Miracle-dz/UniFAR-FTUs huggingface dataset:
Ringdown overtones, black hole spectroscopy, and no-hair theorem tests

Validating the black-hole no-hair theorem with gravitational-wave observations of compact binary coalescences provides a compelling argument that the remnant object is indeed a black hole as described by the general theory of relativity. This requires performing a spectroscopic analysis of the post-merger signal and resolving the frequencies of either different angular modes or overtones (of the same angular mode). For a nearly-equal mass binary black-hole system, only the dominant angular mode ($l=m=2$) is sufficiently excited and the overtones are instrumental to perform this test. Here we investigate the robustness of modelling the post-merger signal of a binary black hole coalescence as a superposition of overtones. Further, we study the bias expected in the recovered frequencies as a function of the start time of a spectroscopic analysis and provide a computationally cheap procedure to choose it based on the interplay between the expected statistical error due to the detector noise and the systematic errors due to waveform modelling. Moreover, since the overtone frequencies are closely spaced, we find that resolving the overtones is particularly challenging and requires a loud ringdown signal. Rayleigh's resolvability criterion suggests that in an optimistic scenario a ringdown signal-to-noise ratio larger than $\sim 30$ (achievable possibly with LIGO at design sensitivity and routinely with future interferometers such as Einstein Telescope, Cosmic Explorer, and LISA) is necessary to resolve the overtone frequencies. We then conclude by discussing some conceptual issues associated with black-hole spectroscopy with overtones.
Incomplete Reparameterizations and Equivalent Metrics

Reparameterizing a probabilisitic system is common advice for improving the performance of a statistical algorithm like Markov chain Monte Carlo, even though in theory such reparameterizations should leave the system, and the performance of any algorithm, invariant. In this paper I show how the reparameterizations common in practice are only incomplete reparameterizations which result in different interactions between a target probabilistic system and a given algorithm. I then consider how these changing interactions manifest in the context of Markov chain Monte Carlo algorithms defined on Riemannian manifolds. In particular I show how any incomplete reparameterization is equivalent to modifying the metric geometry directly.
Musical Instrument Playing Technique Detection Based on FCN: Using Chinese Bowed-Stringed Instrument as an Example

Unlike melody extraction and other aspects of music transcription, research on playing technique detection is still in its early stages. Compared to existing work mostly focused on playing technique detection for individual single notes, we propose a general end-to-end method based on Sound Event Detection by FCN for musical instrument playing technique detection. In our case, we choose Erhu, a well-known Chinese bowed-stringed instrument, to experiment with our method. Because of the limitation of FCN, we present an algorithm to detect on variable length audio. The effectiveness of the proposed framework is tested on a new dataset, its categorization of techniques is similar to our training dataset. The highest accuracy of our 3 experiments on the new test set is 87.31%. Furthermore, we also evaluate the performance of the proposed framework on 10 real-world studio music (produced by midi) and 7 real-world recording samples to address the ability of generalization on our model.
ELSA

The next significant step in the evolution and proliferation of artificial intelligence technology will be the integration of neural network (NN) models within embedded and mobile systems. This calls for the design of compact, energy efficient NN models in silicon. In this article, we present a scalable application-specific integrated circuit (ASIC) design of an energy-efficient Long Short-Term Memory (LSTM) accelerator, named ELSA, which is suitable for energy-constrained devices. It includes several architectural innovations to achieve small area and high energy efficiency. To reduce the area and power consumption of the overall design, the compute-intensive units of ELSA employ approximate multiplications and still achieve high performance and accuracy. The performance is further improved through efficient synchronization of the elastic pipeline stages to maximize the utilization. The article also includes a performance model of ELSA, as a function of the hidden nodes and timesteps, permitting its use for the evaluation of any LSTM application. ELSA was implemented in register transfer level (RTL) and was synthesized and placed and routed in 65nm technology. Its functionality is demonstrated for language modeling—a common application of LSTM. ELSA is compared against a baseline implementation of an LSTM accelerator with standard functional units and without any of the architectural innovations of ELSA. The article demonstrates that ELSA can achieve significant improvements in power, area, and energy-efficiency when compared to the baseline design and several ASIC implementations reported in the literature, making it suitable for use in embedded systems and real-time applications.
A lower-bound estimate of the Lyapunov dimension for the global attractor of the Lorenz system

In this short report, for the classical Lorenz attractor we demonstrate the applications of the Pyragas time-delayed feedback control technique and Leonov analytical method for the Lyapunov dimension estimation and verification of the Eden's conjecture. The problem of reliable numerical computation of the finite-time Lyapunov dimension along the trajectories over large time intervals is discussed.
Modeling of Individual HRTFs Based on Spatial Principal Component Analysis

Head-related transfer function (HRTF) plays an important role in the construction of 3D auditory display. This article presents an individual HRTF modeling method using deep neural networks based on spatial principal component analysis. The HRTFs are represented by a small set of spatial principal components combined with frequency and individual-dependent weights. By estimating the spatial principal components using deep neural networks and mapping the corresponding weights to a quantity of anthropometric parameters, we predict individual HRTFs in arbitrary spatial directions. The objective and subjective experiments evaluate the HRTFs generated by the proposed method, the principal component analysis (PCA) method, and the generic method. The results show that the HRTFs generated by the proposed method and PCA method perform better than the generic method. For most frequencies the spectral distortion of the proposed method is significantly smaller than the PCA method in the high frequencies but significantly larger in the low frequencies. The evaluation of the localization model shows the PCA method is better than the proposed method. The subjective localization experiments show that the PCA and the proposed methods have similar performances in most conditions. Both the objective and subjective experiments show that the proposed method can predict HRTFs in arbitrary spatial directions.
Integrals over Gaussians under Linear Domain Constraints

Integrals of linearly constrained multivariate Gaussian densities are a frequent problem in machine learning and statistics, arising in tasks like generalized linear models and Bayesian optimization. Yet they are notoriously hard to compute, and to further complicate matters, the numerical values of such integrals may be very small. We present an efficient black-box algorithm that exploits geometry for the estimation of integrals over a small, truncated Gaussian volume, and to simulate therefrom. Our algorithm uses the Holmes-Diaconis-Ross (HDR) method combined with an analytic version of elliptical slice sampling (ESS). Adapted to the linear setting, ESS allows for rejection-free sampling, because intersections of ellipses and domain boundaries have closed-form solutions. The key idea of HDR is to decompose the integral into easier-to-compute conditional probabilities by using a sequence of nested domains. Remarkably, it allows for direct computation of the logarithm of the integral value and thus enables the computation of extremely small probability masses. We demonstrate the effectiveness of our tailored combination of HDR and ESS on high-dimensional integrals and on entropy search for Bayesian optimization.
Spectral Characterization of Functional MRI Data on Voxel-Resolution Cortical Graphs

The human cortical layer exhibits a convoluted morphology that is unique to each individual. Conventional volumetric fMRI processing schemes take for granted the rich information provided by the underlying anatomy. We present a method to study fMRI data on subject-specific cerebral hemisphere cortex (CHC) graphs, which encode the cortical morphology at the resolution of voxels in 3-D. Using graph signal processing principles, we study spectral energy metrics associated to fMRI data, on 100 subjects from the Human Connectome Project database, across seven tasks. Experimental results signify the strength of CHC graphs' Laplacian eigenvector bases in capturing subtle spatial patterns specific to different functional loads as well as to sets of experimental conditions within each task.
An Erd\H{o}s-Ko-Rado Theorem for unions of length 2 paths.

A family of sets is intersecting if any two sets in the family intersect. Given a graph $G$ and an integer $r\geq 1$, let $\mathcal{I}^{(r)}(G)$ denote the family of independent sets of size $r$ of $G$. For a vertex $v$ of $G$, the family of independent sets of size $r$ that contain $v$ is called an $r$-star. Then $G$ is said to be $r$-EKR if no intersecting subfamily of $ \mathcal{I}^{(r)}(G)$ is bigger than the largest $r$-star. Let $n$ be a positive integer, and let $G$ consist of the disjoint union of $n$ paths each of length 2. We prove that if $1 \leq r \leq n/2$, then $G$ is $r$-EKR. This affirms a longstanding conjecture of Holroyd and Talbot for this class of graphs and can be seen as an analogue of a well-known theorem on signed sets, proved using different methods, by Deza and Frankl and by Bollob\'as and Leader. Our main approach is a novel probabilistic extension of Katona's elegant cycle method, which might be of independent interest.
Tracking-Assisted Segmentation of Biological Cells

U-Net and its variants have been demonstrated to work sufficiently well in biological cell tracking and segmentation. However, these methods still suffer in the presence of complex processes such as collision of cells, mitosis and apoptosis. In this paper, we augment U-Net with Siamese matching-based tracking and propose to track individual nuclei over time. By modelling the behavioural pattern of the cells, we achieve improved segmentation and tracking performances through a re-segmentation procedure. Our preliminary investigations on the Fluo-N2DH-SIM+ and Fluo-N2DH-GOWT1 datasets demonstrate that absolute improvements of up to 3.8 % and 3.4% can be obtained in segmentation and tracking accuracy, respectively.
Detector R&D for ANNIE and Future Neutrino Experiments

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is designed to serve as a test bed for new detector technologies in future water and liquid scintillator based neutrino experiments. Located on the Booster Neutrino Beam at Fermilab, ANNIE will be the first gadolinium-loaded water Cherenkov detector on a neutrino beam and will provide high statistics measurements of neutron yields from neutrino interactions in water. It is also the first particle physics application of the new photosensor technology: Large Area Picosecond Photodetectors (LAPPDs). With single photon time resolutions of roughly 50 psec and mm-level imaging capabilities, LAPPDs bring considerable new capabilities for neutrino reconstruction in Cherenkov and scintillator detectors. Leveraging this technology to make detailed neutrino measurements, ANNIE will serve as a first demonstration of their impact on physics. In addition to LAPPDs, the ANNIE R&D program will likely explore other new technologies such as the addition of water-based liquid scintillator. The ANNIE Phase II detector is currently under construction and will start to take data in the summer of 2019. In this talk, I will present on the ANNIE detector R&D program and its relevance to current and future neutrino experiments.
Identification of Interaction Clusters Using a Semi-supervised Hierarchical Clustering Method

Motivation: Identifying interaction clusters of large gene regulatory networks (GRNs) is critical for its further investigation, while this task is very challenging, attributed to data noise in experiment data, large scale of GRNs, and inconsistency between gene expression profiles and function modules, etc. It is promising to semi-supervise this process by prior information, but shortage of prior information sometimes make it very challenging. Meanwhile, it is also annoying, and sometimes impossible to discovery gold standard for evaluation of clustering results. \\ Results: With assistance of an online enrichment tool, this research proposes a semi-supervised hierarchical clustering method via deconvolved correlation matrix~(SHC-DC) to discover interaction clusters of large-scale GRNs. Three benchmark networks including a \emph{Ecoli} network and two \emph{Yeast} networks are employed to test semi-supervision scheme of the proposed method. Then, SHC-DC is utilized to cluster genes in sleep study. Results demonstrates it can find interaction modules that are generally enriched in various signal pathways. Besides the significant influence on blood level of interleukins, impact of sleep on important pathways mediated by them is also validated by the discovered interaction modules.
Personalizing Graph Neural Networks with Attention Mechanism for Session-based Recommendation

The problem of personalized session-based recommendation aims to predict users' next click based on their sequential behaviors. Existing session-based recommendation methods only consider all sessions of user as a single sequence, ignoring the relationship of among sessions. Other than that, most of them neglect complex transitions of items and the collaborative relationship between users and items. To this end, we propose a novel method, named Personalizing Graph Neural Networks with Attention Mechanism, A-PGNN for brevity. A-PGNN mainly consists of two components: One is Personalizing Graph Neural Network (PGNN), which is used to capture complex transitions in user session sequence. Compared with the traditional Graph Neural Network (GNN) model, it also considers the role of users in the sequence. The other is Dot-Product Attention mechanism, which draws on the attention mechanism in machine translation to explicitly model the effect of historical sessions on the current session. These two parts make it possible to learn the multi-level transition relationships between items and sessions in user-specific fashion. Extensive experiments conducted on two real-world data sets show that A-PGNN significantly outperforms the state-of-the-art personalizing session-based recommendation methods consistently.
The Deepfake Detection Challenge (DFDC) Preview Dataset

In this paper, we introduce a preview of the Deepfakes Detection Challenge (DFDC) dataset consisting of 5K videos featuring two facial modification algorithms. A data collection campaign has been carried out where participating actors have entered into an agreement to the use and manipulation of their likenesses in our creation of the dataset. Diversity in several axes (gender, skin-tone, age, etc.) has been considered and actors recorded videos with arbitrary backgrounds thus bringing visual variability. Finally, a set of specific metrics to evaluate the performance have been defined and two existing models for detecting deepfakes have been tested to provide a reference performance baseline. The DFDC dataset preview can be downloaded at: deepfakedetectionchallenge.ai
Entropic Dynamic Time Warping Kernels for Co-Evolving Financial Time Series Analysis

Network representations are powerful tools to modeling the dynamic time-varying financial complex systems consisting of multiple co-evolving financial time series, e.g., stock prices. In this work, we develop a novel framework to compute the kernel-based similarity measure between dynamic time-varying financial networks. Specifically, we explore whether the proposed kernel can be employed to understand the structural evolution of the financial networks with time associated with standard kernel machines. For a set of time-varying financial networks with each vertex representing the individual time series of a different stock and each edge between a pair of time series representing the absolute value of their Pearson correlation, our start point is to compute the commute time (CT) matrix associated with the weighted adjacency matrix of the network structures, where each element of the matrix can be seen as the enhanced correlation value between pairwise stocks. For each network, we show how the CT matrix allows us to identify a reliable set of dominant correlated time series as well as an associated dominant probability distribution of the stock belonging to this set. Furthermore, we represent each original network as a discrete dominant Shannon entropy time series computed from the dominant probability distribution. With the dominant entropy time series for each pair of financial networks to hand, we develop an entropic dynamic time warping kernels through the classical dynamic time warping framework, for analyzing the financial time-varying networks. We show that the proposed kernel bridges the gap between graph kernels and the classical dynamic time warping framework for multiple financial time series analysis. Experiments on time-varying networks extracted through New York Stock Exchange (NYSE) database demonstrate that the effectiveness of the proposed method.
Predictors of psychosocial adjustment in systemic sclerosis

Objective. To determine predictors of psychosocial adjustment in patients with systemic sclerosis (SSc). Methods. We surveyed 94 patients with SSc. Age, sex, education level, marital status, work status, income, support group attendance, length of time since diagnosis, functional status, social support, illness-related uncertainty, and hardiness were examined as potential predictors of psychosocial adjustment. The reliability and validity of the instruments used to measure these variables have been established. Results. Only education level, functional ability, illness-related uncertainty, hardiness, and social support were predictive of psychosocial adjustment. Education level and functional ability explained 14% of the variance in psychosocial adjustment, while illness-related uncertainty, hardiness, and social support increased the explained variance to 38%. Conclusion. Although patients with relatively poorer psychosocial adjustment to illness have lower formal education levels and more functional disability, the majority of the explained variance in psychosocial adjustment is ascribable to illness-related uncertainty, low level of hardiness, and less satisfaction with social support.
Unsupervised decoding of spinal motor neuron spike trains for estimating hand kinematics following targeted muscle reinnervation

The performance of upper-limb prostheses is currently limited by the relatively poor functionality of unintuitive control schemes. This paper proposes to extract, from multichannel electromyographic signals (EMG), motor neuron spike trains and project them into lower dimensional continuous signals, which are used as multichannel proportional inputs to control the prosthetic's actuators. These control signals are an estimation of the common synaptic input that the motor neurons receive. We use the simplest of metric learning approaches known as principal component analysis (PCA), as a linear unsupervised metric projection to extract the spectral information of high dimensional data into the subspace of the prosthetic hand degrees of freedom. We also investigate the importance of a rotation in the projection space that best aligns the PCA subspace with the space of degrees of freedom of the prosthetic hand, to attempt to approximate the sparseness properties of the motor axes (which are orthogonal), while no sparseness is enforced by PCA. Proof of concept for the feedforward path (open loop) is given by successful estimation of concurrent movements with up to three degrees of freedom. We also analyze and quantify the performance of the proposed decoding algorithm under implementation constraints and hardware limitations and propose a space-time subsampling strategy, to maximize projection fidelity, in each extracted source over time. The results confirm that the proposed decoding approach can directly project the motor neuron spike trains into kinematics for simultaneous and proportional prosthesis control that can be extended to multiple degrees of freedom. We show that the method is robust to reducing the training data in space (number of sources) and time, which makes it potentially suitable for clinical applications.
Machine Learning Systems for Highly-Distributed and Rapidly-Growing Data

e usability and practicality of any machine learning (ML) applications are largely influenced by two critical but hard-to-attain factors: low latency and low cost. Unfortunately, achieving low latency and low cost is very challenging when ML depends on real-world data that are highly distributed and rapidly growing (e.g., data collected by mobile phones and video cameras all over the world). Such real-worlddata pose many challenges in communication and computation. For example, when training data are distributed across data centers that span multiple continents, communication among data centers can easily overwhelm the limited wide-area network bandwidth, leading to prohibitively high latency and high cost. In this dissertation, we demonstrate that the latency and cost of ML on highly distributed and rapidly-growing data can be improved by one to two orders of magnitudeby designing ML systems that exploit the characteristics of ML algorithms, ML model structures, and ML training/serving data. We support this thesis statement with three contributions. First, we design a system that provides both low-latency and low-cost ML serving (inferencing) over large-scale and continuously-growing datasets, such as videos. Second, we build a system that makes ML training over geodistributed datasets as fast as training within a single data center. Third, we present a first detailed study and a system-level solution on a fundamental and largely overlooked problem: ML training over non-IID (i.e., not independent and identically distributed) data partitions (e.g., facial images collected by cameras varies according tothe demographics of each camera’s location).
Reverse Experience Replay

This paper describes an improvement in Deep Q-learning called Reverse Experience Replay (also RER) that solves the problem of sparse rewards and helps to deal with reward maximizing tasks by sampling transitions successively in reverse order. On tasks with enough experience for training and enough Experience Replay memory capacity, Deep Q-learning Network with Reverse Experience Replay shows competitive results against both Double DQN, with a standard Experience Replay, and vanilla DQN. Also, RER achieves significantly increased results in tasks with a lack of experience and Replay memory capacity.