9 research outputs found
DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification
Data augmentation is vital to the generalization ability and robustness of
deep neural networks (DNNs) models. Existing augmentation methods for speaker
verification manipulate the raw signal, which are time-consuming and the
augmented samples lack diversity. In this paper, we present a novel
difficulty-aware semantic augmentation (DASA) approach for speaker
verification, which can generate diversified training samples in speaker
embedding space with negligible extra computing cost. Firstly, we augment
training samples by perturbing speaker embeddings along semantic directions,
which are obtained from speaker-wise covariance matrices. Secondly, accurate
covariance matrices are estimated from robust speaker embeddings during
training, so we introduce difficultyaware additive margin softmax
(DAAM-Softmax) to obtain optimal speaker embeddings. Finally, we assume the
number of augmented samples goes to infinity and derive a closed-form upper
bound of the expected loss with DASA, which achieves compatibility and
efficiency. Extensive experiments demonstrate the proposed approach can achieve
a remarkable performance improvement. The best result achieves a 14.6% relative
reduction in EER metric on CN-Celeb evaluation set.Comment: Accepted by ICASSP 202
Mechanical transistors for logic-with-memory computing
As a potential revolutionary topic in future information processing,
mechanical computing has gained tremendous attention for replacing or
supplementing conventional electronics vulnerable to power outages, security
attacks, and harsh environments. Despite its potential for constructing
intelligent matter towards nonclassical computing systems beyond the von
Neumann architecture, most works on mechanical computing demonstrated that the
ad hoc design of simple logic gates cannot fully realize a universal mechanical
processing framework involving interconnected arithmetic logic components and
memory. However, such a logic-with-memory computing architecture is critical
for complex and persistent state-dependent computations such as sequential
logic. Here we propose a mechanical transistor (M-Transistor), abstracting
omnipresent temperatures as the input-output mechanical bits, which consists of
a metamaterial thermal channel as the gate terminal driving a nonlinear
bistable soft actuator to selectively connect the output terminal to two other
variable thermal sources. This M-Transistor is an elementary unit to modularly
form various combinational and sequential circuits, such as complex logic
gates, registers (volatile memory), and long-term memories (non-volatile
memory) with much fewer units than the electronic counterparts. Moreover, they
can establish a universal processing core comprising an arithmetic circuit and
a register in a compact, reprogrammable network involving periodic read, write,
memory, and logic operations of the mechanical bits. Our work contributes to
realizing a non-electric universal mechanical computing architecture that
combines multidisciplinary engineering with structural mechanics, materials
science, thermal engineering, physical intelligence, and computational science.Comment: 25 pages, 4 figures, Articl
Few-photon single ionization of cold rubidium in the over-the-barrier regime
Photoionization of the rubidium (Rb) atoms cooled in a magneto-optical trap,
characterized by the coexistence of the ground 5 and the excited
5 states, is investigated experimentally and theoretically with the
400 nm femtosecond laser pulses at intensities of W/cm -
W/cm. Recoil-ion momentum distribution (RIMD) of Rb
exhibits rich ring-like structures and their energies correspond to one-photon
ionization of the 5 state, two-photon and three-photon ionizations of
the 5 state, respectively. With the increasing of , we find that
experimental signals near zero-momentum (NZM) in RIMDs resulted from the
5 state enhance dramatically and its peaked Rb momenta dwindle
obviously while that from the 5 state is maintained. Meanwhile, the
ion-yield ratio of the 5 over the 5 states varies from to
as increases. These features indicate a transition from
perturbative ionization to strong-perturbative ionization for the 5
state. Numerical simulations by solving the time-dependent Schr\"odinger
equation (TDSE) can qualitatively explain the measurements of RIMD, photoion
angular distributions, as well as ion-yield ratio. However, some discrepancies
still exist, especially for the NZM dip, which could stem from the
electron-electron correlation that is neglected in the present TDSE simulations
since we have adopted the single-active-electron approximation
Recommended from our members
Author Correction: A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images.
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data
Multifield-Modulated Spintronic Terahertz Emitter Based on a Vanadium Dioxide Phase Transition
The efficient generation and active
modulation of terahertz (THz)
waves are strongly required for the development of various THz applications
such as THz imaging/spectroscopy and THz communication. In addition,
due to the increasing degree of integration for the THz optoelectronic
devices, miniaturizing the complex THz system into a compact unit
is also important and necessary. Today, integrating the THz source
with the modulator to develop a powerful, easy-to-adjust, and scalable
or on-chip THz emitter is still a challenge. As a new type of THz
emitter, a spintronic THz emitter has attracted a great deal of attention
due to its advantages of high efficiency, ultrawide band, low cost,
and easy integration. In this study, we have proposed a multifield-modulated
spintronic THz emitter based on the VO2/Ni/Pt multilayer
film structure with a wide band region of 0–3 THz. Because
of the pronounced phase transition of the integrated VO2 layer, the fabricated THz emitter can be efficiently modulated via
thermal or electric stimuli with a modulation depth of about one order
of magnitude; the modulation depths under thermal stimulation and
electrical stimulation were 91.8% and 97.3%, respectively. It is believed
that this multifield modulated spintronic THz emitter will provide
various possibilities for the integration of next-generation on-chip
THz sources and THz modulators