Search

Swarm perception refers to the ability of a robot swarm to utilize the perception capabilities of each individual robot, forming a collective understanding of the environment. Their distributed nature enables robot swarms to continuously monitor dynamic environments by maintaining a constant presence throughout the space.In this study, we present a preliminary experiment on the collective tracking of people using a robot swarm. The experiment was conducted in simulation across four different office environments, with swarms of varying sizes. The robots were provided with images sampled from a dataset of real-world office environment pictures.We measured the time distribution required for a robot to detect a person changing location and to propagate this information to increasing fractions of the swarm. The results indicate that robot swarms show significant promise in monitoring dynamic environments., Comment: Presented at ICRA@40, Rotterdam

Recently, Younes et al. proposed an efficient multi-party semi-quantum secret sharing (SQSS) scheme that generalizes Tian et al.'s three-party protocol \cite{Tian2021} to accommodate multiple participants. This scheme retains the original advantages, such as high qubit efficiency and allowing the secret dealer, Alice, to control the message content. However, He et al. \cite{He2024} identified a vulnerability in Tian et al.'s protocol to the double CNOT attack (DCNA), which also affects the generalized scheme. In response, He et al. proposed an improved protocol to address this issue. Despite these improvements, their protocol is limited to two participants and remains a primarily two-way communication scheme, which does not fully prevent the Trojan horse attack without expensive quantum devices such as photon number splitters (PNS) and wavelength filters (WF). To address these issues, this paper develops a novel multi-party SQSS scheme using the quantum property between Bell states and the Hadamard operation to detect eavesdroppers. This new scheme is secure against the DCNA, intercept-resend attack, and collective attack. It employs a fully one-way communication scheme, entirely preventing the Trojan horse attack without costly quantum devices, aligning with the semi-quantum environment's original intent. This new protocol also offers better qubit efficiency and allows Alice to share specific secrets., Comment: 18 pages, 1 table, research paper

Targeted protein degradation (TPD) is a rapidly growing field in modern drug discovery that aims to regulate the intracellular levels of proteins by harnessing the cell's innate degradation pathways to selectively target and degrade disease-related proteins. This strategy creates new opportunities for therapeutic intervention in cases where occupancy-based inhibitors have not been successful. Proteolysis-targeting chimeras (PROTACs) are at the heart of TPD strategies, which leverage the ubiquitin-proteasome system for the selective targeting and proteasomal degradation of pathogenic proteins. As the field evolves, it becomes increasingly apparent that the traditional methodologies for designing such complex molecules have limitations. This has led to the use of machine learning (ML) and generative modeling to improve and accelerate the development process. In this review, we explore the impact of ML on de novo PROTAC design $-$ an aspect of molecular design that has not been comprehensively reviewed despite its significance. We delve into the distinct characteristics of PROTAC linker design, underscoring the complexities required to create effective bifunctional molecules capable of TPD. We then examine how ML in the context of fragment-based drug design (FBDD), honed in the realm of small-molecule drug discovery, is paving the way for PROTAC linker design. Our review provides a critical evaluation of the limitations inherent in applying this method to the complex field of PROTAC development. Moreover, we review existing ML works applied to PROTAC design, highlighting pioneering efforts and, importantly, the limitations these studies face. By offering insights into the current state of PROTAC development and the integral role of ML in PROTAC design, we aim to provide valuable perspectives for researchers in their pursuit of better design strategies for this new modality.

O-acetylations are functional modifications which can be found on different hydroxyl groups of glycans and which contribute to the fine tuning of their biological activity. Localizing the acetyl modifications is notoriously challenging in glycoanalysis, in particular because of their mobility: loss or migration of the acetyl group may occur through the analytical workflow. Whereas migration conditions in the condensed phase have been rationalized, little is known about the suitability of Mass Spectrometry to retain and resolve the structure of O-acetylated glycan isomers. Here we use the resolving power of infrared ion spectroscopy in combination with ab initio calculation to assess the structure of O-acetylated monosaccharide ions in the gaseous environment of a mass analyzer. N-acetyl glucosamines were synthetized with an O-acetyl group in positions 3 or 6, respectively. The protonated ions produced by electrospray ionization were observed by mass spectrometry and their vibrational fingerprints were recorded in the 3 $\mu$m range by IRMPD spectroscopy (InfraRed Multiple Photon Dissociation). Experimentally, the isomers show distinctive IR fingerprints. Additionally, ab initio calculations confirm the position of the O-acetylation and resolve their gas phase conformation. These findings demonstrate that the position of O-acetyl groups is retained through the transfer from solution to the gas phase, and can be identified by IRMPD spectroscopy.

Diffusion models have shown an impressive ability to model complex data distributions, with several key advantages over GANs, such as stable training, better coverage of the training distribution's modes, and the ability to solve inverse problems without extra training. However, most diffusion models learn the distribution of fixed-resolution images. We propose to learn the distribution of continuous images by training diffusion models on image neural fields, which can be rendered at any resolution, and show its advantages over fixed-resolution models. To achieve this, a key challenge is to obtain a latent space that represents photorealistic image neural fields. We propose a simple and effective method, inspired by several recent techniques but with key changes to make the image neural fields photorealistic. Our method can be used to convert existing latent diffusion autoencoders into image neural field autoencoders. We show that image neural field diffusion models can be trained using mixed-resolution image datasets, outperform fixed-resolution diffusion models followed by super-resolution models, and can solve inverse problems with conditions applied at different scales efficiently., Comment: Project page: https://yinboc.github.io/infd/

Recent approaches have shown promises distilling diffusion models into efficient one-step generators. Among them, Distribution Matching Distillation (DMD) produces one-step generators that match their teacher in distribution, without enforcing a one-to-one correspondence with the sampling trajectories of their teachers. However, to ensure stable training, DMD requires an additional regression loss computed using a large set of noise-image pairs generated by the teacher with many steps of a deterministic sampler. This is costly for large-scale text-to-image synthesis and limits the student's quality, tying it too closely to the teacher's original sampling paths. We introduce DMD2, a set of techniques that lift this limitation and improve DMD training. First, we eliminate the regression loss and the need for expensive dataset construction. We show that the resulting instability is due to the fake critic not estimating the distribution of generated samples accurately and propose a two time-scale update rule as a remedy. Second, we integrate a GAN loss into the distillation procedure, discriminating between generated samples and real images. This lets us train the student model on real data, mitigating the imperfect real score estimation from the teacher model, and enhancing quality. Lastly, we modify the training procedure to enable multi-step sampling. We identify and address the training-inference input mismatch problem in this setting, by simulating inference-time generator samples during training time. Taken together, our improvements set new benchmarks in one-step image generation, with FID scores of 1.28 on ImageNet-64x64 and 8.35 on zero-shot COCO 2014, surpassing the original teacher despite a 500X reduction in inference cost. Further, we show our approach can generate megapixel images by distilling SDXL, demonstrating exceptional visual quality among few-step methods., Comment: Code, model, and dataset are available at https://tianweiy.github.io/dmd2

Whilst multi-principal element alloys (MPEAs) remain a promising class of materials owing to several attractive mechanical properties, their corrosion performance is also unique. In this concise review, we present an emerging overview of some of the general features related to MPEA corrosion, following a decade of work in the field. This includes highlighting some of the key aspects related to the electrochemical phenomena in MPEA corrosion, and the relevant future works required for a holistic mechanistic understanding. In addition, a comprehensive database of the reported corrosion performance of MPEAs is presented, based on works reported to date. The database is assembled to also allow users to undertake machine learning or their own data analysis, with a parsed representation of alloy composition, test electrolyte, and corrosion related parameters.

Most studies in swarm robotics treat the swarm as an isolated system of interest. We argue that the prevailing view of swarms as self-sufficient, independent systems limits the scope of potential applications for swarm robotics. A robot swarm could act as a support in an heterogeneous system comprising other robots and/or human operators, in particular by quickly providing access to a large amount of data acquired in large unknown environments. Tasks such as target identification & tracking, scouting, or monitoring/surveillance could benefit from this approach., Comment: Presented at the "Breaking swarm stereotypes" workshop at ICRA 2024

In this paper, we propose a new efficient mediated semi-quantum key distribution (MSQKD) protocol, facilitating the establishment of a shared secret key between two classical participants with the assistance of an untrusted third party (TP). Unlike existing MSQKD protocols, our approach significantly reduces the quantum requirements for TP, who only needs to prepare and measure qubits in the $X$ basis. Meanwhile, the classical participants are limited to preparing and measuring qubits in the $Z$ basis, along with performing Hadamard operations. This reduction in quantum overhead enhances the practicality of our MSQKD protocol without compromising qubit efficiency. Additionally, we demonstrate the security of our protocol against various well-known attacks., Comment: 24 pages, 11 tables

Diffusion models have made significant advances in text-guided synthesis tasks. However, editing user-provided images remains challenging, as the high dimensional noise input space of diffusion models is not naturally suited for image inversion or spatial editing. In this work, we propose an image representation that promotes spatial editing of input images using a diffusion model. Concretely, we learn to encode an input into "image elements" that can faithfully reconstruct an input image. These elements can be intuitively edited by a user, and are decoded by a diffusion model into realistic images. We show the effectiveness of our representation on various image editing tasks, such as object resizing, rearrangement, dragging, de-occlusion, removal, variation, and image composition. Project page: https://jitengmu.github.io/Editable_Image_Elements/, Comment: Project page: https://jitengmu.github.io/Editable_Image_Elements/

We introduce a novel diffusion transformer, LazyDiffusion, that generates partial image updates efficiently. Our approach targets interactive image editing applications in which, starting from a blank canvas or an image, a user specifies a sequence of localized image modifications using binary masks and text prompts. Our generator operates in two phases. First, a context encoder processes the current canvas and user mask to produce a compact global context tailored to the region to generate. Second, conditioned on this context, a diffusion-based transformer decoder synthesizes the masked pixels in a "lazy" fashion, i.e., it only generates the masked region. This contrasts with previous works that either regenerate the full canvas, wasting time and computation, or confine processing to a tight rectangular crop around the mask, ignoring the global image context altogether. Our decoder's runtime scales with the mask size, which is typically small, while our encoder introduces negligible overhead. We demonstrate that our approach is competitive with state-of-the-art inpainting methods in terms of quality and fidelity while providing a 10x speedup for typical user interactions, where the editing mask represents 10% of the image.

For decades, hospital services have been faced with the challenge of ensuring quality care for patients despite the pressures on staff due to workload overload. Nursing staff are particularly affected by this reality, with a patient/nursing staff ratio so imbalanced that it directly affects the quality of care and the well-being of nurses. This article examines in detail the workload of nursing staff, shedding light on the various factors influencing this workload, including the type of care provided, the characteristics of patients and nursing staff, as well as the organizational context. Furthermore, different methods of calculating and estimating workload are analyzed, ranging from workload calculation systems to advanced predictive models. Finally, reflection is made on future research avenues, particularly concerning the identification of factors influencing workload, data collection and processing, and model validation., Comment: in French language

We propose a generative model that, given a coarsely edited image, synthesizes a photorealistic output that follows the prescribed layout. Our method transfers fine details from the original image and preserves the identity of its parts. Yet, it adapts it to the lighting and context defined by the new layout. Our key insight is that videos are a powerful source of supervision for this task: objects and camera motions provide many observations of how the world changes with viewpoint, lighting, and physical interactions. We construct an image dataset in which each sample is a pair of source and target frames extracted from the same video at randomly chosen time intervals. We warp the source frame toward the target using two motion models that mimic the expected test-time user edits. We supervise our model to translate the warped image into the ground truth, starting from a pretrained diffusion model. Our model design explicitly enables fine detail transfer from the source frame to the generated image, while closely following the user-specified layout. We show that by using simple segmentations and coarse 2D manipulations, we can synthesize a photorealistic edit faithful to the user's input while addressing second-order effects like harmonizing the lighting and physical interactions between edited objects., Comment: Project page: https://magic-fixup.github.io/

Person re-identification (PRe-ID) is a computer vision issue, that has been a fertile research area in the last few years. It aims to identify persons across different non-overlapping camera views. In this paper, We propose a novel PRe-ID system that combines tensor feature representation and multilinear subspace learning. Our method exploits the power of pre-trained Convolutional Neural Networks (CNNs) as a strong deep feature extractor, along with two complementary descriptors, Local Maximal Occurrence (LOMO) and Gaussian Of Gaussian (GOG). Then, Tensor-based Cross-View Quadratic Discriminant Analysis (TXQDA) is used to learn a discriminative subspace that enhances the separability between different individuals. Mahalanobis distance is used to match and similarity computation between query and gallery samples. Finally, we evaluate our approach by conducting experiments on three datasets VIPeR, GRID, and PRID450s., Comment: arXiv admin note: substantial text overlap with arXiv:2312.10470

How to frame (or crop) a photo often depends on the image subject and its context; e.g., a human portrait. Recent works have defined the subject-aware image cropping task as a nuanced and practical version of image cropping. We propose a weakly-supervised approach (GenCrop) to learn what makes a high-quality, subject-aware crop from professional stock images. Unlike supervised prior work, GenCrop requires no new manual annotations beyond the existing stock image collection. The key challenge in learning from this data, however, is that the images are already cropped and we do not know what regions were removed. Our insight is to combine a library of stock images with a modern, pre-trained text-to-image diffusion model. The stock image collection provides diversity and its images serve as pseudo-labels for a good crop, while the text-image diffusion model is used to out-paint (i.e., outward inpainting) realistic uncropped images. Using this procedure, we are able to automatically generate a large dataset of cropped-uncropped training pairs to train a cropping model. Despite being weakly-supervised, GenCrop is competitive with state-of-the-art supervised methods and significantly better than comparable weakly-supervised baselines on quantitative and qualitative evaluation metrics., Comment: AAAI 24. Extended version with supplemental materials

In this paper, we present a novel person reidentification (PRe-ID) system that based on tensor feature representation and multilinear subspace learning. Our approach utilizes pretrained CNNs for high-level feature extraction, along with Local Maximal Occurrence (LOMO) and Gaussian Of Gaussian (GOG ) descriptors. Additionally, Cross-View Quadratic Discriminant Analysis (TXQDA) algorithm is used for multilinear subspace learning, which models the data in a tensor framework to enhance discriminative capabilities. Similarity measure based on Mahalanobis distance is used for matching between training and test pedestrian images. Experimental evaluations on VIPeR and PRID450s datasets demonstrate the effectiveness of our method.

Kinship verification from face images is a novel and formidable challenge in the realms of pattern recognition and computer vision. This work makes notable contributions by incorporating a preprocessing technique known as Multiscale Retinex (MSR), which enhances image quality. Our approach harnesses the strength of complementary deep (VGG16) and shallow texture descriptors (BSIF) by combining them at the score level using Logistic Regression (LR) technique. We assess the effectiveness of our approach by conducting comprehensive experiments on three challenging kinship datasets: Cornell Kin Face, UB Kin Face and TS Kin Face, Comment: arXiv admin note: text overlap with arXiv:2312.03562

We present VecFusion, a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions. Our approach is a cascaded diffusion model which consists of a raster diffusion model followed by a vector diffusion model. The raster model generates low-resolution, rasterized fonts with auxiliary control point information, capturing the global style and shape of the font, while the vector model synthesizes vector fonts conditioned on the low-resolution raster fonts from the first stage. To synthesize long and complex curves, our vector diffusion model uses a transformer architecture and a novel vector representation that enables the modeling of diverse vector geometry and the precise prediction of control points. Our experiments show that, in contrast to previous generative models for vector graphics, our new cascaded vector diffusion model generates higher quality vector fonts, with complex structures and diverse styles.

Diffusion models generate high-quality images but require dozens of forward passes. We introduce Distribution Matching Distillation (DMD), a procedure to transform a diffusion model into a one-step image generator with minimal impact on image quality. We enforce the one-step image generator match the diffusion model at distribution level, by minimizing an approximate KL divergence whose gradient can be expressed as the difference between 2 score functions, one of the target distribution and the other of the synthetic distribution being produced by our one-step generator. The score functions are parameterized as two diffusion models trained separately on each distribution. Combined with a simple regression loss matching the large-scale structure of the multi-step diffusion outputs, our method outperforms all published few-step diffusion approaches, reaching 2.62 FID on ImageNet 64x64 and 11.49 FID on zero-shot COCO-30k, comparable to Stable Diffusion but orders of magnitude faster. Utilizing FP16 inference, our model generates images at 20 FPS on modern hardware., Comment: CVPR 2024, Project page: https://tianweiy.github.io/dmd/

There is nowadays a growing demand in vehicular communications for real-time applications requiring video assistance. The new state-of-the-art high-efficiency video coding (HEVC) standard is very promising for real-time video streaming. It offers high coding efficiency, as well as dedicated low delay coding structures. Among these, the all intra (AI) coding structure guarantees minimal coding time at the expense of higher video bitrates, which therefore penalizes transmission performances. In this work, we propose an original cross-layer system in order to enhance received video quality in vehicular communications. The system is low complex and relies on a multiple description coding (MDC) approach. It is based on an adaptive mapping mechanism applied at the IEEE 802.11p standard medium access control (MAC) layer. Simulation results in a realistic vehicular environment demonstrate that for low delay video communications, the proposed method provides significant video quality improvements on the receiver side.

Vehicular communication has become a reality guided by various applications. Among those, high video quality delivery with low latency constraints required by real-time applications constitutes a very challenging task. By dint of its never-before-achieved compression level, the new High-Efficiency Video Coding (HEVC) is very promising for real-time video streaming through Vehicular Ad-hoc Networks (VANET). However, these networks have variable channel quality and limited bandwidth. Therefore, ensuring satisfactory video quality on such networks is a major challenge. In this work, a low complexity cross-layer mechanism is proposed to improve end-to-end performances of HEVC video streaming in VANET under low delay constraints. The idea is to assign to each packet of the transmitted video the most appropriate Access Category (AC) queue on the Medium Access Control (MAC) layer, considering the temporal prediction structure of the video encoding process, the importance of the frame and the state of the network traffic load. Simulation results demonstrate that for different targeted low-delay video communication scenarios, the proposed mechanism offers significant improvements regarding video quality at the reception and end-to-end delay compared to the Enhanced Distributed Channel Access (EDCA) adopted in the 802.11p. Both Quality of Service (QoS) and Quality of Experience (QoE) evaluations have been also carried out to validate the proposed approach.

Nowadays, real-time vehicle applications increasingly rely on video acquisition and processing to detect or even identify vehicles and obstacles in the driving environment. In this letter, we propose an algorithm that allows reinforcing these operations by improving end-to-end video transmission quality in a vehicular context. The proposed low complexity solution gives highest priority to the scene regions of interest (ROI) on which the perception of the driving environment is based on. This is done by applying an adaptive cross-layer mapping of the ROI visual data packets at the IEEE 802.11p MAC layer. Realistic VANET simulation results demonstrate that for HEVC compressed video communications, the proposed system offers PSNR gains up to 11dB on the ROI part.

We consider quantum systems with a Hamiltonian containing a weak perturbation i.e. $\boldsymbol{H=H_0} + \boldsymbol{\lambda} \cdot \boldsymbol{\tilde{H}}$, $\boldsymbol{\lambda}= \{\lambda_1, \lambda_2,...\}$, $\boldsymbol{\tilde{H}}$ $= \{H_1, H_2,...\}$, $\left|\boldsymbol{\lambda}\right| \ll 1$, and address situations where $\boldsymbol{\tilde{H}}$ is known but the values of the couplings $\boldsymbol{\lambda}$ are unknown, and should be determined by performing measurements on the system. We consider two scenarios: in the first one we assume that measurements are performed on a given stationary state of the system, e.g., the ground state, whereas in the second one an initial state is prepared and then measured after evolution. In both cases, we look for the optimal measurements to estimate the couplings and evaluate the ultimate limits to precision. In particular, we derive general results for one and two couplings, and analyze in details some specific qubit models. Our results indicates that dynamical estimation schemes may provide enhanced precision upon a suitable choice of the initial preparation and the interaction time., Comment: 8 pages, 1 figure

The purpose of this paper is to introduce and study the notion of generalized Reynolds operators on Lie triple systems with representations (Abbr. \textsf{L.t.sRep} pairs) as generalization of weighted Reynolds operators on Lie triple systems. First, We construct an $L_{\infty}$-algebra whose Maurer-Cartan elements are generalized Reynolds operators. This allows us to define a Yamaguti cohomology of a generalized Reynolds operator. This cohomology can be seen as the Yamaguti cohomology of a certain Lie triple system with coefficients in a suitable representation. Next, we study deformations of generalized Reynolds operators from cohomological points of view and we investigate the obstruction class of an extendable deformation of order $n$. We end this paper by introducing a new algebraic structure, in connection with generalized Reynolds operator, called NS-Lie triple system. Moreover, we show that NS-Lie triple systems can be derived from NS-Lie algebras.