Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Speaker Multi-Style Speech Synthesis with Timbre and Style Disentanglement

Disentanglement of a speaker's timbre and style is very important for style transfer in multi-speaker multi-style text-to-speech (TTS) scenarios. With the disentanglement of timbres and styles, TTS systems could synthesize expressive speech for a given speaker with any style which has been seen in the training corpus. However, there are still some shortcomings with the current research on timbre and style disentanglement. The current method either requires single-speaker multi-style recordings, which are difficult and expensive to collect, or uses a complex network and complicated training method, which is difficult to reproduce and control the style transfer behavior. To improve the disentanglement effectiveness of timbres and styles, and to remove the reliance on single-speaker multi-style corpus, a simple but effective timbre and style disentanglement method is proposed in this paper. The FastSpeech2 network is employed as the backbone network, with explicit duration, pitch, and energy trajectory to represent the style. Each speaker's data is considered as a separate and isolated style, then a speaker embedding and a style embedding are added to the FastSpeech2 network to learn disentangled representations. Utterance level pitch and energy normalization are utilized to improve the decoupling effect. Experimental results demonstrate that the proposed model could synthesize speech with any style seen during training with high style similarity while maintaining very high speaker similarity.
Estimating intracluster correlation for ordinal data

Purpose: In this paper we consider the estimation of intracluster correlation for ordinal data. We focus on pure-tone audiometry hearing threshold data, where thresholds are measured in 5 decibel increments. We estimate the intracluster correlation for tests from iPhone-based hearing assessment application as a measure of test/retest reliability. Methods: We present a method to estimate the intracluster correlation using mixed effects cumulative logistic and probit models, which assume the outcome data are ordinal. This contrasts with using a mixed effects linear model which assumes that the outcome data are continuous. Results: In simulation studies we show that using a mixed effects linear model to estimate the intracluster correlation for ordinal data results in a negative finite sample bias, while using mixed effects cumulative logistic or probit models reduces this bias. The estimated intracluster correlation for the iPhone-based hearing assessment application is higher when using the mixed effects cumulative logistic and probit models compared to using a mixed effects linear model. Conclusion: When data are ordinal, using mixed effects cumulative logistic or probit models reduces the bias of intracluster correlation estimates relative to using a mixed effects linear model.
Tree representations of brain structural connectivity via persistent homology

The brain structural connectome is generated by a collection of white matter fiber bundles constructed from diffusion weighted MRI (dMRI), acting as highways for neural activity. There has been abundant interest in studying how the structural connectome varies across individuals in relation to their traits, ranging from age and gender to neuropsychiatric outcomes. After applying tractography to dMRI to get white matter fiber bundles, a key question is how to represent the brain connectome to facilitate statistical analyses relating connectomes to traits. The current standard divides the brain into regions of interest (ROIs), and then relies on an adjacency matrix (AM) representation. Each cell in the AM is a measure of connectivity, e.g., number of fiber curves, between a pair of ROIs. Although the AM representation is intuitive, a disadvantage is the high-dimensionality due to the large number of cells in the matrix. This article proposes a simpler tree representation of the brain connectome, which is motivated by ideas in computational topology and takes topological and biological information on the cortical surface into consideration. We demonstrate that our tree representation preserves useful information and interpretability, while reducing dimensionality to improve statistical and computational efficiency. Applications to data from the Human Connectome Project (HCP) are considered and code is provided for reproducing our analyses.
Holographic-Type Communication for Digital Twin: A Learning-based Auction Approach

Digital Twin (DT) technologies, which aim to build digital replicas of physical entities, are the key to providing efficient, concurrent simulation and analysis of real-world objects. In displaying DTs, Holographic-Type Communication (HTC), which supports the transmission of holographic data such as Light Field (LF), can provide an immersive way for users to interact with Holographic DTs (HDT). However, it is challenging to effectively allocate interactive and resource-intensive HDT services among HDT users and providers. In this paper, we integrate the paradigms of HTC and DT to form a HTC for DT system, design a marketplace for HDT services where HDT users' and providers' prices are evaluated by their valuation functions, and propose an auction-based mechanism to match HDT services using a learning-based Double Dutch Auction (DDA). Specifically, we apply DDA and train an agent acting as the auctioneer to adjust the auction clock dynamically using Deep Reinforcement Learning (DRL), aiming to achieve the best market efficiency. Simulation results demonstrate that the proposed learning-based auctioneer can achieve near-optimal social welfare at halved auction information exchange cost of the baseline method.
Cluster-Based Autoencoders for Volumetric Point Clouds

Autoencoders allow to reconstruct a given input from a small set of parameters. However, the input size is often limited due to computational costs. We therefore propose a clustering and reassembling method for volumetric point clouds, in order to allow high resolution data as input. We furthermore present an autoencoder based on the well-known FoldingNet [22] for volumetric point clouds and discuss how our approach can be utilized for blending between high resolution point clouds as well as for transferring a volumetric design/style onto a pointcloud while maintaining its shape.
Random Embeddings of Graphs: The Expected Number of Faces in Most Graphs is Logarithmic

A random 2-cell embedding of a connected graph $G$ in some orientable surface is obtained by choosing a random local rotation around each vertex. Under this setup, the number of faces or the genus of the corresponding 2-cell embedding becomes a random variable. Random embeddings of two particular graph classes, those of a bouquet of $n$ loops and those of $n$ parallel edges connecting two vertices, have been extensively studied and are well-understood. However, little is known about more general graphs. The results of this paper explain why Monte Carlo methods cannot work for approximating the minimum genus of graphs. In his breakthrough work [Permutation-partition pairs, JCTB 1991], Stahl developed the foundation of"random topological graph theory". Most of his results have been unsurpassed until today. In our work, we analyze the expected number of faces of random embeddings (equivalently, the average genus) of a graph $G$. It was very recently shown that for any graph $G$, the expected number of faces is at most linear. We show that the actual expected number of faces $F(G)$ is almost always much smaller. In particular, we prove: 1) $\frac{1}{2}\ln n - 2<\mathbb{E}[F(K_n)] \le 3.65 \ln n +o(1)$. 2) For random graphs $G(n,p)$ ($p=p(n)$), we have $\mathbb{E}[F(G(n,p))] \le \ln^2 n+\frac{1}{p}$. 3) For random models $B(n,\Delta)$ containing only graphs, whose maximum degree is at most $\Delta$, we get stronger bounds $\mathbb{E}[F(B(n,\Delta))]=\Theta(\ln n)$.
Constrained high-order statistical blind deconvolution of spectral data

A constrained high-order statistical algorithm is proposed to blindly deconvolute the measured spectral data and estimate the response function of the instruments simultaneously. In this algorithm, no prior-knowledge is necessary except a proper length of the unit-impulse response. This length can be easily set to be the width of the narrowest spectral line by observing the measured data. The feasibility of this method has been demonstrated experimentally by the measured Raman and absorption spectral data.
Learning Melanocytic Cell Masks from Adjacent Stained Tissue

Melanoma is one of the most aggressive forms of skin cancer, causing a large proportion of skin cancer deaths. However, melanoma diagnoses by pathologists shows low interrater reliability. As melanoma is a cancer of the melanocyte, there is a clear need to develop a melanocytic cell segmentation tool that is agnostic to pathologist variability and automates pixel-level annotation. Gigapixel-level pathologist labeling, however, is impractical. Herein, we propose a means to train deep neural networks for melanocytic cell segmentation from hematoxylin and eosin (H&E) stained sections and paired immunohistochemistry (IHC) of adjacent tissue sections, achieving a mean IOU of 0.64 despite imperfect ground-truth labels.
A $\sim$300 pc-sized core of Milky Way dark matter halo constrained from the OGLE micro-lensing sky map

We report the detection of a 282 $^{+34}_{-31}$ pc-sized core in the center of Milky Way dark matter halo at $68\%$ confidence level by using the micro-lensing event rate sky map data from the Optical Gravitational Lensing Experiment (OGLE) survey. We apply the spacial information of the micro-lensing sky map and model it with the detailed Milky Way dark matter halo Core/Cusp profile, and the fraction of dark matter in the form of Mini Dark Matter Structure (MDMS, $f_{\rm MDMS}=\Omega_{\rm MDMS}/\Omega_{\rm DM}$), e.g. primordial black hole, earth-mass subhalos, floating planets and so on. We find that this sky map can constrain both $f_{\rm MDMS}$ and the core size simultaneously without strong degeneracy while fully considering mass function of Milky Way stellar components from both the bulge and disk.
Neural Block-Slot Representations

In this paper, we propose a novel object-centric representation, called BlockSlot Representation. Unlike the conventional slot representation, the Block-Slot Representation provides concept-level disentanglement within a slot. A block-slot is constructed by composing a set of modular concept representations, called blocks, generated from a learned memory of abstract concept prototypes. We call this blockslot construction process Block-Slot Attention. Block-Slot Attention facilitates the emergence of abstract concept blocks within a slot such as color, position, and texture, without any supervision. This brings the benefits of disentanglement into slots and the representation becomes more interpretable. Similar to Slot Attention, this mechanism can be used as a drop-in module in any arbitrary neural architecture. In experiments, we show that our model disentangles object properties significantly better than the previous methods, including complex textured scenes. We also demonstrate the ability to compose novel scenes by composing slots at the blocklevel. https://sites.google. com/view/block-slot-attention
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning

Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning. Their drawbacks are poor scaling with data and the need to run an optimization loop when using a non-Gaussian likelihood. In this paper, we focus on `fantasizing' batch acquisition functions that need the ability to condition on new fantasized data computationally efficiently. By using a sparse Dual GP parameterization, we gain linear scaling with batch size as well as one-step updates for non-Gaussian likelihoods, thus extending sparse models to greedy batch fantasizing acquisition functions.
A Two Step Approach to Weighted Bipartite Link Recommendations

Many real world person-person or person-product relationships can be modeled graphically. More specifically, bipartite graphs can be especially useful when modeling scenarios that involve two disjoint groups. As a result, many existing papers have utilized bipartite graphs for the classical link recommendation problem. In this paper, using the principle of bipartite graphs, we present another approach to this problem with a two step algorithm that takes into account frequency and similarity between common edges to make recommendations. We test this approach with bipartite data gathered from the Epinions and Movielens data sources, and find it to perform with roughly 14 percent error, which improves upon baseline results. This is a promising result, and can be refined to generate even more accurate recommendations.
Molecular Outflows and Mass Loss in Pre Main-Sequence Stars

Molecular outflows are used here to probe mass loss in premain sequence (PMS) stars. Mass-loss rates are determined for 26 objects ranging in luminosity from four to about 100,000 solar, in mass from 0.5 to 30 solar, and in age from about 10,000 to about a million years. The derived mass-loss rates range from 9 x 10 to the -9th to 9 x 10 to the -4th solar mass/yr, with a typical value of 3 x 10 to the -7th solar mass/yr. PMS objects showing mass loss fall in a clearly demarcated region of the H-R diagram. The mass loss is proportional to bolometric luminosity to the 0.6 power and to stellar mass to the 1.8 power. The implications of these findings for the nature of the PMS mass-loss mechanism, for self-regulated low-mass star formation, and for planetary formation are discussed. 117 references.
Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications

Adaptive robotics plays an essential role in achieving truly co-creative cyber physical systems. In robotic manipulation tasks, one of the biggest challenges is to estimate the pose of given workpieces. Even though the recent deep-learning-based models show promising results, they require an immense dataset for training. In this paper, two vision-based, multi-object grasp pose estimation models (MOGPE), the MOGPE Real-Time and the MOGPE High-Precision are proposed. Furthermore, a sim2real method based on domain randomization to diminish the reality gap and overcome the data shortage. Our methods yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place experiment, with the MOGPE Real-Time and the MOGPE High-Precision model respectively. Our framework provides an industrial tool for fast data generation and model training and requires minimal domain-specific data.
Counting and Computing Join-Endomorphisms in Lattices (Revisited)

Structures involving a lattice and join-endomorphisms on it are ubiquitous in computer science. We study the cardinality of the set $\mathcal{E}(L)$ of all join-endomorphisms of a given finite lattice $L$. In particular, we show for $\mathbf{M}_n$, the discrete order of $n$ elements extended with top and bottom, $| \mathcal{E}(\mathbf{M}_n) | =n! \mathcal{L}_n(-1)+(n+1)^2$ where $\mathcal{L}_n(x)$ is the Laguerre polynomial of degree $n$. We also study the following problem: Given a lattice $L$ of size $n$ and a set $S\subseteq \mathcal{E}(L)$ of size $m$, find the greatest lower bound ${\large\sqcap}_{\mathcal{E}(L)} S$. The join-endomorphism ${\large\sqcap}_{\mathcal{E}(L)} S$ has meaningful interpretations in epistemic logic, distributed systems, and Aumann structures. We show that this problem can be solved with worst-case time complexity in $O(mn)$ for distributive lattices and $O(mn + n^3)$ for arbitrary lattices. In the particular case of modular lattices, we present an adaptation of the latter algorithm that reduces its average time complexity. We provide theoretical and experimental results to support this enhancement. The complexity is expressed in terms of the basic binary lattice operations performed by the algorithm.
Theoretical foundations for finite-time transient stability and sensitivity analysis of power systems

Transient stability and sensitivity analysis of power systems are problems of enormous academic and practical interest. These classical problems have received renewed interest, because of the advancement in sensor technology in the form of phasor measurement units (PMUs). The advancement in sensor technology has provided unique opportunity for the development of real-time stability monitoring and sensitivity analysis tools. Transient stability problem in power system is inherently a problem of stability analysis of the non-equilibrium dynamics, because for a short time period following a fault or disturbance the system trajectory moves away from the equilibrium point. The real-time stability decision has to be made over this short time period. However, the existing stability definitions and hence analysis tools for transient stability are asymptotic in nature. In this thesis, we discover theoretical foundations for the short-term transient stability analysis of power systems, based on the theory of normally hyperbolic invariant manifolds and finite time Lyapunov exponents, adopted from geometric theory of dynamical systems. The theory of normally hyperbolic surfaces allows us to characterize the rate of expansion and contraction of co-dimension one material surfaces in the phase space. The expansion and contraction rates of these material surfaces can be computed in finite time. We prove that the expansion and contraction rates can be used as finite time transient stability certificates. Furthermore, material surfaces with maximum expansion and contraction rate are identified with the stability boundaries. These stability boundaries are used for computation of stability margin. We have used the theoretical framework for the development of model-based and model-free real-time stability monitoring methods. Both the model-based and model-free approaches rely on the availability of high resolution time series data from the PMUs for stability prediction. The problem of sensitivity analysis of power system, subjected to changes or uncertainty in load parameters and network topology, is also studied using the theory of normally hyperbolic manifolds. The sensitivity analysis is used for the identification
Coordinated Transmit Beamforming for Multi-Antenna Network Integrated Sensing and Communication

This paper studies a multi-antenna network integrated sensing and communication (ISAC) system, in which a set of multi-antenna base stations (BSs) employ the coordinated transmit beamforming to serve their respectively associated single-antenna communication users (CUs), and at the same time reuse the reflected information signals to perform joint target detection. In particular, we consider two target detection scenarios depending on the time synchronization among BSs. In Scenario I, these BSs are synchronized and can exploit the target-reflected signals over both the direct links (from each BS to target to itself) and the cross links (from each BS to target to other BSs) for joint detection. In Scenario II, these BSs are not synchronized and can only utilize target-reflected signals over the direct links for joint detection. For each scenario, we derive the detection probability under a specific false alarm probability at any given target location. Based on the derivation, we optimize the coordinated transmit beamforming at the BSs to maximize the minimum detection probability over a particular target area, while ensuring the minimum signal-to-interference-plus-noise ratio (SINR) constraints at the CUs, subject to the maximum transmit power constraints at the BSs. We use the semi-definite relaxation (SDR) technique to obtain highly-quality solutions to the formulated problems. Numerical results show that for each scenario, the proposed design achieves higher detection probability than the benchmark scheme based on communication design. It is also shown that the time synchronization among BSs is beneficial in enhancing the detection performance as more reflected signal paths are exploited.
Dual-Polarized Massive MIMO-RSMA Networks: Tackling Imperfect SIC

The polarization domain provides an extra degree of freedom (DoF) for improving the performance of multiple-input multiple-output (MIMO) systems. This paper takes advantage of this additional DoF to alleviate practical issues of successive interference cancellation (SIC) in rate-splitting multiple access (RSMA) schemes. Specifically, we propose three dual-polarized downlink transmission approaches for a massive MIMO-RSMA network under the effects of polarization interference and residual errors of imperfect SIC. The first approach implements polarization multiplexing for transmitting the users’ data messages, which removes the need to execute SIC in the reception. The second approach transmits replicas of users’ messages in the two polarizations, which enables users to exploit diversity through the polarization domain. The third approach, in its turn, employs the original SIC-based RSMA technique per polarization, and this allows the BS to transmit two independent superimposed data streams simultaneously. An in-depth theoretical analysis is carried out, in which we derive tight closed-form approximations for the outage probabilities of the three proposed approaches. Accurate approximations for the ergodic sum-rates of the two first schemes are also derived. Simulation results validate the theoretical analysis and confirm the effectiveness of the proposed schemes. For instance, under low to moderate cross-polar interference, the results show that, even under high levels of residual SIC error, our dual-polarized MIMO-RSMA strategies outperform the conventional single-polarized MIMO-RSMA counterpart. It is also shown that the performance of all RSMA schemes is impressively higher than that of single and dual-polarized massive MIMO systems employing non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) techniques.
DynamicLight: Dynamically Tuning Traffic Signal Duration with DRL

Deep reinforcement learning (DRL) is becoming increasingly popular in implementing traffic signal control (TSC). However, most existing DRL methods employ fixed control strategies, making traffic signal phase duration less flexible. Additionally, the trend of using more complex DRL models makes real-life deployment more challenging. To address these two challenges, we firstly propose a two-stage DRL framework, named DynamicLight, which uses Max Queue-Length to select the proper phase and employs a deep Q-learning network to determine the duration of the corresponding phase. Based on the design of DynamicLight, we also introduce two variants: (1) DynamicLight-Lite, which addresses the first challenge by using only 19 parameters to achieve dynamic phase duration settings; and (2) DynamicLight-Cycle, which tackles the second challenge by actuating a set of phases in a fixed cyclical order to implement flexible phase duration in the respective cyclical phase structure. Numerical experiments are conducted using both real-world and synthetic datasets, covering four most commonly adopted traffic signal intersections in real life. Experimental results show that: (1) DynamicLight can learn satisfactorily on determining the phase duration and achieve a new state-of-the-art, with improvement up to 6% compared to the baselines in terms of adjusted average travel time; (2) DynamicLight-Lite matches or outperforms most baseline methods with only 19 parameters; and (3) DynamicLight-Cycle demonstrates high performance for current TSC systems without remarkable modification in an actual deployment. Our code is released at Github.