Deep Multimodal Speaker Naming

Automatic speaker naming is the problem of localizing as well as identifying each speaking character in a TV/movie/live show video. This is a challenging problem mainly attributes to its multimodal nature, namely face cue alone is insufficient to achieve good performance. Previous multimodal approaches to this problem usually process the data of different modalities individually and merge them using handcrafted heuristics. Such approaches work well for simple scenes, but fail to achieve high performance for speakers with large appearance variations. In this paper, we propose a novel convolutional neural networks (CNN) based learning framework to automatically learn the fusion function of both face and audio cues. We show that without using face tracking, facial landmark localization or subtitle/transcript, our system with robust multimodal feature extraction is able to achieve state-of-the-art speaker naming performance evaluated on two diverse TV series. The dataset and implementation of our algorithm are publicly available online

Transition metal oxides for high performance sodium ion battery anodes

Sodium-ion batteries (SIBs) are attracting considerable attention with expectation of replacing lithium-ion batteries (LIBs) in large-scale energy storage systems (ESSs). To explore high performance anode materials for SIBs is highly desired subject to the current anode research mainly limited to carbonaceous materials. In this study, a series of transition metal oxides (TMOs) is successfully demonstrated as anodes for SIBs for the first time. The sodium uptake/extract is confirmed in the way of reversible conversion reaction. The pseudocapacitance-type behavior is also observed in the contribution of sodium capacity. For Fe2O3anode, a reversible capacity of 386 mAh g-1at 100 mA g-1 is achieved over 200 cycles; as high as 233 mAhg-1is sustained even cycling at a large current-density of 5 A g-1

You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors

In this paper, we propose a novel local descriptor-based framework, called You Only Hypothesize Once (YOHO), for the registration of two unaligned point clouds. In contrast to most existing local descriptors which rely on a fragile local reference frame to gain rotation invariance, the proposed descriptor achieves the rotation invariance by recent technologies of group equivariant feature learning, which brings more robustness to point density and noise. Meanwhile, the descriptor in YOHO also has a rotation equivariant part, which enables us to estimate the registration from just one correspondence hypothesis. Such property reduces the searching space for feasible transformations, thus greatly improves both the accuracy and the efficiency of YOHO. Extensive experiments show that YOHO achieves superior performances with much fewer needed RANSAC iterations on four widely-used datasets, the 3DMatch/3DLoMatch datasets, the ETH dataset and the WHU-TLS dataset. More details are shown in our project page: https://hpwang-whu.github.io/YOHO/.Comment: Accepted by ACM Multimedia(MM) 2022, Project page: https://hpwang-whu.github.io/YOHO

The c-differential properties of a class of power functions

Power functions with low $c$-differential uniformity have been widely studied not only because of their strong resistance to multiplicative differential attacks, but also low implementation cost in hardware. Furthermore, the $c$-differential spectrum of a function gives a more precise characterization of its $c$-differential properties. Let $f(x)=x^{\frac{p^n+3}{2}}$ be a power function over the finite field $\mathbb{F}_{p^{n}}$, where $p\neq3$ is an odd prime and $n$ is a positive integer. In this paper, for all primes $p\neq3$, by investigating certain character sums with regard to elliptic curves and computing the number of solutions of a system of equations over $\mathbb{F}_{p^{n}}$, we determine explicitly the $(-1)$-differential spectrum of $f$ with a unified approach. We show that if $p^n \equiv 3 \pmod 4$, then $f$ is a differentially $(-1,3)$-uniform function except for $p^n\in\{7,19,23\}$ where $f$ is an APcN function, and if $p^n \equiv 1 \pmod 4$, the $(-1)$-differential uniformity of $f$ is equal to $4$. In addition, an upper bound of the $c$-differential uniformity of $f$ is also given

CCD photometric study of the W UMa-type binary II CMa in the field of Berkeley 33

The CCD photometric data of the EW-type binary, II CMa, which is a contact star in the field of the middle-aged open cluster Berkeley 33, are presented. The complete R light curve was obtained. In the present paper, using the five CCD epochs of light minimum (three of them are calculated from Mazur et al. (1993)'s data and two from our new data), the orbital period P was revised to 0.22919704 days. The complete R light curve was analyzed by using the 2003 version of W-D (Wilson-Devinney) program. It is found that this is a contact system with a mass ratio $q=0.9$ and a contact factor $f=4.1$. The high mass ratio ($q=0.9$) and the low contact factor ($f=4.1$) indicate that the system just evolved into the marginal contact stage

Coordinated Control of a Wind-Methanol-Fuel Cell System with Hydrogen Storage

This paper presents a wind-methanol-fuel cell system with hydrogen storage. It can manage various energy flow to provide stable wind power supply, produce constant methanol, and reduce CO2 emissions. Firstly, this study establishes the theoretical basis and formulation algorithms. And then, computational experiments are developed with MATLAB/Simulink (R2016a, MathWorks, Natick, MA, USA). Real data are used to fit the developed models in the study. From the test results, the developed system can generate maximum electricity whilst maintaining a stable production of methanol with the aid of a hybrid energy storage system (HESS). A sophisticated control scheme is also developed to coordinate these actions to achieve satisfactory system performance

Design and Assessment of an Electric Vehicle Powertrain Model Based on Real-World Driving and Charging Cycles

In this paper, an advanced analytical model for an electric vehicle (EV) powertrain has been developed to illustrate the vehicular dynamics by combining electrical and mechanical models in the analysis. This study is based on a Nissan Leaf EV. In the electrical system, the powertrain has various components including a battery pack, a battery management system, a dc/dc converter, a dc/ac inverter, a permanent magnet synchronous motor, and a control system. In the mechanical system, it consists of power transmissions, axial shaft, and vehicle wheels. Furthermore, the driving performance of the Nissan Leaf is studied through the real-world driving tests and simulation tests in MATLAB/Simulink. In the analytical model, the vehicular dynamics is evaluated against changes in the vehicle velocity and acceleration, state of charge of the battery, and the motor power. Finally, a number of EVs involved in the power dispatch is studied. The greenhouse gas emissions of the EV are analyzed according to various energy power and driving features, and compared with the conventional internal combustion engine vehicle. In this case, Nissan Leaf is a pure EV. For a given drive cycle, Nissan Leaf can reduce CO2 emissions by 70%, depending on the way electricity is generated and duty cycles

Vegetation response to extreme climate events on the Mongolian Plateau from 2000 to 2010

Climate change has led to more frequent extreme winters (aka, dzud) and summer droughts on the Mongolian Plateau during the last decade. Among these events, the 2000–2002 combined summer drought–dzud and 2010 dzud were the most severe on vegetation. We examined the vegetation response to these extremes through the past decade across the Mongolian Plateau as compared to decadal means. We first assessed the severity and extent of drought using the Tropical Rainfall Measuring Mission (TRMM) precipitation data and the Palmer drought severity index (PDSI). We then examined the effects of drought by mapping anomalies in vegetation indices (EVI, EVI2) and land surface temperature derived from MODIS and AVHRR for the period of 2000–2010. We found that the standardized anomalies of vegetation indices exhibited positively skewed frequency distributions in dry years, which were more common for the desert biome than for grasslands. For the desert biome, the dry years (2000–2001, 2005 and 2009) were characterized by negative anomalies with peak values between �1.5 and �0.5 and were statistically different (P \u3c 0:001) from relatively wet years (2003, 2004 and 2007). Conversely, the frequency distributions of the dry years were not statistically different (p \u3c 0:001) from those of the relatively wet years for the grassland biome, showing that they were less responsive to drought and more resilient than the desert biome. We found that the desert biome is more vulnerable to drought than the grassland biome. Spatially averaged EVI was strongly correlated with the proportion of land area affected by drought (PDSI \u3c �1) in Inner Mongolia (IM) and Outer Mongolia (OM), showing that droughts substantially reduced vegetation activity. The correlation was stronger for the desert biome (R2 D 65 and 60, p \u3c 0:05) than for the IM grassland biome (R2 D 53, p \u3c 0:05). Our results showed significant differences in the responses to extreme climatic events (summer drought and dzud) between the desert and grassland biomes on the Plateau

Spatio-Temporal Patterns and Climate Variables Controlling of Biomass Carbon Stock of Global Grassland Ecosystems from 1982 to 2006

Grassland ecosystems play an important role in subsistence agriculture and the global carbon cycle. However, the global spatio-temporal patterns and environmental controls of grassland biomass are not well quantified and understood. The goal of this study was to estimate the spatial and temporal patterns of the global grassland biomass and analyze their driving forces using field measurements, Normalized Difference Vegetation Index (NDVI) time series from satellite data, climate reanalysis data, and a satellite-based statistical model. Results showed that the NDVI-based biomass carbon model developed from this study explained 60% of the variance across 38 sites globally. The global carbon stock in grassland aboveground live biomass was 1.05 Pg·C, averaged from 1982 to 2006, and increased at a rate of 2.43 Tg·C·y−1 during this period. Temporal change of the global biomass was significantly and positively correlated with temperature and precipitation. The distribution of biomass carbon density followed the precipitation gradient. The dynamics of regional grassland biomass showed various trends largely determined by regional climate variability, disturbances, and management practices (such as grazing for meat production). The methods and results from this study can be used to monitor the dynamics of grassland aboveground biomass and evaluate grassland susceptibility to climate variability and change, disturbances, and management

Energetic macroscopic representation control method for a hybrid-source energy system including wind, hydrogen, and fuel cell

This paper proposes a new control method for a hybrid energy system. A wind turbine, a hydrogen energy storage system, and a proton exchange membrane fuel cell are utilized in the system to balance the load and supply. The system is modeled in MATLAB/Simulink and is controlled by an improved energetic macroscopic representation (EMR) method in order to match the load profile with wind power. The simulation and test results have proved that (1) the proposed system is effective to meet the varying load demand with fluctuating wind power inputs, (2) the hybrid energy storage system can improve the stability and fault-ride-through performance of the system, and (3) the dynamic response of the proposed system is satisfactory to operate with wind turbines, energy storage, and fuel cells under EMR control