In the context of visual perception, the optical signal from a scene is
transferred into the electronic domain by detectors in the form of image data,
which are then processed for the extraction of visual information. In noisy and
weak-signal environments such as thermal imaging for night vision applications,
however, the performance of neural computing tasks faces a significant
bottleneck due to the inherent degradation of data quality upon noisy
detection. Here, we propose a concept of optical signal processing before
detection to address this issue. We demonstrate that spatially redistributing
optical signals through a properly designed linear transformer can enhance the
detection noise resilience of visual perception tasks, as benchmarked with the
MNIST classification. Our idea is supported by a quantitative analysis
detailing the relationship between signal concentration and noise robustness,
as well as its practical implementation in an incoherent imaging system. This
compute-first detection scheme can pave the way for advancing infrared machine
vision technologies widely used for industrial and defense applications.

我们提出了一种光信号处理方案，在检测之前通过适当设计的线性变换器对光信号进行空间重新分布，以增强视觉感知任务的检测噪声鲁棒性，并通过 MNIST 分类的基准测试进行了验证。这种计算优先的检测方案可以推动广泛应用于工业和国防领域的红外机器视觉技术的发展。

计算优先的噪音鲁棒视觉感知

Compute-first optical detection for noise-resilient visual perception

Looming detection plays an important role in insect collision prevention
systems. As a vital capability evolutionary survival, it has been extensively
studied in neuroscience and is attracting increasing research interest in
robotics due to its close relationship with collision detection and navigation.
Visual cues such as angular size, angular velocity, and expansion have been
widely studied for looming detection by means of optic flow or elementary
neural computing research. However, a critical visual motion cue has been long
neglected because it is so easy to be confused with expansion, that is
radial-opponent-motion (ROM). Recent research on the discovery of LPLC2, a
ROM-sensitive neuron in Drosophila, has revealed its ultra-selectivity because
it only responds to stimuli with focal, outward movement. This characteristic
of ROM-sensitivity is consistent with the demand for collision detection
because it is strongly associated with danger looming that is moving towards
the center of the observer. Thus, we hope to extend the well-studied neural
model of the lobula giant movement detector (LGMD) with ROM-sensibility in
order to enhance robustness and accuracy at the same time. In this paper, we
investigate the potential to extend an image velocity-based looming detector,
the lobula giant movement detector (LGMD), with ROM-sensibility. To achieve
this, we propose the mathematical definition of ROM and its main property, the
radial motion opponency (RMO). Then, a synaptic neuropile that analogizes the
synaptic processing of LPLC2 is proposed in the form of lateral inhibition and
attention. Thus, our proposed model is the first to perform both image velocity
selectivity and ROM sensitivity. Systematic experiments are conducted to
exhibit the huge potential of the proposed bio-inspired looming detector.

本篇研究旨在将感知 RADIAL-OPPONENT-MOTION (ROM) 的神经元 ——LPLC2 运用到 LGMD 模型中，提高图像速度及 ROM 灵敏度，为昆虫的碰撞预防系统及机器人的碰撞检测及导航系统提供更高的鲁棒性和精度。

OppLoD: 基于对立性的迫近检测器，从 LGMD 模型扩展到 LPLC2 模型的迫近敏感性

OppLoD: the Opponency based Looming Detector, Model Extension of Looming Sensitivity from LGMD to LPLC2

In order to formally understand the power of neural computing, we first need
to crack the frontier of threshold circuits with two and three layers, a regime
that has been surprisingly intractable to analyze. We prove the first
super-linear gate lower bounds and the first super-quadratic wire lower bounds
for depth-two linear threshold circuits with arbitrary weights, and depth-three
majority circuits computing an explicit function.
$\bullet$ We prove that for all $\epsilon\gg \sqrt{\log(n)/n}$, the
linear-time computable Andreev's function cannot be computed on a
$(1/2+\epsilon)$-fraction of $n$-bit inputs by depth-two linear threshold
circuits of $o(\epsilon^3 n^{3/2}/\log^3 n)$ gates, nor can it be computed with
$o(\epsilon^{3} n^{5/2}/\log^{7/2} n)$ wires. This establishes an average-case
``size hierarchy'' for threshold circuits, as Andreev's function is computable
by uniform depth-two circuits of $o(n^3)$ linear threshold gates, and by
uniform depth-three circuits of $O(n)$ majority gates.
$\bullet$ We present a new function in $P$ based on small-biased sets, which
we prove cannot be computed by a majority vote of depth-two linear threshold
circuits with $o(n^{3/2}/\log^3 n)$ gates, nor with $o(n^{5/2}/\log^{7/2}n)$
wires.
$\bullet$ We give tight average-case (gate and wire) complexity results for
computing PARITY with depth-two threshold circuits; the answer turns out to be
the same as for depth-two majority circuits.
The key is a new random restriction lemma for linear threshold functions. Our
main analytical tool is the Littlewood-Offord Lemma from additive
combinatorics.

该论文研究了神经计算的权限，并证明了阈值电路的下限

深度为二和三的阈值电路的超线性门和超二次线性电缆下界

Super-Linear Gate and Super-Quadratic Wire Lower Bounds for Depth-Two  and Depth-Three Threshold Circuits

With the overwhelming success in the field of quantum information in the last
decades, the "quest" for a Quantum Neural Network (QNN) model began in order to
combine quantum computing with the striking properties of neural computing.
This article presents a systematic approach to QNN research, which so far
consists of a conglomeration of ideas and proposals. It outlines the challenge
of combining the nonlinear, dissipative dynamics of neural computing and the
linear, unitary dynamics of quantum computing. It establishes requirements for
a meaningful QNN and reviews existing literature against these requirements. It
is found that none of the proposals for a potential QNN model fully exploits
both the advantages of quantum physics and computing in neural networks. An
outlook on possible ways forward is given, emphasizing the idea of Open Quantum
Neural Networks based on dissipative quantum computing.

本文提出了一种系统方法，对量子神经网络的研究进行了综述和总结，旨在将量子计算与神经计算的突出特性相结合，但是目前为止并没有一个潜在的 QNN 模型能够完全利用量子物理和计算在神经网络中的优势，未来的发展方向可能是基于耗散量子计算的开放量子神经网络。