Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep-Learning-Based Automated Classification of Chinese Speech Sound Disorders

This article describes a system for analyzing acoustic data to assist in the diagnosis and classification of children’s speech sound disorders (SSDs) using a computer. The analysis concentrated on identifying and categorizing four distinct types of Chinese SSDs. The study collected and generated a speech corpus containing 2540 stopping, backing, final consonant deletion process (FCDP), and affrication samples from 90 children aged 3–6 years with normal or pathological articulatory features. Each recording was accompanied by a detailed diagnostic annotation by two speech–language pathologists (SLPs). Classification of the speech samples was accomplished using three well-established neural network models for image classification. The feature maps were created using three sets of MFCC (Mel-frequency cepstral coefficients) parameters extracted from speech sounds and aggregated into a three-dimensional data structure as model input. We employed six techniques for data augmentation to augment the available dataset while avoiding overfitting. The experiments examine the usability of four different categories of Chinese phrases and characters. Experiments with different data subsets demonstrate the system’s ability to accurately detect the analyzed pronunciation disorders. The best multi-class classification using a single Chinese phrase achieves an accuracy of 74.4 percent.
Unsupervised Learning of Hierarchical Conversation Structure

Human conversations can evolve in many different ways, creating challenges for automatic understanding and summarization. Goal-oriented conversations often have meaningful sub-dialogue structure, but it can be highly domain-dependent. This work introduces an unsupervised approach to learning hierarchical conversation structure, including turn and sub-dialogue segment labels, corresponding roughly to dialogue acts and sub-tasks, respectively. The decoded structure is shown to be useful in enhancing neural models of language for three conversation-level understanding tasks. Further, the learned finite-state sub-dialogue network is made interpretable through automatic summarization.
Implications of a Temperature-dependent Initial Mass Function. I. Photometric Template Fitting

A universal stellar initial mass function (IMF) should not be expected from theoretical models of star formation, but little conclusive observational evidence for a variable IMF has been uncovered. In this paper, a parameterization of the IMF is introduced into photometric template fitting of the COSMOS2015 catalog. The resulting best-fit templates suggest systematic variations in the IMF, with most galaxies exhibiting top-heavier stellar populations than in the Milky Way. At fixed redshift, only a small range of IMFs are found, with the typical IMF becoming progressively top-heavier with increasing redshift. Additionally, subpopulations of ULIRGs, quiescent and star-forming galaxies are compared with predictions of stellar population feedback and show clear qualitative similarities to the evolution of dust temperatures.
Forecasting Multilinear Data via Transform-Based Tensor Autoregression

In the era of big data, there is an increasing demand for new methods for analyzing and forecasting 2-dimensional data. The current research aims to accomplish these goals through the combination of time-series modeling and multilinear algebraic systems. We expand previous autoregressive techniques to forecast multilinear data, aptly named the L -Transform Tensor autoregressive ( L -TAR for short). Tensor decompositions and multilinear tensor products have allowed for this approach to be a feasible method of forecasting. We achieve statistical independence between the columns of the observations through invertible discrete linear transforms, enabling a divide and conquer approach. We present an experimental validation of the proposed methods on datasets containing image collections, video sequences, sea surface temperature measurements, stock prices, and networks. The current research was supported in part by the Department of the Navy, Naval Engineering Education Consortium under Grant No. (N00174-19-1-0014) and the National Science Founda-tion under Grant No. (2007367). Marchette was funded by the NSWC Naval Innovative Science and Engineering (NISE) program. Any opinions, ﬁndings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reﬂect the views of the Naval Engineering Education Consortium or the National Science Foundation. Portions of this work were presented in part at the 2021 IEEE/ACM International Conference on Machine Learning and Applications [1].
Safe, Occlusion-Aware Manipulation for Online Object Reconstruction in Confined Spaces

Recent work in robotic manipulation focuses on object retrieval in cluttered spaces under occlusion. Nevertheless, the majority of efforts lack an analysis of conditions for the completeness of the approaches or the methods apply only when objects can be removed from the workspace. This work formulates the general, occlusion-aware manipulation task, and focuses on safe object reconstruction in a confined space with in-place rearrangement. It proposes a framework that ensures safety with completeness guarantees. Furthermore, an algorithm, which is an instantiation of this abstract framework for monotone instances is developed and evaluated empirically by comparing against a random and a greedy baseline on randomly generated experiments in simulation. Even for cluttered scenes with realistic objects, the proposed algorithm significantly outperforms the baselines and maintains a high success rate across experimental conditions.
Faithful Explanations for Deep Graph Models

This paper studies faithful explanations for Graph Neural Networks (GNNs). First, we provide a new and general method for formally characterizing the faithfulness of explanations for GNNs. It applies to existing explanation methods, including feature attributions and subgraph explanations. Second, our analytical and empirical results demonstrate that feature attribution methods cannot capture the nonlinear effect of edge features, while existing subgraph explanation methods are not faithful. Third, we introduce \emph{k-hop Explanation with a Convolutional Core} (KEC), a new explanation method that provably maximizes faithfulness to the original GNN by leveraging information about the graph structure in its adjacency matrix and its \emph{k-th} power. Lastly, our empirical results over both synthetic and real-world datasets for classification and anomaly detection tasks with GNNs demonstrate the effectiveness of our approach.
Solution to the two-body Smoluchowski equation with shear flow for charge-stabilized colloids at low to moderate Péclet numbers.

We developed an analytical theoretical method to determine the microscopical structure of weakly to moderately sheared colloidal suspensions in dilute conditions. The microstructure is described by the static structure factor, obtained by solving the stationary two-body Smoluchowski advection-diffusion equation. The singularly perturbed partial differential equation problem is solved by performing an angular averaging over the extensional and compressing sectors and by the rigorous application of boundary-layer theory (intermediate asymptotics). This allows us to expand the solution to a higher order in Péclet with respect to previous methods. The scheme is independent of the type of interaction potential. We apply it to the example of charge-stabilized colloidal particles interacting via the repulsive Yukawa potential and study the distortion of the structure factor. It is predicted that the distortion is larger at small wave vectors k and its dependence on Pe is a simple power law. At increasing Pe, the main peak of the structure factor displays a broadening and shift toward lower k in the extensional sectors, which indicates shear-induced spreading out of particle correlations and neighbor particles locally being dragged away from the reference one. In the compressing sectors, instead, a narrowing and shift toward high k is predicted, reflecting shear-induced ordering near contact and concomitant depletion in the medium range. An overall narrowing of the peak is also predicted for the structure factor averaged over the whole solid angle. Calculations are also performed for hard spheres, showing good overall agreement with experimental data. It is also shown that the shear-induced structure factor distortion is orders of magnitude larger for the Yukawa repulsion than for the hard spheres.
Influence of lung physical properties on its flow--volume curves using a detailed multi-scale mathematical model of the lung

We develop a mathematical model of the lung that can estimate independently the air flows and pressures in the upper bronchi. It accounts for the lung multi-scale properties and for the air-tissue interactions. The model equations are solved using the Discrete Fourier Transform, which allows quasi instantaneous solving, in the limit of the model hypotheses. With this model, we explore how the air flow--volume curves are affected by airways obstruction or by change in lung compliance. Our work suggests that a fine analysis of the flow-volume curves might bring information about the inner phenomena occurring in the lung.
VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification

Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream task. In this paper, we approach the document classification problem by learning cross-modal representations through language and vision cues, considering intra- and inter-modality relationships. Instead of merging features from different modalities into a joint representation space, the proposed method exploits high-level interactions and learns relevant semantic information from effective attention flows within and across modalities. The proposed learning objective is devised between intra- and inter-modality alignment tasks, where the similarity distribution per task is computed by contracting positive sample pairs while simultaneously contrasting negative ones in the joint representation space}. Extensive experiments on public document classification datasets demonstrate the effectiveness and the generality of our model on low-scale and large-scale datasets.
Masters Thesis: Simulation of level-ground gait with a foot prosthesis

The number of amputees is expected to grow drastically in the coming years as a result of dysvascular disease, trauma and cancer. Unilateral transtibial amputations are the most common type of amputations and thus the need for foot prostheses will increase. Currently, prostheses with improved physiological characteristics are under development, which promise to enhance the quality of life of amputees. This new generation of prostheses include carbon-fibre prosthetic feet and microprocessor-controlled ankle-foot prostheses, which show biomechanical advantages over traditional foot prostheses. New prostheses are mainly evaluated by experiments in gait labs, which is costly and time-consuming, and test users are scarce. Computer simulations of prosthetic gait have a large potential to fulfil this task, because parameters can be varied and tested without the need to construct prototypes and perform experiments. In this study, the main question is whether a walking model could be a test tool in the development of new prostheses. As a first step to investigate this question, gait experiments with unilateral transtibial amputees and five different foot prostheses from Össur are performed. Here the goal is to measure differences between the foot prostheses during level-ground gait, considering overall kinematics, ground reaction forces and prosthesis kinematics. Secondly, a foot prosthesis model is proposed that captures the mechanical properties of the different prosthetic feet. This prosthesis model also gives insight into the parameters that influence two important functions of the foot prosthesis, namely plantarand dorsiflexion as well as energy storage and return during the stance phase. Next, a muscle-reflex walking model is adapted for simulation of unilateral transtibial amputee gait and the prosthesis model is integrated into the walking model. Lastly, simulations of level-ground gait with the different foot prostheses are carried out. The simulation results are compared to experimental data and outcomes found in literature, also considering prosthesis energy storage and return, muscle activity and energy consumption. It is shown that the muscle-reflex walking model can adapt to the situation of a unilateral transtibial amputation and is able to reproduce human-like overall kinematics. However, discrepancies in magnitude and phase are found between the simulation results and normative data. Ground reaction forces reproduced with the walking model have the same order of magnitude, but the shape of the forces was unrealistic. Characteristics of unilateral transtibial prosthetic gait can nevertheless be observed in the simulation results. Master of Science Thesis CONFIDENTIAL Siebert Frieling
D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat

In a depression-diagnosis-directed clinical session, doctors initiate a conversation with ample emotional support that guides the patients to expose their symptoms based on clinical diagnosis criteria. Such a dialogue system is distinguished from existing single-purpose human-machine dialog systems, as it combines task-oriented and chit-chats with uniqueness in dialogue topics and procedures. However, due to the social stigma associated with mental illness, the dialogue data related to depression consultation and diagnosis are rarely disclosed. Based on clinical depression diagnostic criteria ICD-11 and DSM-5, we designed a 3-phase procedure to construct D^4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat, which simulates the dialogue between doctors and patients during the diagnosis of depression, including diagnosis results and symptom summary given by professional psychiatrists for each conversation. Upon the newly-constructed dataset, four tasks mirroring the depression diagnosis process are established: response generation, topic prediction, dialog summary, and severity classification of depressive episode and suicide risk. Multi-scale evaluation results demonstrate that a more empathy-driven and diagnostic-accurate consultation dialogue system trained on our dataset can be achieved compared to rule-based bots.
SUBGEOMETRICALLY ERGODIC AUTOREGRESSIONS WITH AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY

In this paper, we consider subgeometric (specifically, polynomial) ergodicity of univariate nonlinear autoregressions with autoregressive conditional heteroskedasticity (ARCH). The notion of subgeometric ergodicity was introduced in the Markov chain literature in the 1980s, and it means that the transition probability measures converge to the stationary measure at a rate slower than geometric; this rate is also closely related to the convergence rate of $\beta $ -mixing coefficients. While the existing literature on subgeometrically ergodic autoregressions assumes a homoskedastic error term, this paper provides an extension to the case of conditionally heteroskedastic ARCH-type errors, considerably widening the scope of potential applications. Specifically, we consider suitably defined higher-order nonlinear autoregressions with possibly nonlinear ARCH errors and show that they are, under appropriate conditions, subgeometrically ergodic at a polynomial rate. An empirical example using energy sector volatility index data illustrates the use of subgeometrically ergodic AR–ARCH models.
SparseLNR: accelerating sparse tensor computations using loop nest restructuring

Sparse tensor algebra computations have become important in many real-world applications like machine learning, scientific simulations, and data mining. Hence, automated code generation and performance optimizations for tensor algebra kernels are paramount. Recent advancements such as the Tensor Algebra Compiler (TACO) greatly generalize and automate the code generation for tensor algebra expressions. However, the code generated by TACO for many important tensor computations remains suboptimal due to the absence of a scheduling directive to support transformations such as distribution/fusion. This paper extends TACO's scheduling space to support kernel distribution/loop fusion in order to reduce asymptotic time complexity and improve locality of complex tensor algebra computations. We develop an intermediate representation (IR) for tensor operations called branched iteration graph which specifies breakdown of the computation into smaller ones (kernel distribution) and then fuse (loop fusion) outermost dimensions of the loop nests, while the inner-most dimensions are distributed, to increase data locality. We describe exchanges of intermediate results between space iteration spaces, transformation in the IR, and its programmatic invocation. Finally, we show that the transformation can be used to optimize sparse tensor kernels. Our results show that this new transformation significantly improves the performance of several real-world tensor algebra computations compared to TACO-generated code.
Rate-Splitting Multiple Access and Its Interplay with Intelligent Reflecting Surfaces

Rate-splitting multiple access (RSMA) has recently appeared as a powerful technique for improving the downlink performance of multiple-input multiple-output systems. By flexibly managing interference, RSMA can deliver high spectral and energy efficiency, as well as robustness to imperfect channel state information. In another development, an intelligent reflecting surface (IRS) has emerged as a method to control the wireless environment through software-configurable, near-passive, sub-wavelength reflecting elements. This article presents the potential of synergy between IRS and RSMA. Three important improvements achievable by IRS-RSMA schemes are identified, supported by insightful numerical examples, and mapped to beyond 5G use cases, along with future research directions.
An intelligent controller for underactuated mechanical systems

This paper presents an intelligent controller for uncertain underactuated nonlinear systems. The adopted approach is based on sliding mode control and enhanced by an artiﬁcial neural network to cope with modeling inaccuracies and external disturbances that can arise. The sliding surfaces are deﬁned as a linear combination of both actuated and unactuated variables. A radial basis function is added to compensate the performance drop when, in order to avoid the chattering phenomenon, the sign function is substituted by a saturation function in the conventional sliding mode controller. An application of the proposed scheme is introduced for an inverted pendulum, in order to illustrate the controller design method, and numerical results are presented to demonstrate the improved performance of the resulting intelligent controller. technique because of its robustness against both structured and unstructured uncertainties as well as external disturbances. Nevertheless, the discontinuities in the control law must be smoothed out to avoid the undesirable chattering eﬀects. The adoption of properly designed boundary layers have proven eﬀective in completely eliminating chattering, however, leading to an inferior tracking performance, as demonstrated by Bessa (2009).
Automatic Verification of Sound Abstractions for Generalized Planning

Generalized planning studies the computation of general solutions for a set of planning problems. Computing general solutions with correctness guarantee has long been a key issue in generalized planning. Abstractions are widely used to solve generalized planning problems. Solutions of sound abstractions are those with correctness guarantees for generalized planning problems. Recently, Cui et al. proposed a uniform abstraction framework for generalized planning. They gave the model-theoretic definitions of sound and complete abstractions for generalized planning problems. In this paper, based on Cui et al.'s work, we explore automatic verification of sound abstractions for generalized planning. We firstly present the proof-theoretic characterization for sound abstraction. Then, based on the characterization, we give a sufficient condition for sound abstractions which is first-order verifiable. To implement it, we exploit regression extensions, and develop methods to handle counting and transitive closure. Finally, we implement a sound abstraction verification system and report experimental results on several domains.
Hierarchical Vectorization for Portrait Images

Aiming at developing intuitive and easy-to-use portrait editing tools, we propose a novel vectorization method that can automatically convert raster images into a 3-tier hierarchical representation. The base layer consists of a set of sparse diffusion curves (DC) which characterize salient geometric features and low-frequency colors and provide means for semantic color transfer and facial expression editing. The middle level encodes specular highlights and shadows to large and editable Poisson regions (PR) and allows the user to directly adjust illumination via tuning the strength and/or changing shape of PR. The top level contains two types of pixel-sized PRs for high-frequency residuals and fine details such as pimples and pigmentation. We also train a deep generative model that can produce high-frequency residuals automatically. Thanks to the meaningful organization of vector primitives, editing portraits becomes easy and intuitive. In particular, our method supports color transfer, facial expression editing, highlight and shadow editing and automatic retouching. Thanks to the linearity of the Laplace operator, we introduce alpha blending, linear dodge and linear burn to vector editing and show that they are effective in editing highlights and shadows. To quantitatively evaluate the results, we extend the commonly used FLIP metric (which measures differences between two images) by considering illumination. The new metric, called illumination-sensitive FLIP or IS-FLIP, can effectively capture the salient changes in color transfer results, and is more consistent with human perception than FLIP and other quality measures on portrait images. We evaluate our method on the FFHQR dataset and show that our method is effective for common portrait editing tasks, such as retouching, light editing, color transfer and expression editing. We will make the code and trained models publicly available.
Soft-SVM Regression For Binary Classification

The binomial deviance and the SVM hinge loss functions are two of the most widely used loss functions in machine learning. While there are many similarities between them, they also have their own strengths when dealing with different types of data. In this work, we introduce a new exponential family based on a convex relaxation of the hinge loss function using softness and class-separation parameters. This new family, denoted Soft-SVM, allows us to prescribe a generalized linear model that effectively bridges between logistic regression and SVM classification. This new model is interpretable and avoids data separability issues, attaining good fitting and predictive performance by automatically adjusting for data label separability via the softness parameter. These results are confirmed empirically through simulations and case studies as we compare regularized logistic, SVM, and Soft-SVM regressions and conclude that the proposed model performs well in terms of both classification and prediction errors.