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The availability of coherent sources of higher order Poincare optical beams have opened up new opportunities for applications such as in the optical trapping of atoms and small particles, the manipulation of chirally-sensitive systems and in improved encoding schemes for broad-bandwidth communications. Here we determine the intrinsic properties of Poincare Laguerre-Gaussian (LG) modes which have so far neither been evaluated, nor their significance highlighted. The theoretical framework we adopt here is both novel and essential because it emphasises the crucial role played by the normally ignored axial components of the twisted light fields of these modes. We show that the inclusion of the axial field components enables the intrinsic properties of the Poincare modes, notably their angular momentum, both spin and orbital as well as their helicity and chirality, to be determined. We predict significant enhancements of the intrinsic properties of these modes when compared with those due to the zero order LG modes. In particular, we show that higher order LG Poincare modes exhibit super-chirality and, significantly so, even in the case of the first order, Comment: 14 pages, 7 figures

We respond to the recent comment in Optics Communications by Kayn Forbes on our recent Optics Communications article and we maintain that, contrary to what Forbes claims, substantial superchirality exists as a property of the $m\geq 1$ higher order Poincare modes. Forbes arguments are based on misconceptions and analytical errors, leading to erroneous results and unjustified criticism., Comment: Reply to arXiv:2403.13495, which is itself a comment on arXiv:2301.05204

We report on experiments generating a magneto-optical trap (MOT) of 88-strontium ($^{88}$Sr) atoms at microkelvin temperature, using integrated-photonics devices. With metasurface optics integrated on a fused-silica substrate, we generate six-beam, circularly polarized, counter-propagating MOTs on the blue broad-line, 461 nm, and red narrow-line, 689 nm, Sr cooling transitions without bulk optics. By use of a diverging beam configuration, we create up to 10 mm diameter MOT beams at the trapping location. To frequency stabilize and linewidth narrow the cooling lasers, we use fiber-packaged, integrated nonlinear waveguides to spectrally broaden a frequency comb. The ultra-coherent supercontinuum of the waveguides covers 650 nm to 2500 nm, enabling phase locks of the cooling lasers to hertz level linewidth. Our work highlights the possibility to simplify the preparation of an ultracold 88Sr gas for an optical-lattice clock with photonic devices. By implementing a timing sequence for control of the MOT lasers and the quadrupole magnetic-field gradient, we collect atoms directly from a thermal beam into the blue MOT and continuously cool into a red MOT with dynamic detuning and intensity control. There, the red MOT temperature is as low as $2~{\mu}$K and the overall transfer efficiency up to 16%. We characterize this sequence, including an intermediate red MOT with modulated detuning. Our experiments demonstrate an integrated photonics system capable of cooling alkaline-earth gases to microkelvin temperature with sufficient transfer efficiencies for adoption in scalable optical clocks and quantum sensors., Comment: 5 pages, 3 figures

We demonstrate the formation of a complex, multi-wavelength, three-dimensional laser beam configuration with integrated metasurface optics. Our experiments support the development of a compact Sr optical-lattice clock, which leverages magneto-optical trapping on atomic transitions at 461 nm and 689 nm without bulk free-space optics. We integrate six, mm-scale metasurface optics on a fused-silica substrate and illuminate them with light from optical fibers. The metasurface optics provide full control of beam pointing, divergence, and polarization to create the laser configuration for a magneto-optical trap. We report the efficiency and integration of the three-dimensional visible laser beam configuration, demonstrating the suitability of metasurface optics for atomic laser cooling., Comment: 5 pages, 3 figures

In the modern transportation industry, accurate prediction of travelers' next destinations brings multiple benefits to companies, such as customer satisfaction and targeted marketing. This study focuses on developing a precise model that captures the sequential patterns and dependencies in travel data, enabling accurate predictions of individual travelers' future destinations. To achieve this, a novel model architecture with a sliding window approach based on Long Short-Term Memory (LSTM) is proposed for destination prediction in the transportation industry. The experimental results highlight satisfactory performance and high scores achieved by the proposed model across different data sizes and performance metrics. This research contributes to advancing destination prediction methods, empowering companies to deliver personalized recommendations and optimize customer experiences in the dynamic travel landscape.

Constructing first principles models is a challenging task for nonlinear and complex systems such as a wastewater treatment unit. In recent years, data-driven models are widely used to overcome the complexity. However, they often suffer from issues such as missing, low quality or noisy data. Transfer learning is a solution for this issue where knowledge from another task is transferred to target one to increase the prediction performance. In this work, the objective is increasing the prediction performance of an industrial wastewater treatment plant by transferring the knowledge of (i) an open-source simulation model that captures the underlying physics of the process, albeit with dissimilarities to the target plant, (ii) another industrial plant characterized by noisy and limited data but located in the same refinery, and (iii) the model in (ii) and making the objective function of the training problem physics informed where the physics information derived from the open-source model in (ii). The results have shown that test and validation performance are improved up to 27% and 59%, respectively.

We study a class of scheduling problems, where each job is divided into a batch of unit-size tasks and these tasks can be executed in parallel on multiple servers with New-Better-than-Used (NBU) service time distributions. While many delay optimality results are available for single-server queueing systems, generalizing these results to the multi-server case has been challenging. This motivated us to investigate near delay-optimal scheduling of batch jobs in multi-server queueing systems. We consider three lowcomplexity scheduling policies: the Fewest Unassigned Tasks first (FUT) policy, the Earliest Due Date first (EDD) policy, and the First-Come, First-Served (FCFS) policy. We prove that for arbitrary number, batch sizes, arrival times, and due times of the jobs, these scheduling policies are near delay-optimal in stochastic ordering for minimizing three classes of delay metrics among all causal and non-preemptive policies. In particular, the FUT policy is within a constant additive delay gap from the optimum for minimizing the mean average delay, and the FCFS policy within twice of the optimum for minimizing the mean maximum delay and the mean p-norm of delay. The key proof tools are several novel samplepath orderings, which can be used to compare the sample-path delay of different policies in a near-optimal sense., Comment: This is a preprint prepared in 2017. arXiv admin note: substantial text overlap with arXiv:1603.07322

A key challenge with procedure planning in instructional videos lies in how to handle a large decision space consisting of a multitude of action types that belong to various tasks. To understand real-world video content, an AI agent must proficiently discern these action types (e.g., pour milk, pour water, open lid, close lid, etc.) based on brief visual observation. Moreover, it must adeptly capture the intricate semantic relation of the action types and task goals, along with the variable action sequences. Recently, notable progress has been made via the integration of diffusion models and visual representation learning to address the challenge. However, existing models employ rudimentary mechanisms to utilize task information to manage the decision space. To overcome this limitation, we introduce a simple yet effective enhancement - a masked diffusion model. The introduced mask acts akin to a task-oriented attention filter, enabling the diffusion/denoising process to concentrate on a subset of action types. Furthermore, to bolster the accuracy of task classification, we harness more potent visual representation learning techniques. In particular, we learn a joint visual-text embedding, where a text embedding is generated by prompting a pre-trained vision-language model to focus on human actions. We evaluate the method on three public datasets and achieve state-of-the-art performance on multiple metrics. Code is available at https://github.com/ffzzy840304/Masked-PDPP., Comment: 7 pages (main text excluding references), 3 figures, 7 tables

The rapid and accurate detection of COVID-19 cases is critical for timely treatment and preventing the spread of the disease. In this study, a two-stage feature extraction framework using eight state-of-the-art pre-trained deep Convolutional Neural Networks (CNNs) and an autoencoder is proposed to determine the health conditions of patients (COVID-19, Normal, Viral Pneumonia) based on chest X-rays. The X-ray scans are divided into four equally sized sections and analyzed by deep pre-trained CNNs. Subsequently, an autoencoder with three hidden layers is trained to extract reproductive features from the concatenated ouput of CNNs. To evaluate the performance of the proposed framework, three different classifiers, which are single-layer perceptron (SLP), multi-layer perceptron (MLP), and support vector machine (SVM) are used. Furthermore, the deep CNN architectures are used to create benchmark models and trained on the same dataset for comparision. The proposed framework outperforms other frameworks wih pre-trained feature extractors in binary classification and shows competitive results in three-class classification. The proposed methodology is task-independent and suitable for addressing various problems. The results show that the discriminative features are a subset of the reproductive features, suggesting that extracting task-independent features is superior to the extraction only task-based features. The flexibility and task-independence of the reproductive features make the conceptive information approach more favorable. The proposed methodology is novel and shows promising results for analyzing medical image data.

A novel experimental technique for the realisation of the optical Faraday effect using Laguerre- Gaussian (LG) light is described. The experiment employs a zero-order vortex half-wave retarder to generate a radially or azimuthally-polarised LG doughnut beam. The light emerging from the retarder then passes through a linear polariser, which gives rise to two intensity lobes, with the orientation of the intensity gap between the two lobes pointing parallel (perpendicular) to the polarization direction of the radially (azimuthally) polarised beam. To complete the Faraday set up, the light traverses a material subject to a magnetic field, before passing through a final linear polariser, which results in a visible rotation of the lobes pattern. This technique exhibits the Faraday effect readily visually, without further elaborate steps to detect changes in the light intensity. The degree of rotation of the plane of polarisation is determined directly by the visibly clear change in the orientation of the intensity gap between the lobes., Comment: 4 figures

Constructing first-principles models is usually a challenging and time-consuming task due to the complexity of the real-life processes. On the other hand, data-driven modeling, and in particular neural network models often suffer from issues such as overfitting and lack of useful and highquality data. At the same time, embedding trained machine learning models directly into the optimization problems has become an effective and state-of-the-art approach for surrogate optimization, whose performance can be improved by physics-informed training. In this study, it is proposed to upgrade piece-wise linear neural network models with physics informed knowledge for optimization problems with neural network models embedded. In addition to using widely accepted and naturally piece-wise linear rectified linear unit (ReLU) activation functions, this study also suggests piece-wise linear approximations for the hyperbolic tangent activation function to widen the domain. Optimization of three case studies, a blending process, an industrial distillation column and a crude oil column are investigated. For all cases, physics-informed trained neural network based optimal results are closer to global optimality. Finally, associated CPU times for the optimization problems are much shorter than the standard optimization results., Comment: submitted to Computers and Chemical Engineering

We demonstrate that higher order Poincare modes of order m are super-chiral, displaying enhancement factors proportional to $m$ and $m^2$ in their helicity/chirality. With m having arbitrarily large integer values, such modes, in principle, possess unlimited super-chirality. These findings pave the way to applications, including the strong enhancements of optical interactions with chiral matter. The work indicates considerable flexibility in controlling the helicity of any higher order paraxial twisted light mode and it incorporates a very wide range of physical scenarios., Comment: 5 pages, 3 figures

Millimeter-wave transceivers use large antenna arrays to form narrow high-directional beams and overcome severe attenuation. Narrow beams require large signaling overhead to be aligned if no prior information about beam directions is available. Moreover, beams drift with time due to user mobility and may need to be realigned. Beam tracking is commonly used to keep the beams tightly coupled and eliminate the overhead associated with realignment. Hence, with periodic measurements, beams are adjusted before they lose alignment. We propose a model where the receiver adjusts beam direction "continuously" over each physical-layer sample according to a carefully calculated estimate of the continuous variation of the beams. In our approach, the change of direction is updated using the rate variation prediction of beam angles via three different solutions. Our approach incurs no additional overhead in pilots, yet, the performance of beam tracking is improved significantly. Numerical results reveal an SNR enhancement associated with reducing the MSE of the beam directions. In addition, our approach reduces the pilot overhead by 60% and up to 87% while achieving a similar total tracking duration as the state-of-the-art., Comment: The paper is accepted to be published by IEEE Transactions on Wireless Communications

Motivated by the rise of quantum computers, existing public-key cryptosystems are expected to be replaced by post-quantum schemes in the next decade in billions of devices. To facilitate the transition, NIST is running a standardization process which is currently in its final Round. Only three digital signature schemes are left in the competition, among which Dilithium and Falcon are the ones based on lattices. Classical fault attacks on signature schemes make use of pairs of faulty and correct signatures to recover the secret key which only works on deterministic schemes. To counter such attacks, Dilithium offers a randomized version which makes each signature unique, even when signing identical messages. In this work, we introduce a novel Signature Correction Attack which not only applies to the deterministic version but also to the randomized version of Dilithium and is effective even on constant-time implementations using AVX2 instructions. The Signature Correction Attack exploits the mathematical structure of Dilithium to recover the secret key bits by using faulty signatures and the public-key. It can work for any fault mechanism which can induce single bit-flips. For demonstration, we are using Rowhammer induced faults. Thus, our attack does not require any physical access or special privileges, and hence could be also implemented on shared cloud servers. We perform a thorough classical and quantum security analysis of Dilithium and successfully recover 1,851 bits out of 3,072 bits of secret key $s_1$ for security level 2. The lattice strength against quantum attackers is reduced from $2^{128}$ to $2^{81}$ while the strength against classical attackers is reduced from $2^{141}$ to $2^{89}$. Hence, the Signature Correction Attack may be employed to achieve a practical attack on Dilithium (security level 2) as proposed in Round 3 of the NIST post-quantum standardization process.

Hard example mining methods generally improve the performance of the object detectors, which suffer from imbalanced training sets. In this work, two existing hard example mining approaches (LRM and focal loss, FL) are adapted and combined in a state-of-the-art real-time object detector, YOLOv5. The effectiveness of the proposed approach for improving the performance on hard examples is extensively evaluated. The proposed method increases mAP by 3% compared to using the original loss function and around 1-2% compared to using the hard-mining methods (LRM or FL) individually on 2021 Anti-UAV Challenge Dataset., Comment: ICIP 2022. 5 pages, 2 figures, 7 tables. The codes are available at https://github.com/aybora/yolov5Loss

We study the possibility of the generation of the photo-induced image currents at a distance from the surface of nano-sized metal clusters by using time-dependent perturbation theory. We reveal that the wave function of an electron excited to the image state is localized outside the surface and current flows in a spherical shell whose radius is a few times the radius of the sphere. Spin polarized light has been applied to a perfect icosahedral metal cluster Li$_{13}$ whose optimization is achieved by molecular dynamic simulation and band structure is obtained by DFT method and by solution of radial Schr\"odinger equation. Up to our knowledge, despite the great effort on their characteristics, image electrons have not been the subject of the studies on photo-induced current., Comment: 4 pages