This work aims to determine how the effective slip length for a wall-bounded flow may depend on, among other geometrical parameters, the phase shift between patterns on the two walls. An analytical model is developed for Stokes flow through a channel bounded by walls patterned with a regular array of rectangular ribs and grooves, where the patterns on the two walls can be misaligned by any phase shift. This study incorporates several previous studies as limiting or special cases. It is shown that the phase shift can have qualitatively different effects on the flow rate and effective slip length, depending on the flow direction. In a narrow channel, increasing the phase shift may mildly decrease the flow rate and effective slip length for flow parallel to the grooves, but can dramatically increase the flow rate and effective slip length for flow transverse to the grooves. It is found that unless the channel height is much larger than the period of the wall pattern, the effect due to wall confinement has to be taken into account on evaluating the effective slip lengths.
Teaching Decision Theory Proof Strategies Using a Crowdsourcing Problem

ABSTRACT Teaching how to derive minimax decision rules can be challenging because of the lack of examples that are simple enough to be used in the classroom. Motivated by this challenge, we provide a new example that illustrates the use of standard techniques in the derivation of optimal decision rules under the Bayes and minimax approaches. We discuss how to predict the value of an unknown quantity, θ ∈ {0, 1}, given the opinions of n experts. An important example of such crowdsourcing problem occurs in modern cosmology, where θ indicates whether a given galaxy is merging or not, and Y1, …, Yn are the opinions from n astronomers regarding θ. We use the obtained prediction rules to discuss advantages and disadvantages of the Bayes and minimax approaches to decision theory. The material presented here is intended to be taught to first-year graduate students.