Miracle-dz/UniFAR-FTUs huggingface dataset:
Controllable Sequence-To-Sequence Neural TTS with LPCNET Backend for Real-time Speech Synthesis on CPU

State-of-the-art sequence-to-sequence acoustic networks, that convert a phonetic sequence to a sequence of spectral features with no explicit prosody prediction, generate speech with close to natural quality, when cascaded with neural vocoders, such as Wavenet. However, the combined system is typically too heavy for real-time speech synthesis on a CPU. In this work we present a sequence-to-sequence acoustic network combined with lightweight LPCNet neural vocoder, designed for real-time speech synthesis on a CPU. In addition, the system allows sentence-level pace and expressivity control at inference time. We demonstrate that the proposed system can synthesize high quality 22 kHz speech in real-time on a general-purpose CPU. In terms of MOS score degradation relative to PCM, the system attained as low as 6.1-6.5% for quality and 6.3- 7.0% for expressiveness, reaching equivalent or better quality when compared to a similar system with a Wavenet vocoder backend.
Sinai billiard maps with Ruelle resonances

We construct families of two-dimensional Sinai billiards whose transfer operators have Ruelle resonances arbitrarily close to 1. Our method involves taking a large enough cover of an initial billiard table, and relating the transfer operator of the covering table to twisted transfer operators of the initial table. We also study the distribution of these resonances which are close to 1.
Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Critic

Multi-agent adversarial inverse reinforcement learning (MA-AIRL) is a recent approach that applies single-agent AIRL to multi-agent problems where we seek to recover both policies for our agents and reward functions that promote expert-like behavior. While MA-AIRL has promising results on cooperative and competitive tasks, it is sample-inefficient and has only been validated empirically for small numbers of agents -- its ability to scale to many agents remains an open question. We propose a multi-agent inverse RL algorithm that is more sample-efficient and scalable than previous works. Specifically, we employ multi-agent actor-attention-critic (MAAC) -- an off-policy multi-agent RL (MARL) method -- for the RL inner loop of the inverse RL procedure. In doing so, we are able to increase sample efficiency compared to state-of-the-art baselines, across both small- and large-scale tasks. Moreover, the RL agents trained on the rewards recovered by our method better match the experts than those trained on the rewards derived from the baselines. Finally, our method requires far fewer agent-environment interactions, particularly as the number of agents increases.
LoRa Beyond ALOHA: An Investigation of Alternative Random Access Protocols

In this article, we present a stochastic geometry-based model to investigate alternative medium access choices for LoRaWAN—a widely adopted low-power wide-area network (LPWAN) technology for the Internet-of-Things. LoRaWAN adoption is driven by its simplified network architecture, air interface, and medium access. The physical layer, known as Long Range (LoRa), provides quasi-orthogonal virtual channels through spreading factors (SFs) and time-power capture gains. However, the adopted pure ALOHA access mechanism suffers, in terms of scalability, under the same-channel same-SF transmissions from a large number of devices. In this article, our objective is to explore access mechanisms beyond-ALOHA for LoRaWAN. Using recent results on time- and power-capture effects of LoRa, we develop a unified model for the comparative study of other choices, i.e., slotted ALOHA and carrier-sense multiple access (CSMA). The model includes the necessary design parameters of these access mechanisms, such as guard time and synchronization accuracy for slotted ALOHA, carrier sensing threshold for CSMA. It also accounts for the spatial interaction of devices in annular shaped regions, characteristic of LoRa, for CSMA. The performance metrics derived from the model in terms of coverage probability, channel throughput, and energy efficiency are validated using Monte-Carlo simulations. Our analysis shows that slotted ALOHA indeed has higher reliability than pure ALOHA but at the cost of lower energy efficiency for low device densities. Whereas, CSMA outperforms slotted ALOHA at smaller SFs in terms of reliability and energy efficiency, with its performance degrading to pure ALOHA at higher SFs.
LATIS: The Lyα Tomography IMACS Survey

We introduce LATIS, the Lyα Tomography IMACS Survey, a spectroscopic survey at Magellan designed to map the z = 2.2–2.8 intergalactic medium (IGM) in three dimensions by observing the Lyα forest in the spectra of galaxies and QSOs. Within an area of 1.7 deg2, we will observe approximately half of ≳L* galaxies at z = 2.2–3.2 for typically 12 hr, providing a dense network of sightlines piercing the IGM with an average transverse separation of 2.5 h−1 comoving Mpc (1 physical Mpc). At these scales, the opacity of the IGM is expected to be closely related to the dark matter density, and LATIS will therefore map the density field in the z ∼ 2.5 universe at ∼Mpc resolution over the largest volume to date. Ultimately, LATIS will produce approximately 3800 spectra of z = 2.2–3.2 galaxies that probe the IGM within a volume of 4 × 106h−3 Mpc3, large enough to contain a representative sample of structures from protoclusters to large voids. Observations are already complete over one-third of the survey area. In this paper, we describe the survey design and execution. We present the largest IGM tomographic maps at comparable resolution yet made. We show that the recovered matter overdensities are broadly consistent with cosmological expectations based on realistic mock surveys, that they correspond to galaxy overdensities, and that we can recover structures identified using other tracers. LATIS is conducted in Canada–France–Hawaii Telescope Legacy Survey fields, including COSMOS. Coupling the LATIS tomographic maps with the rich data sets collected in these fields will enable novel studies of environment-dependent galaxy evolution and the galaxy–IGM connection at cosmic noon.
Cross-Layer Feature Pyramid Network for Salient Object Detection

Feature pyramid network (FPN) based models, which fuse the semantics and salient details in a progressive manner, have been proven highly effective in salient object detection. However, it is observed that these models often generate saliency maps with incomplete object structures or unclear object boundaries, due to the indirect information propagation among distant layers that makes such fusion structure less effective. In this work, we propose a novel Cross-layer Feature Pyramid Network (CFPN), in which direct cross-layer communication is enabled to improve the progressive fusion in salient object detection. Specifically, the proposed network first aggregates multi-scale features from different layers into feature maps that have access to both the high- and low- level information. Then, it distributes the aggregated features to all the involved layers to gain access to richer context. In this way, the distributed features per layer own both semantics and salient details from all other layers simultaneously, and suffer reduced loss of important information during the progressive feature fusion. At last, CFPN fuses the distributed features of each layer stage-by-stage. This way, the high-level features that contain context useful for locating complete objects are preserved until the final output layer, and the low-level features that contain spatial structure details are embedded into each layer to preserve spatial structural details. Extensive experimental results over six widely used salient object detection benchmarks and with three popular backbones clearly demonstrate that CFPN can accurately locate fairly complete salient regions and effectively segment the object boundaries.
Random horizon principal-agent problem

We consider a general formulation of the random horizon Principal-Agent problem with a continuous payment and a lump-sum payment at termination. In the European version of the problem, the random horizon is chosen solely by the principal with no other possible action from the agent than exerting effort on the dynamics of the output process. We also consider the American version of the contract, which covers the seminal Sannikov's model, where the agent can also quit by optimally choosing the termination time of the contract. Our main result reduces such non-zero-sum stochastic differential games to appropriate stochastic control problems which may be solved by standard methods of stochastic control theory. This reduction is obtained by following Sannikov's approach, further developed by Cvitanic, Possamai, and Touzi. We first introduce an appropriate class of contracts for which the agent's optimal effort is immediately characterized by the standard verification argument in stochastic control theory. We then show that this class of contracts is dense in an appropriate sense so that the optimization over this restricted family of contracts represents no loss of generality. The result is obtained by using the recent well-posedness result of random horizon second-order backward SDE.
The Power of Recourse: Better Algorithms for Facility Location in Online and Dynamic Models

In this paper we study the facility location problem in the online with recourse and dynamic algorithm models. In the online with recourse model, clients arrive one by one and our algorithm needs to maintain good solutions at all time steps with only a few changes to the previously made decisions (called recourse). We show that the classic local search technique can lead to a $(1+\sqrt{2}+\epsilon)$-competitive online algorithm for facility location with only $O\left(\frac{\log n}{\epsilon}\log\frac1\epsilon\right)$ amortized facility and client recourse. We then turn to the dynamic algorithm model for the problem, where the main goal is to design fast algorithms that maintain good solutions at all time steps. We show that the result for online facility location, combined with the randomized local search technique of Charikar and Guha [10], leads to an $O(1+\sqrt{2}+\epsilon)$ approximation dynamic algorithm with amortized update time of $\tilde O(n)$ in the incremental setting. Notice that the running time is almost optimal, since in general metric space it takes $\Omega(n)$ time to specify a new client's position. The approximation factor of our algorithm also matches the best offline analysis of the classic local search algorithm. Finally, we study the fully dynamic model for facility location, where clients can both arrive and depart. Our main result is an $O(1)$-approximation algorithm in this model with $O(|F|)$ preprocessing time and $O(\log^3 D)$ amortized update time for the HST metric spaces. Using the seminal results of Bartal [4] and Fakcharoenphol, Rao and Talwar [17], which show that any arbitrary $N$-point metric space can be embedded into a distribution over HSTs such that the expected distortion is at most $O(\log N)$, we obtain a $O(\log |F|)$ approximation with preprocessing time of $O(|F|^2\log |F|)$ and $O(\log^3 D)$ amortized update time.
Circle Loss: A Unified Perspective of Pair Similarity Optimization

This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$. We find a majority of loss functions, including the triplet loss and the softmax cross-entropy loss, embed $s_n$ and $s_p$ into similarity pairs and seek to reduce $(s_n-s_p)$. Such an optimization manner is inflexible, because the penalty strength on every single similarity score is restricted to be equal. Our intuition is that if a similarity score deviates far from the optimum, it should be emphasized. To this end, we simply re-weight each similarity to highlight the less-optimized similarity scores. It results in a Circle loss, which is named due to its circular decision boundary. The Circle loss has a unified formula for two elemental deep feature learning paradigms, \emph {i.e.}, learning with class-level labels and pair-wise labels. Analytically, we show that the Circle loss offers a more flexible optimization approach towards a more definite convergence target, compared with the loss functions optimizing $(s_n-s_p)$. Experimentally, we demonstrate the superiority of the Circle loss on a variety of deep feature learning tasks. On face recognition, person re-identification, as well as several fine-grained image retrieval datasets, the achieved performance is on par with the state of the art.
Provable Representation Learning for Imitation Learning via Bi-level Optimization

A common strategy in modern learning systems is to learn a representation that is useful for many tasks, a.k. a. representation learning. We study this strategy in the imitation learning setting for Markov decision processes (MDPs) where multiple experts' trajectories are available. We formulate representation learning as a bi-level optimization problem where the "outer" optimization tries to learn the joint representation and the "inner" optimization encodes the imitation learning setup and tries to learn task-specific parameters. We instantiate this framework for the imitation learning settings of behavior cloning and observation-alone. Theoretically, we show using our framework that representation learning can provide sample complexity benefits for imitation learning in both settings. We also provide proof-of-concept experiments to verify our theory.
Separate Silicate and Carbonaceous Solids Formed from Mixed Atomic and Molecular Species Diffusing in Neon Ice

The formation and growth of refractory matter on pre-existing interstellar dust grain surfaces was studied experimentally by annealing neon-ice matrices in which potential precursors of silicate grains (Mg and Fe atoms, SiO and SiO2 molecules) and of solid carbon (Cn molecules, n = 2–10) were initially isolated. Other molecules, mainly O3, CO, CO2, C3O, and H2O, were embedded at the same time in the matrices. The annealing procedure caused the cold dopants to diffuse and interact in the neon ice. Monitoring the procedure in situ with infrared spectroscopy revealed the disappearance of the silicon oxide and carbon molecules at temperatures lower than 13 K, and the rise of the Si–O stretching band of silicates. Ex situ electron microscopy confirmed the formation of silicate grains and showed that their structure was amorphous. It also showed that amorphous carbon matter was formed simultaneously next to the silicate grains, the two materials being chemically separated. The results of the experiments support the hypothesis that grains of complex silicates and of carbonaceous materials are reformed in the cold interstellar medium, as suggested by astronomical observations and evolution models of cosmic dust masses. Moreover, they show that the potential precursors of one material do not combine with those of the other at cryogenic temperatures, providing us with a clue as to the separation of silicates and carbon in interstellar grains.
Distributed Optimization Over Time-Varying Networks With Minimal Connectivity

Many existing distributed optimization algorithms are applicable to time-varying networks, whereas their convergence results are established under the standard $B$ -connectivity condition. In this letter, we establish the convergence of the Fenchel dual gradient methods, proposed in our prior work, under a less restrictive and indeed minimal connectivity condition on undirected networks, which, referred to as joint connectivity, requires the infinitely occurring agent interactions to form a connected graph. Compared to the existing distributed optimization algorithms that are guaranteed to converge under joint connectivity, the Fenchel dual gradient methods are able to handle nonlinear local cost functions and nonidentical local constraints. We also demonstrate the effectiveness of the Fenchel dual gradient methods over time-varying networks satisfying joint connectivity via simulations.
MATLAB/SIMULINK Model For HVDC Fault Calculations

Power transmission has faced many challenges as the days goes by. Power demand has increased and its becoming increasingly difficult to acquire right way for new lines, due to growth, population, urbanizations and environmental issues. This research aims to provide methods for the rationale selections of HVDC system, essential HVDC equations and their control. A MATLAB/SIMULINK model is developed for a HVDC link using IGBT/DIODE converters between a 500KV, 50Hz system to a 330KV, 60Hz system over the distance of 450km. Analysis by using the model on this system identifies any abnormal behavior on the AC and DC sides and calculates the time elapsed for recovery to its reliable state conditions.
Cultural Differences Between Chinese and Western User Instructions: A Content Analysis of User Manuals for Household Appliances

Research problem: Cultural differences may be increasingly important in technical communication. Research is needed to investigate differences in document design practices and user preferences. This study examines cultural differences between Chinese and Western manuals for household appliances. Literature review: Earlier studies identified a wide range of possible differences between Chinese and Western documents, but the findings are not consistent and do not provide more generic perspectives on cultural differences. Possible reasons are the diversity of the documents used, the rather informal research designs, and relatively small sample sizes. Research question: To what extent and how do Chinese and Western manuals for household appliances differ from each other in terms of content, structure, and use of visuals? Methodology: To overcome these shortcomings, a quantitative content analysis was conducted, comparing 50 Chinese manuals and 50 Western manuals for household appliances. The coding scheme was based on earlier research findings and focused on content, structure, and the use of visuals. Results and conclusions: The results show that the content of Chinese manuals is less strictly confined to the function of user support than that of Western manuals. Compared to Western manuals, the structure of Chinese manuals appears to be fuzzier and less rigid. Regarding visuals, Chinese manuals contain more non-instrumental, entertaining illustrations than Western manuals. Underlying these differences is a more general distinction between highly instrumental Western manuals and more flexible Chinese manuals. These differences seem to point to two cultural dimensions: holistic versus analytic thinking and analog versus digital cultures.
An Interactive Online Audience Extension System

In online advertising market, advertisers aim to target the right audience through media channels such as display, mobile, video, or social with limited budget. Usually the advertiser can collect high valuable users from their existing customs, whose size however is usually small. Hence, it is very important for advertisers to reach more audiences who can potentially be turned into future customers. How to induce the preferable attributes from the seed audience and hence identify more similar users from billions of cookie-able users within the runtime of a few minutes is a challenge to data science techniques. This is an important problem and many researchers have proposed several solutions [1] –[6] In this paper, we propose an efficient approach which can return a set of Boolean rules that defines the extended audience by analyzing the seed audience. As an additional feature, the proposed system also provides an option for advertisers to participate in the decision making process which could potentially improve the user reachability as well as the campaign performance. The experiment results show the superior performance of the proposed system compared with other methods in terms of efficiency, user reachability, action rate, click through rate, etc.
Impact of Reconstruction Algorithms on Diffuse Correlation Tomography Blood Flow Imaging

Near-infrared diffuse correlation tomography (DCT) is an emerging technology for non-invasive imaging of the tissue blood flow. The flow imaging quality relies on the image reconstruction algorithm, which, however, is little studied thus far. In this study, we conducted the first investigation of reconstruction algorithm impact on DCT blood flow imaging. Two reconstruction algorithms, i.e., the finite element method (FEM) representing the imaging framework of partial differential equation, and the Nth-order linear (NL) approach, representing the imaging framework of integral equation that was recently proposed by us to incorporate the tissue morphological information, were compared. Both computer simulations and phantom experiment outcomes show that the NL approach performs much better in image accuracy and homogeneity over anomaly or background, when compared with the FEM at the same source-detector configuration and spatial resolution. This study demonstrates that the DCT blood flow imaging is substantially influenced by the reconstruction algorithm, thus it has great potential in future algorithm design and optimization.
The Influence of Using a Static Diastolic Geometry in ECG Imaging

One of the common choices when performing electrocardiographic imaging (ECGI) is that the cardiac geometry is in a static, diastolic state. To test the influence of this approximation, we compared epicardial potential maps and isochrones during systolic and diastolic geometries in four patients. Zero-th order Tikhonov regularization was used to reconstruct ventricular epicardial potentials. A spatiotemporal estimation method was then used to determine the activation and recovery times from the reconstructed epicardial electrograms. Activation times (AT), recovery times (RT) and electrogram correlation coefficients (CC) were compared for both geometries. Furthermore, CC and differences in AT/ RT were correlated against the linear movement and a substitute for rotational movement. Poor correlation was found between linear/rotational movement and reconstruction differences. Overall, agreement between epicardial potential maps and isochrones of both geometries was high when assessed quantitatively, but regional differences might occur for qualitative interpretation. These differences mostly occurred in areas of flat T-waves. This novel, more accurate quantification of the influence of assuming a diastolic geometry in ECGI may further help in interpreting ECGI measurements.
Wind Forecasting:Hybrid Statistical and Deep Neural Network Approaches

High penetration of renewable energies in the electrical grids require forecasting information for their proper integration. This necessitates the robust forecasting techniques in terms of faster speed and high accuracy. There is a large number of statistical and machine learning methods available for forecasting, but individually their speed and accuracy is lesser than the hybrid methods. This paper proposes two-hybrid methods ARIMA-SVR and ARIMA-RNN which are very fast and accurate. These methods are tested on a wind farm dataset of two zones, and hourly predictions are performed on these. Results obtained have shown that ARIMA-SVR hybrid architecture outperformed the ARIMA-RNN for the selected two wind farm zones.