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Time-of-flight resolved light field fluctuations reveal deep human tissue physiology.
Red blood cells (RBCs) transport oxygen to tissues and remove carbon dioxide. Diffuse optical flowmetry (DOF) assesses deep tissue RBC dynamics by measuring coherent fluctuations of multiply scattered near-infrared light intensity. While classical DOF measurements empirically correlate with blood flow, they remain far-removed from light scattering physics and difficult to interpret in layered media. To advance DOF measurements closer to the physics, here we introduce an interferometric technique, surmounting challenges of bulk motion to apply it in awake humans. We reveal two measurement dimensions: optical phase, and time-of-flight (TOF), the latter with 22 picosecond resolution. With this multidimensional data, we directly confirm the unordered, or Brownian, nature of optically probed RBC dynamics typically assumed in classical DOF. We illustrate how incorrect absorption assumptions, anisotropic RBC scattering, and layered tissues may confound classical DOF. By comparison, our direct method enables accurate and comprehensive assessment of blood flow dynamics in humans
Adversarial Illusions in Multi-Modal Embeddings
Multi-modal embeddings encode images, sounds, texts, videos, etc. into a
single embedding space, aligning representations across modalities (e.g.,
associate an image of a dog with a barking sound). We show that multi-modal
embeddings can be vulnerable to an attack we call "adversarial illusions."
Given an image or a sound, an adversary can perturb it so as to make its
embedding close to an arbitrary, adversary-chosen input in another modality.
This enables the adversary to align any image and any sound with any text.
Adversarial illusions exploit proximity in the embedding space and are thus
agnostic to downstream tasks. Using ImageBind embeddings, we demonstrate how
adversarially aligned inputs, generated without knowledge of specific
downstream tasks, mislead image generation, text generation, and zero-shot
classification
VISinger 2: High-Fidelity End-to-End Singing Voice Synthesis Enhanced by Digital Signal Processing Synthesizer
End-to-end singing voice synthesis (SVS) model VISinger can achieve better
performance than the typical two-stage model with fewer parameters. However,
VISinger has several problems: text-to-phase problem, the end-to-end model
learns the meaningless mapping of text-to-phase; glitches problem, the harmonic
components corresponding to the periodic signal of the voiced segment occurs a
sudden change with audible artefacts; low sampling rate, the sampling rate of
24KHz does not meet the application needs of high-fidelity generation with the
full-band rate (44.1KHz or higher). In this paper, we propose VISinger 2 to
address these issues by integrating the digital signal processing (DSP) methods
with VISinger. Specifically, inspired by recent advances in differentiable
digital signal processing (DDSP), we incorporate a DSP synthesizer into the
decoder to solve the above issues. The DSP synthesizer consists of a harmonic
synthesizer and a noise synthesizer to generate periodic and aperiodic signals,
respectively, from the latent representation z in VISinger. It supervises the
posterior encoder to extract the latent representation without phase
information and avoid the prior encoder modelling text-to-phase mapping. To
avoid glitch artefacts, the HiFi-GAN is modified to accept the waveforms
generated by the DSP synthesizer as a condition to produce the singing voice.
Moreover, with the improved waveform decoder, VISinger 2 manages to generate
44.1kHz singing audio with richer expression and better quality. Experiments on
OpenCpop corpus show that VISinger 2 outperforms VISinger, CpopSing and
RefineSinger in both subjective and objective metrics.Comment: Submitted to ICASSP 202
Modelling and Performance Analysis of the Over-the-Air Computing in Cellular IoT Networks
Ultra-fast wireless data aggregation (WDA) of distributed data has emerged as
a critical design challenge in the ultra-densely deployed cellular internet of
things network (CITN) due to limited spectral resources. Over-the-air computing
(AirComp) has been proposed as an effective solution for ultra-fast WDA by
exploiting the superposition property of wireless channels. However, the effect
of access radius of access point (AP) on the AirComp performance has not been
investigated yet. Therefore, in this work, the mean square error (MSE)
performance of AirComp in the ultra-densely deployed CITN is analyzed with the
AP access radius. By modelling the spatial locations of internet of things
devices as a Poisson point process, the expression of MSE is derived in an
analytical form, which is validated by Monte Carlo simulations. Based on the
analytical MSE, we investigate the effect of AP access radius on the MSE of
AirComp numerically. The results show that there exists an optimal AP access
radius for AirComp, which can decrease the MSE by up to 12.7%. It indicates
that the AP access radius should be carefully chosen to improve the AirComp
performance in the ultra-densely deployed CITN
SoK: Pitfalls in Evaluating Black-Box Attacks
Numerous works study black-box attacks on image classifiers. However, these
works make different assumptions on the adversary's knowledge and current
literature lacks a cohesive organization centered around the threat model. To
systematize knowledge in this area, we propose a taxonomy over the threat space
spanning the axes of feedback granularity, the access of interactive queries,
and the quality and quantity of the auxiliary data available to the attacker.
Our new taxonomy provides three key insights. 1) Despite extensive literature,
numerous under-explored threat spaces exist, which cannot be trivially solved
by adapting techniques from well-explored settings. We demonstrate this by
establishing a new state-of-the-art in the less-studied setting of access to
top-k confidence scores by adapting techniques from well-explored settings of
accessing the complete confidence vector, but show how it still falls short of
the more restrictive setting that only obtains the prediction label,
highlighting the need for more research. 2) Identification the threat model of
different attacks uncovers stronger baselines that challenge prior
state-of-the-art claims. We demonstrate this by enhancing an initially weaker
baseline (under interactive query access) via surrogate models, effectively
overturning claims in the respective paper. 3) Our taxonomy reveals
interactions between attacker knowledge that connect well to related areas,
such as model inversion and extraction attacks. We discuss how advances in
other areas can enable potentially stronger black-box attacks. Finally, we
emphasize the need for a more realistic assessment of attack success by
factoring in local attack runtime. This approach reveals the potential for
certain attacks to achieve notably higher success rates and the need to
evaluate attacks in diverse and harder settings, highlighting the need for
better selection criteria
Structural-semantics Guided Program Simplification for Understanding Neural Code Intelligence Models
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Examining how internet use and non-farm employment affect rural households’ income gap? Evidence from China
The objective of this study is to assess the effect of Internet use on the income disparity between rural households and to determine how Internet usage can be used to reduce this income gap. We use the Recentered Influence Function Regression (RIF) and data from the China Family Panel Studies (CFPS) conducted by the China Social Science Survey (CSSS) center at Peking University to make the results of regression estimation more reliable. The results reveal that Internet use can make rural households’ income gap shrink considerably, and that the degree of non-farm employment among rural families has a mediating effect between Internet use and the income disparity of farm households. In addition, the Eastern region experiences a stronger mitigating effect from Internet use, whereas ethnic minorities find out no such mitigating effect. This study expands the scope of income disparity theory, provides new ideas for the construction of digital villages, and identifies new empirical evidence and decision-making grounds for improving the livelihoods of rural households and narrowing the income gap between rural households
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