Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive re-calibration of channel-wise features for Adversarial Audio Classification

DeepFake Audio, unlike DeepFake images and videos, has been relatively less explored from detection perspective, and the solutions which exist for the synthetic speech classification either use complex networks or dont generalize to different varieties of synthetic speech obtained using different generative and optimization-based methods. Through this work, we propose a channel-wise recalibration of features using attention feature fusion for synthetic speech detection and compare its performance against different detection methods including End2End models and Resnet-based models on synthetic speech generated using Text to Speech and Vocoder systems like WaveNet, WaveRNN, Tactotron, and WaveGlow. We also experiment with Squeeze Excitation (SE) blocks in our Resnet models and found that the combination was able to get better performance. In addition to the analysis, we also demonstrate that the combination of Linear frequency cepstral coefficients (LFCC) and Mel Frequency cepstral coefficients (MFCC) using the attentional feature fusion technique creates better input features representations which can help even simpler models generalize well on synthetic speech classification tasks. Our models (Resnet based using feature fusion) trained on Fake or Real (FoR) dataset and were able to achieve 95% test accuracy with the FoR data, and an average of 90% accuracy with samples we generated using different generative models after adapting this framework.
Model-Based Derivative-Free Optimization Methods and Software

This thesis studies derivative-free optimization (DFO), particularly model-based methods and software. These methods are motivated by optimization problems for which it is impossible or prohibitively expensive to access the first-order information of the objective function and possibly the constraint functions. In particular, this thesis presents PDFO, a package we develop to provide both MATLAB and Python interfaces to Powell's model-based DFO solvers, namely COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA. Moreover, a significant part of this thesis is devoted to developing a new DFO method based on the sequential quadratic programming (SQP) method. Therefore, we present an overview of the SQP method and provide some perspectives on its theory and practice. In particular, we show that the objective function of the SQP subproblem is a natural quadratic approximation of the original objective function in the tangent space of a surface. Finally, we elaborate on developing our new DFO method, named COBYQA after Constrained Optimization BY Quadratic Approximations. This derivative-free trust-region SQP method is designed to tackle nonlinearly constrained optimization problems that admit equality and inequality constraints. An important feature of COBYQA is that it always respects bound constraints, if any, which is motivated by applications where the objective function is undefined when bounds are violated. We expose extensive numerical experiments of COBYQA, showing evident advantages of COBYQA compared with Powell's DFO solvers. These experiments demonstrate that COBYQA is an excellent successor to COBYLA as a general-purpose DFO solver. This is the Ph.D. thesis finished under the supervision of Dr. Zaikun Zhang and Prof. Xiaojun Chen at The Hong Kong Polytechnic University. Financial support was provided by the UGC of Hong Kong under the Hong Kong Ph.D. Fellowship Scheme.
Triplet losses-based matrix factorization for robust recommendations

Much like other learning-based models, recommender systems can be affected by biases in the training data. While typical evaluation metrics (e.g. hit rate) are not concerned with them, some categories of final users are heavily affected by these biases. In this work, we propose using multiple triplet losses terms to extract meaningful and robust representations of users and items. We empirically evaluate the soundness of such representations through several"bias-aware"evaluation metrics, as well as in terms of stability to changes in the training set and agreement of the predictions variance w.r. t. that of each user.
Slippage-robust Gaze Tracking for Near-eye Display

In recent years, head-mounted near-eye display devices have become the key hardware foundation for virtual reality and augmented reality. Thus head-mounted gaze tracking technology has received attention as an essential part of human-computer interaction. However, unavoidable slippage of head-mounted devices (HMD) often results higher gaze tracking errors and hinders the practical usage of HMD. To tackle this problem, we propose a slippage-robust gaze tracking for near-eye display method based on the aspheric eyeball model and accurately compute the eyeball optical axis and rotation center. We tested several methods on datasets with slippage and the experimental results show that the proposed method significantly outperforms the previous method (almost double the suboptimal method).
A Q# Implementation of a Quantum Lookup Table for Quantum Arithmetic Functions

In this paper, we present Q# implementations for arbitrary single-variabled fixed-point arithmetic op-erations for a gate-based quantum computer based on lookup tables (LUTs). In general, this is an inefficent way of implementing a function since the number of inputs can be large or even infinite. However, if the input domain can be bounded and there can be some error tolerance in the output (both of which are often the case in practical use-cases), the quantum LUT implementation of certain quantum arithmetic functions can be more efficient than their corresponding reversible arithmetic implementations. We discuss the implementation of the LUT using Q# and its approximation errors. We then show examples of how to use the LUT to implement quantum arithmetic functions and compare the resources required for the implementation with the current state-of-the-art bespoke implementations of some commonly used arithmetic functions. The implementation of the LUT is designed for use by practitioners to use when implementing end-to-end quantum algorithms. In addition, given its well-defined approximation errors, the LUT implementation makes for a clear benchmark for evaluating the efficiency of bespoke quantum arithmetic circuits.
Fuzzy Granular-Ball Computing Framework and Its Implementation in SVM

—Most existing fuzzy computing methods use points as input, which is the ﬁnest granularity from the perspective of granular computing. Consequently, these classiﬁers are neither efﬁcient nor robust to label noise. Therefore, we propose a framework for a fuzzy granular-ball computational classiﬁer by introducing granular-ball computing into fuzzy set. The computational framework is based on the granular-balls input rather than points; therefore, it is more efﬁcient and robust than traditional fuzzy methods. Furthermore, the framework is extended to the fuzzy support vector machine (FSVM), and granular ball fuzzy SVM (GBFSVM) is derived. The experimental results demonstrate the effectiveness and efﬁciency of GBFSVM. The source codes and data sets are available o n the public link: http://www.cquptshuyinxia. com/GBFSVM.html.
Decomposable context-specific models

We introduce a family of discrete context-specific models, which we call decomposable. We construct this family from the subclass of staged tree models known as CStree models. We give an algebraic and combinatorial characterization of all context-specific independence relations that hold in a decomposable context-specific model, which yields a Markov basis. We prove that the moralization operation applied to the graphical representation of a context-specific model does not affect the implied independence relations, thus affirming that these models are algebraically described by a finite collection of decomposable graphical models. More generally, we establish that several algebraic, combinatorial, and geometric properties of decomposable context-specific models generalize those of decomposable graphical models to the context-specific setting.
Shift-Reduce Task-Oriented Semantic Parsing with Stack-Transformers

Intelligent voice assistants, such as Apple Siri and Amazon Alexa, are widely used nowadays. These task-oriented dialog systems require a semantic parsing module in order to process user utterances and understand the action to be performed. This semantic parsing component was initially implemented by rule-based or statistical slot-filling approaches for processing simple queries; however, the appearance of more complex utterances demanded the application of shift-reduce parsers or sequence-to-sequence models. While shift-reduce approaches initially demonstrated to be the best option, recent efforts on sequence-to-sequence systems pushed them to become the highest-performing method for that task. In this article, we advance the research on shift-reduce semantic parsing for task-oriented dialog. In particular, we implement novel shift-reduce parsers that rely on Stack-Transformers. These allow to adequately model transition systems on the cutting-edge Transformer architecture, notably boosting shift-reduce parsing performance. Additionally, we adapt alternative transition systems from constituency parsing to task-oriented parsing, and empirically prove that the in-order algorithm substantially outperforms the commonly-used top-down strategy. Finally, we extensively test our approach on multiple domains from the Facebook TOP benchmark, improving over existing shift-reduce parsers and state-of-the-art sequence-to-sequence models in both high-resource and low-resource settings.
RT-MOT: Confidence-Aware Real-Time Scheduling Framework for Multi-Object Tracking Tasks

Different from existing MOT (Multi-Object Tracking) techniques that usually aim at improving tracking accuracy and average FPS, real-time systems such as autonomous vehicles necessitate new requirements of MOT under limited computing resources: (R1) guarantee of timely execution and (R2) high tracking accuracy. In this paper, we propose RT-MOT, a novel system design for multiple MOT tasks, which addresses R1 and R2. Focusing on multiple choices of a workload pair of detection and association, which are two main components of the tracking-by-detection approach for MOT, we tailor a measure of object confidence for RT-MOT and develop how to estimate the measure for the next frame of each MOT task. By utilizing the estimation, we make it possible to predict tracking accuracy variation according to different workload pairs to be applied to the next frame of an MOT task. Next, we develop a novel confidence-aware real-time scheduling framework, which offers an offline timing guarantee for a set of MOT tasks based on non-preemptive fixed-priority scheduling with the smallest workload pair. At run-time, the framework checks the feasibility of a priority-inversion associated with a larger workload pair, which does not compromise the timing guarantee of every task, and then chooses a feasible scenario that yields the largest tracking accuracy improvement based on the proposed prediction. Our experiment results demonstrate that RT-MOT significantly improves overall tracking accuracy by up to 1.5 ×, compared to existing popular tracking-by-detection approaches, while guaranteeing timely execution of all MOT tasks.
Discrimination Using the Geonics EM63 in a Cued Interrogation Mode at Fort McClellan, AL

Abstract : Data were collected at the former Fort McClellan to test the Geonics EM63 deployed in a cued interrogation mode. Except for one 37mm and several 60mm seed items, all munitions encountered were 75mm or 3.8 inch shrapnel rounds. The EM63 surveys were cued off production mode EM61 data. Polarization tensor models were fit to each surveyed anomaly. Ground truth information was used to train a statistical classifier. For small and medium items, an early time decay rate, equivalent to one that could be obtained with an EM61, provided almost as much discrimination potential as the late time decay rate. The slow rate of data acquisition and the decay curve analysis as good or better than inversion for a physics based model suggest the benefit of the extra information extracted from the EM63 is not justified by the greater costs since some level of discrimination can be obtained without complex inversion and statistical classification, using simpler instrumentation. Better instrumentation and rigorous inversion and statistical classification would still be expected to provide a richer variety of feature vectors to significantly improve discrimination.
Model-based Lifelong Reinforcement Learning with Bayesian Exploration

We propose a model-based lifelong reinforcement-learning approach that estimates a hierarchical Bayesian posterior distilling the common structure shared across different tasks. The learned posterior combined with a sample-based Bayesian exploration procedure increases the sample efficiency of learning across a family of related tasks. We first derive an analysis of the relationship between the sample complexity and the initialization quality of the posterior in the finite MDP setting. We next scale the approach to continuous-state domains by introducing a Variational Bayesian Lifelong Reinforcement Learning algorithm that can be combined with recent model-based deep RL methods, and that exhibits backward transfer. Experimental results on several challenging domains show that our algorithms achieve both better forward and backward transfer performance than state-of-the-art lifelong RL methods.
Management of Machine Learning Lifecycle Artifacts

The explorative and iterative nature of developing and operating ML applications leads to a variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software, configurations, and logs. In order to enable comparability, reproducibility, and traceability of these artifacts across the ML lifecycle steps and iterations, systems and tools have been developed to support their collection, storage, and management. It is often not obvious what precise functional scope such systems offer so that the comparison and the estimation of synergy effects between candidates are quite challenging. In this paper, we aim to give an overview of systems and platforms which support the management of ML lifecycle artifacts. Based on a systematic literature review, we derive assessment criteria and apply them to a representative selection of more than 60 systems and platforms.
D n Dynkin quiver moduli spaces

We study $3d$ $\mathcal{N}=4$ quiver gauge theories with gauge nodes forming a $D_n$ Dynkin diagram. The class of good $D_n$ Dynkin quivers is completely characterised and the moduli space singularity structure fully determined for all such theories. The class of good $D_n$ Dynkin quivers is denoted $D_\nu^\mu(n)_p$ where $n \geq 2$ is an integer, $\nu$ and $\mu$ are integer partitions and $p \in \{ \textrm{even}, \textrm{odd}\}$ denotes membership of one of two broad subclasses. A full assessment of which $\mathfrak{so}_{2n}$ nilpotent varieties are realisable as $D_n$ Dynkin quiver moduli spaces is provided. Quiver addition is introduced and is used to give large subclasses of $D_n$ Dynkin quivers poset structure. The partial ordering is determined by inclusion relations for the moduli space branches. The resulting Hasse diagrams are used to both classify $D_n$ Dynkin quivers and determine the moduli space singularity structure for an arbitrary good theory. The poset constructions and local moduli space analyses are complemented throughout by explicit checks utilising moduli space dimension matching.
Outskirts of dark matter haloes

Halo Models of large scale structure provide powerful and indispensable tools for phenomenological understanding of the clustering of matter in the Universe. While the halo model builds structures out of the superposition of haloes, defining halo profiles in their outskirts - beyond their virial radii - becomes increasingly ambiguous, as one cannot assign matter to individual haloes in a clear way. In this paper, we tackle this question by using numerical N-body simulations to find a systematic definition of mean halo profile that can be extended to large radii. These profiles must be compensated and are the key ingredients for the computation of cosmological correlation functions in the Amended Halo Model. The latter, introduced in our earlier work (arXiv:1912.04872), provides a more physically accurate phenomenological description of nonlinear structure formation, which respects conservation laws on large scales. Here, we extend this model from the matter auto-power spectrum to the halo-matter cross-power spectra, using N-body simulations. We find that the (dimensionless) compensated halo profile, $r^3 \times \rho(r)/M_{200c}$ has a near-universal maximum in the small range $0.03-0.04$ around the virial radius, $r \simeq r_{\rm 200c}$, independent of the halo mass. The profiles cross zero into negative values in the halo outskirts, beyond 2-3$\times r_{\rm 200c}$, consistent with our previous results. We provide preliminary fitting functions for compensated Navarro-Frenk-White (NFW) profiles, and this can be used to compute more physical observables in the Amended Halo Model of large scale structure.
A Polynomial-time Algorithm for the Large Scale of Airplane Refueling Problem

Airplane refueling problem is a nonlinear unconstrained optimization problem with $n! $ feasible solutions. Given a fleet of $n$ airplanes with mid-air refueling technique, the question is to find the best refueling policy to make the last remaining airplane travels the farthest. In order to deal with the large scale of airplanes refueling instances, we proposed the definition of sequential feasible solution by employing the refueling properties of data structure. We proved that if an airplanes refueling instance has feasible solutions, it must have the sequential feasible solutions; and the optimal feasible solution must be the optimal sequential feasible solution. Then we proposed the sequential search algorithm which consists of two steps. The first step of the sequential search algorithm aims to seek out all of the sequential feasible solutions. When the input size of $n$ is greater than an index number, we proved that the number of the sequential feasible solutions will change to grow at a polynomial rate. The second step of the sequential search algorithm aims to search for the maximal sequential feasible solution by bubble sorting all of the sequential feasible solutions. Moreover, we built an efficient computability scheme, according to which we could forecast within a polynomial time the computational complexity of the sequential search algorithm that runs on any given airplanes refueling instance. Thus we could provide a computational strategy for decision makers or algorithm users by considering with their available computing resources.
Splay Top Trees

The top tree data structure is an important and fundamental tool in dynamic graph algorithms. Top trees have existed for decades, and today serve as an ingredient in many state-of-the-art algorithms for dynamic graphs. In this work, we give a new direct proof of the existence of top trees, facilitating simpler and more direct implementations of top trees, based on ideas from splay trees. This result hinges on new insights into the structure of top trees, and in particular the structure of each root path in a top tree.
Baryon Acoustic Oscillations in 2D: Modeling Redshift-space Power Spectrum from Perturbation Theory

We present an improved prescription for the matter power spectrum in redshift space taking proper account of both nonlinear gravitational clustering and redshift distortion, which are of particular importance for accurately modeling baryon acoustic oscillations (BAOs). Contrary to the models of redshift distortion phenomenologically introduced but frequently used in the literature, the new model includes the corrections arising from the nonlinear coupling between the density and velocity fields associated with two competitive effects of redshift distortion, i.e., Kaiser and Finger-of-God effects. Based on the improved treatment of perturbation theory for gravitational clustering, we compare our model predictions with the monopole and quadrupole power spectra of N-body simulations, and an excellent agreement is achieved over the scales of BAOs. Potential impacts on constraining dark energy and modified gravity from the redshift-space power spectrum are also investigated based on the Fisher-matrix formalism, particularly focusing on the measurements of the Hubble parameter, angular diameter distance, and growth rate for structure formation. We find that the existing phenomenological models of redshift distortion produce a systematic error on measurements of the angular diameter distance and Hubble parameter by 1%-2%, and the growth-rate parameter by ∼5%, which would become non-negligible for future galaxy surveys. Correctly modeling redshift distortion is thus essential, and the new prescription for the redshift-space power spectrum including the nonlinear corrections can be used as an accurate theoretical template for anisotropic BAOs.
Iron Gradients in Cooling Flow Galaxies and Groups

Previous studies of the Fe abundances in the hot gas of galaxies and groups have reported conflicting results with most studies finding very subsolar Fe abundances that disagree with standard theory. To investigate the possible role of Fe abundance gradients on these measurements we present deprojection analysis of the ROSAT PSPC data of 10 of the brightest cooling flow galaxies and groups. The PSPC allows for spatially resolved spectral analysis on a half-arcminute scale, and interesting constraints on both the temperatures and Fe abundances are possible because the ~1 keV temperatures of these systems are well matched to the bandpass of the PSPC. In nine out of 10 systems we find clear evidence that the Fe abundance decreases with increasing radius: ZFe ≈ 1-several Z☉ within the central radial bin (r ≲ 10 kpc), which decreases to ZFe ~ 0.5 Z☉ at the largest radii examined (r ~ 50-100 kpc). The Fe abundances (and temperatures) are consistent with the average values for these systems that we obtained in our previous analyses of the ASCA data using multitemperature models, which confirms that previous inferences of very subsolar Fe abundances from ASCA arise from the incorrect assumption of isothermal gas and not the presence of Fe abundance gradients. We discuss why this "Fe bias" affects much more seriously the measurements of ZFe from ASCA data than from ROSAT data. We show that the Fe abundance profiles for these galaxies and groups are consistent with a gasdynamical model where the gas is enriched by stellar ejecta and supernovae in the "solar supernova proportion," the stars formed with a Galactic initial mass function, and the gas is diluted by mixing with primordial gas at large radii.
Collective enhancement in the exciton model

The pre-equilibrium reaction mechanism is considered in the context of the exciton model. A modification to the one-particle one-hole state density is studied which can be interpreted as a collective enhancement. The magnitude of the collective enhancement is set by simulating the Lawrence Livermore National Laboratory (LLNL) pulsed-spheres neutron-leakage spectra. The impact of the collective enhancement is explored in the context of the highly deformed actinide, 239-Pu. A consequence of this enhancement is the removal of fictitious levels in the Distorted-Wave Born Approximation often used in modern nuclear reaction codes.