Miracle-dz/UniFAR-FTUs huggingface dataset:
Expression control of singing voice synthesis: modeling pitch and dynamics with unit selection and statistical approaches

Las tecnologias de sintesis de sonido se han aplicado en diferentes campos, como el habla, los instrumentos musicales, y la voz cantada. Estas tecnologias deben tener una representacion realista del sonido original y tambien tienen que reproducir sus caracteristicas expresivas. Asi podemos hablar de sintesis expresiva del habla, de actuaciones expresivas de instrumentos, y de sintesis expresiva de voz cantada. De hecho, la voz cantada tiene puntos en comun con el habla (misma fuente sonora) y con los instrumentos (aspectos musicales como la melodia y otros recursos expresivos). Modelar la expresividad de la voz cantada es una tarea dificil. El instrumento nos es tan familiar que podemos detectar facilmente si los resultados obtenidos artificialmente son similares a los de un cantante. La dificultad de la tarea tambien esta vinculada a la variedad de caracteristicas a controlar relacionadas con la melodia, la dinamica, el ritmo y el timbre. El objetivo de esta tesis es generar los controles para sintetizadores de voz cantada y asi conseguir una expresividad parecida a la humana. Concretamente, nos fijaremos como objetivo obtener la frecuencia fundamental y la dinamica proponiendo cuatro sistemas. Respecto al sistema de seleccion de unidades hemos definido las funciones de costos para la seleccion de unidades asi como su transformacion i concatenacion. Con los sistemas estadisticos hemos modelado tanto secuencias de notas como secuencias de transiciones y sostenimientos. Tambien hemos presentado un ultimo sistema hibrido de los anteriores. Hemos entrenado todos estos sistemas con dos bases de datos expresivas que hemos disenado, registrado y etiquetado. El diseno lo hemos basado en secuencias de tres notas o silencios. Nuestra evaluacion perceptual compara los sistemas propuestos con un sistema por defecto y un sistema controlado por una voz humana. La evaluacion concluye que el sistema hibrido consigue la expresividad mas cercana a la de una voz humana. La evaluacion objetiva se centra en le eficiencia de los sistemas. Esta tesis contribuye al tema planteado en los siguientes aspectos: 1) recoge diversas definiciones que se han elaborado sobre la expresividad, 2) repasa trabajos anteriores en el control de la expresividad de la sintesis de la voz cantada, 3) presenta una recopilacion online de sonidos que muestran los resultados de estos trabajos, 4) propone una metodologia para la creacion de bases de datos expresivos, 5) implementa un sistema basado en la seleccion de unidades para el control de la expresividad, 6) propone dos sistemas estadisticos, 7) presenta un sistema hibrido, 8) compara los sistemas propuestos con sistemas de referencia, 9) propone otro caso de uso donde aplicar el control de expresividad, y finalmente, 10) proporciona una serie de propuestas para mejorar la evaluacion de sistemas de sintesis de voz cantada.
The influence of route guidance advice on route choice in urban networks

The paper begins by reviewing what is known about route choice processes and notes the mismatch between this knowledge and the route choice assumptions embedded in the most widely used assignment models. Empirical evidence on the influence of route guidance advice on route choice is reviewed and, despite its limited nature, is seen to suggest that users are reluctant to follow advice unless they find it convincing and that, the more familiar they are with the network, the less likely they are to accept advice. Typically only a small minority of journeys are made in total compliance with advice.Results from an interactive route choice simulator (IGOR) are summarised and are seen to reveal that compliance depends on the extent to which the advice is corroborated by other factors, on the drivers' familiarity with the network and on the quality of advice previously received. It is noted that the IGOR results are in a form which would enable response models to be calibrated.Recent approaches to the modelling of route choice in the context of guidance are discussed. Some are seen to make simplifying assumptions which must limit the relevance of their results; most make no allowance for the fact that drivers are unlikely to comply with all advice and several are not able to represent the benefits which guidance might bring in the context of sporadic congestion or incidents.As an alternative, a two phase model comprising a medium term strategic equilibrium and a day-specific simulation with explicit representation of driver response is proposed.
A Lyapunov Function for Robust Stability of Moving Horizon Estimation

We provide a novel robust stability analysis for moving horizon estimation (MHE) using a Lyapunov function. In addition, we introduce linear matrix inequalities (LMIs) to verify the necessary incremental input/output-to-state stability (<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\delta }$</tex-math></inline-formula>-IOSS) detectability condition. We consider an MHE formulation with time-discounted quadratic objective for nonlinear systems admitting an exponential <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\delta }$</tex-math></inline-formula>-IOSS Lyapunov function. We show that with a suitable parameterization of the MHE objective, the <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\delta }$</tex-math></inline-formula>-IOSS Lyapunov function serves as an <inline-formula><tex-math notation="LaTeX">$\boldsymbol{M}$</tex-math></inline-formula>-step Lyapunov function for MHE. Provided that the estimation horizon is chosen large enough, this directly implies exponential stability of MHE. The stability analysis is also applicable to full information estimation, where the restriction to <italic>exponential</italic> <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\delta }$</tex-math></inline-formula>-IOSS can be relaxed. Moreover, we provide simple LMI conditions to systematically derive <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\delta }$</tex-math></inline-formula>-IOSS Lyapunov functions, which allows us to easily verify <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\delta }$</tex-math></inline-formula>-IOSS for a large class of nonlinear detectable systems. This is useful in the context of MHE in general, since most of the existing nonlinear (robust) stability results for MHE depend on the system being <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\delta }$</tex-math></inline-formula>-IOSS (detectable). In combination, we thus provide a framework for designing MHE schemes with guaranteed robust exponential stability. The applicability of the proposed methods is demonstrated with a nonlinear chemical reactor process and a 12-state quadrotor model.
The Combined Effects of Two-body Relaxation Processes and the Eccentric Kozai–Lidov Mechanism on the Extreme-mass-ratio Inspirals Rate

Gravitational wave (GW) emissions from extreme-mass-ratio inspirals (EMRIs) are promising sources for low-frequency GW detectors. They result from a compact object, such as a stellar-mass black hole (BH), captured by a supermassive BH (SMBH). Several physical processes have been proposed to form EMRIs. In particular, weak two-body interactions over a long timescale (i.e., relaxation processes) have been proposed as a likely mechanism to drive the BH orbit to high eccentricity. Consequently, it is captured by the SMBH and becomes an EMRI. Here we demonstrate that EMRIs are naturally formed in SMBH binaries. Gravitational perturbations from an SMBH companion, known as the eccentric Kozai–Lidov (EKL) mechanism, combined with relaxation processes, yield a significantly more enhanced rate than any of these processes operating alone. Because EKL is sensitive to the orbital configuration, two-body relaxation can alter the orbital parameters, rendering the system in a more EKL-favorable regime. As SMBH binaries are expected to be prevalent in the universe, this process predicts a substantially high EMRI rate.
RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment

—Due to climate change, we can observe a recent surge of natural disasters all around the world. These disasters are causing disastrous impact on both nature and human lives. Economic losses are getting greater due to the hurricanes. Quick and prompt response of the rescue teams are crucial in saving human lives and reducing economic cost. Deep learning based computer vision techniques can help in scene understanding, and help rescue teams with precise damage assessment. Semantic segmentation, an active research area in computer vision, can put labels to each pixel of an image, and therefore can be a valuable arsenal in the effort of reducing the impacts of hurricanes. Unfortunately, available datasets for natural disaster damage assessment lack detailed annotation of the affected areas, and therefore do not support the deep learning models in total damage assessment. To this end, we introduce the RescueNet, a high resolution post disaster dataset, for semantic segmentation to assess damages after natural disasters. The RescueNet consists of post disaster images collected after Hurricane Michael. The data is collected using Unmanned Aerial Vehicles (UAVs) from several areas impacted by the hurricane. The uniqueness of the RescueNet comes from the fact that this dataset provides high resolution post-disaster images and comprehensive annotation of each image. While most of the existing dataset offer annotation of only part of the scene, like building, road, or river, RescueNet provides pixel level annotation of all the classes including building, road, pool, tree, debris, and so on. We further analyze the usefulness of the dataset by implementing state-of-the-art segmentation models on the RescueNet. The experiments demonstrate that our dataset can be valuable in further improvement of the existing methodologies for natural disaster damage assessment.
Towards optimal lower bounds for k-median and k-means coresets

The (k,z)-clustering problem consists of finding a set of k points called centers, such that the sum of distances raised to the power of z of every data point to its closest center is minimized. Among the most commonly encountered special cases are k-median problem (z=1) and k-means problem (z=2). The k-median and k-means problems are at the heart of modern data analysis and massive data applications have given raise to the notion of coreset: a small (weighted) subset of the input point set preserving the cost of any solution to the problem up to a multiplicative (1± ε) factor, hence reducing from large to small scale the input to the problem. While there has been an intensive effort to understand what is the best coreset size possible for both problems in various metric spaces, there is still a significant gap between the state-of-the-art upper and lower bounds. In this paper, we make progress on both upper and lower bounds, obtaining tight bounds for several cases, namely: • In finite n point general metrics, any coreset must consist of Ω(k logn / ε2) points. This improves on the Ω(k logn /ε) lower bound of Braverman, Jiang, Krauthgamer, and Wu [ICML’19] and matches the upper bounds proposed for k-median by Feldman and Langberg [STOC’11] and k-means by Cohen-Addad, Saulpic and Schwiegelshohn [STOC’21] up to polylog factors. • For doubling metrics with doubling constant D, any coreset must consist of Ω(k D/ε2) points. This matches the k-median and k-means upper bounds by Cohen-Addad, Saulpic, and Schwiegelshohn [STOC’21] up to polylog factors. • In d-dimensional Euclidean space, any coreset for (k,z) clustering requires Ω(k/ε2) points. This improves on the Ω(k/√ε) lower bound of Baker, Braverman, Huang, Jiang, Krauthgamer, and Wu [ICML’20] for k-median and complements the Ω(kmin(d,2z/20)) lower bound of Huang and Vishnoi [STOC’20]. We complement our lower bound for d-dimensional Euclidean space with the construction of a coreset of size Õ(k/ε2· min(ε−z,k)). This improves over the Õ(k2 ε−4) upper bound for general power of z proposed by Braverman Jiang, Krauthgamer, and Wu [SODA’21] and over the Õ(k/ε4) upper bound for k-median by Huang and Vishnoi [STOC’20]. In fact, ours is the first construction breaking through the ε−2· min(d,ε−2) barrier inherent in all previous coreset constructions. To do this, we employ a novel chaining based analysis that may be of independent interest. Together our upper and lower bounds for k-median in Euclidean spaces are tight up to a factor O(ε−1 polylog k/ε).
An exploration of the performances achievable by combining unsupervised background subtraction algorithms

Background subtraction (BGS) is a common choice for performing motion detection in video. Hundreds of BGS algorithms are released every year, but combining them to detect motion remains largely unexplored. We found that combination strategies allow to capitalize on this massive amount of available BGS algorithms, and offer significant space for performance improvement. In this paper, we explore sets of performances achievable by 6 strategies combining, pixelwise, the outputs of 26 unsupervised BGS algorithms, on the CDnet 2014 dataset, both in the ROC space and in terms of the F1 score. The chosen strategies are representative for a large panel of strategies, including both deterministic and non-deterministic ones, voting and learning. In our experiments, we compare our results with the state-of-the-art combinations IUTIS-5 and CNN-SFC, and report six conclusions, among which the existence of an important gap between the performances of the individual algorithms and the best performances achievable by combining them.
RRL: Regional Rotation Layer in Convolutional Neural Networks

Convolutional Neural Networks (CNNs) perform very well in image classification and object detection in recent years, but even the most advanced models have limited rotation invariance. Known solutions include the enhancement of training data and the increase of rotation invariance by globally merging the rotation equivariant features. These methods either increase the workload of training or increase the number of model parameters. To address this problem, this paper proposes a module that can be inserted into the existing networks, and directly incorporates the rotation invariance into the feature extraction layers of the CNNs. This module does not have learnable parameters and will not increase the complexity of the model. At the same time, only by training the upright data, it can perform well on the rotated testing set. These advantages will be suitable for fields such as biomedicine and astronomy where it is difficult to obtain upright samples or the target has no directionality. Evaluate our module with LeNet-5, ResNet-18 and tiny-yolov3, we get impressive results.
Challenges and Opportunities for Machine Learning in Fluid Mechanics

Big data and machine learning are driving comprehensive economic and social transformations and are rapidly re-shaping the toolbox and the methodologies of applied scientists. Machine learning tools are designed to learn functions from data with little to no need of prior knowledge. As continuous developments in experimental and numerical methods improve our ability to collect high-quality data, machine learning tools become increasingly viable and promising also in disciplines rooted in physical principles. These notes explore how machine learning can be integrated and combined with more classic methods in fluid dynamics. After a brief review of the machine learning landscape, we show how many problems in fluid mechanics can be framed as machine learning problems and we explore challenges and opportunities. We consider several relevant applications: aeroacoustic noise prediction, turbulence modelling, reduced-order modelling and forecasting, meshless integration of (partial) differential equations, super-resolution and flow control. While this list is by no means exhaustive, the presentation will provide enough concrete examples to offer perspectives on how machine learning might impact the way we do research and learn from data.
Indirect measurement of junction temperature for condition monitoring of power semiconductor devices during operation

In a power converter, the monitoring of junction temperature Tvj is imperative for overtemperature or overload protection. Further, comparing measured junction temperature during operation with the temperature value calculated by a suitable model allows condition monitoring, i. e. to detect wearout or a deteriorated thermal interface. Since junction temperature within a device can't be directly measured, it is determined indirectly instead. This paper presents a relatively simple circuit to be added to an IGBT driver which permits to monitor junction temperature of the chip during converter operation as has been demonstrated.
CFP-SLAM: A Real-time Visual SLAM Based on Coarse-to-Fine Probability in Dynamic Environments

The dynamic factors in the environment will lead to the decline of camera localization accuracy due to the violation of the static environment assumption of SLAM algorithm. Recently, some related works generally use the combination of semantic constraints and geometric constraints to deal with dynamic objects, but problems can still be raised, such as poor real-time performance, easy to treat people as rigid bodies, and poor performance in low dynamic scenes. In this paper, a dynamic scene-oriented visual SLAM algorithm based on object detection and coarse-to-fine static probability named CFP-SLAM is proposed. The algorithm combines semantic constraints and geometric constraints to calculate the static probability of objects, keypoints and map points, and takes them as weights to participate in camera pose estimation. Extensive evaluations show that our approach can achieve almost the best results in high dynamic and low dynamic scenarios compared to the state-of-the-art dynamic SLAM methods, and shows quite high real-time ability.
THE UNIVERSITY OF CHICAGO HOW TO EFFECTIVELY COUPLE DATACENTERS AS FLEXIBLE LOADS TO THE POWER GRID FOR CARBON REDUCTION A DISSERTATION SUBMITTED TO THE FACULTY OF THE DIVISION OF THE PHYSICAL SCIENCES IN CANDIDACY FOR THE DEGREE OF MASTER

The rapid growth of datacenter (DC) loads can be leveraged to help meet renewable portfolio standard (RPS, renewable fraction) targets in power grids. The ability to manipulate DC loads over time (temporal shifting) can facilitate the absorption of renewables and grid decarbonization. We study DC-grid coupling models, characterizing their impact on grid dynamics (e.g. power price, carbon emissions) and DC behaviors, with the goal of finding how to effectively couple dynamic DC loads to the power grid for carbon reduction. With a detailed grid model, we consider a spectrum of coupling models varied in who controls the dynamic load and what DC load information is shared with the grid (e.g. next hour or day’s load). Results show that understanding the effects of dynamic DC load management requires modeling the dynamics of both load and power grid. Dynamic DC-grid coupling can produce large improvements: (1) reduce grid dispatch cost (-3%), (2) increase grid renewable fraction (+1.58%), and (3) reduce DC power cost (-16.9%). However, it also has negative effects: (1) increase cost for both DCs and non-DC customers, (2) differentially increase prices for non-DC customers—fairness issue, and (3) create large power-level changes that may harm DC productivity. Such negative effects are more evident when uncoordinated DC-local control overshifts the load. Addressing these, we find that by utilizing hourly price information, constraining capacity variation, and sharing such 24-hour dynamic load with the power grid ahead of operating day, DC-local control can approximate the best performance of global grid-controlled optimization with lower DC capacity variation.
Detection Sensitivity for HPV between Self-Collected Vaginal Swabs and Physician-Collected Cervical Swabs

Human papillomavirus (HPV) causes benign and malignant neoplasms of the genital tract including cervical cancer (Koutsky, 1997). HPV genotypes 16 and 18 correlate with an increased risk of developing precancerous lesions and are the most common causes of cervical cancer (Khan et al., 2005). HPV DNA testing is an effective screening method for precancerous changes in the cervix, especially for specimens with normal cytology or low-grade squamous intraepithelial lesions (LSIL) (Wang et al., 2013). Papanicolaou (Pap) test is a simple technique used for screening cervical cancer since the 1940’s and has been shown to reduce the incidence of mortality from cervical cancer in countries with an active screening program (Safaeian et al., 2007). Test results help physicians detect precancerous lesions and determine the course of treatment prior to the development of malignancy. In developing countries with few or no screening
Indonesian Journal of Electrical Engineering and Computer Science

Sarifah Putri Raflesia, Taufiqurrahman Rusdy, Dinda Lestarini, Firdaus, Dinna Yunika Hardiyanti Faculty of Computer Science, Universitas Sriwijaya, Indralaya, Indonesia Indonesian Ministry of Defense, Jakarta, Indonesia Database and Big Data Laboratory, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia Electronic Data Processing and Decision Support System Laboratory, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
VIDEO BASED FACE MASK DETECTION AND FACE RECOGNITION USING CNN, YOLO AND GOOGLE FACENET

: Globally Covid-19 has affected a large number of people. Covid-19 caused disruptions to many aspects of normal life. This also caused an increase in the death rate and Tension and Fear among the people. To avoid and overcome all these challenges WHO asked people to follow safety measures to avoid the spread of the virus. Wearing a mask is one of the safety measures among this. Mainly sometimes people gather at public places like offices, banks, airports, bus stops, etc. Considering this condition, we have designed a system that helps in mask detection in the various surveillance system at such places, also it helps in the face recognition of those people who have not worn the mask. Previous Sytems were either only doing face recognition or face mask detection we came forward with this new approach by combining these two algorithms. All This is Possible by Using Artificial Intelligence which helps in Image Processing and Machine learning. Algorithms Like CNN, YOLO, and Google FaceNet were used for the same. Using this System, we got 97% Accuracy in Detecting a Face mask by using CNN. This System helps in classifying with mask and without mask person in the video and also help to identify the person who has not worn a mask which helps to send a notification to that person who violates the rule.