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To achieve dexterity comparable to that of humans, robots must intelligently process tactile sensor data. Taxel-based tactile signals often have low spatial-resolution, with non-standardized representations. In this paper, we propose a novel framework, HyperTaxel, for learning a geometrically-informed representation of taxel-based tactile signals to address challenges associated with their spatial resolution. We use this representation and a contrastive learning objective to encode and map sparse low-resolution taxel signals to high-resolution contact surfaces. To address the uncertainty inherent in these signals, we leverage joint probability distributions across multiple simultaneous contacts to improve taxel hyper-resolution. We evaluate our representation by comparing it with two baselines and present results that suggest our representation outperforms the baselines. Furthermore, we present qualitative results that demonstrate the learned representation captures the geometric features of the contact surface, such as flatness, curvature, and edges, and generalizes across different objects and sensor configurations. Moreover, we present results that suggest our representation improves the performance of various downstream tasks, such as surface classification, 6D in-hand pose estimation, and sim-to-real transfer., Comment: Accepted by IROS 2024

Trauma significantly impacts global health, accounting for over 5 million deaths annually, which is comparable to mortality rates from diseases such as tuberculosis, AIDS, and malaria. In Iran, the financial repercussions of road traffic accidents represent approximately 2% of the nation's Gross National Product each year. Bleeding is the leading cause of mortality in trauma patients within the first 24 hours following an injury, making rapid diagnosis and assessment of severity crucial. Trauma patients require comprehensive scans of all organs, generating a large volume of data. Evaluating CT images for the entire body is time-consuming and requires significant expertise, underscoring the need for efficient time management in diagnosis. Efficient diagnostic processes can significantly reduce treatment costs and decrease the likelihood of secondary complications. In this context, the development of a reliable Decision Support System (DSS) for trauma triage, particularly focused on the abdominal area, is vital. This paper presents a novel method for detecting liver bleeding and lacerations using CT scans, utilising the GAN Pix2Pix translation model. The effectiveness of the method is quantified by Dice score metrics, with the model achieving an accuracy of 97% for liver bleeding and 93% for liver laceration detection. These results represent a notable improvement over current state-of-the-art technologies. The system's design integrates seamlessly with existing medical imaging technologies, making it a practical addition to emergency medical services. This research underscores the potential of advanced image translation models like GAN Pix2Pix in improving the precision and speed of medical diagnostics in critical care scenarios.

Contemporary approaches to optical multiple micro-manipulation typically involve careful pre-engineering of the laser beam shape. In various biomedical and microfluidic scenarios, especially those necessitating unconventional specimen chambers, there is a demand for controlling the collection of micro-objects at fluid-fluid interfaces. This requirement arises in contexts such as the transport of materials across liquid interfaces for applications like living cell manipulation, drug delivery, soft functional material creation, and various industrial processes. For many of these cases, a regular array of trap sites as well as tightly confinement are not essential. For such applications at interfaces, we expand on the concept of speckle tweezers (ST), which incorporate randomly distributed light fields for quasi-2D optical manipulation. The proposed technique is demonstrated experimentally by applying ST to govern the movement of PS micro-particles at water-oil and water-air interfaces. The efficacy of the method is validated through the temporal characterization of micro-particle motions using digital video microscopy.

The development of data-driven approaches for solving differential equations has been followed by a plethora of applications in science and engineering across a multitude of disciplines and remains a central focus of active scientific inquiry. However, a large body of natural phenomena incorporates memory effects that are best described via fractional integro-differential equations (FIDEs), in which the integral or differential operators accept non-integer orders. Addressing the challenges posed by nonlinear FIDEs is a recognized difficulty, necessitating the application of generic methods with immediate practical relevance. This work introduces the Universal Fractional Integro-Differential Equation Solvers (UniFIDES), a comprehensive machine learning platform designed to expeditiously solve a variety of FIDEs in both forward and inverse directions, without the need for ad hoc manipulation of the equations. The effectiveness of UniFIDES is demonstrated through a collection of integer-order and fractional problems in science and engineering. Our results highlight UniFIDES' ability to accurately solve a wide spectrum of integro-differential equations and offer the prospect of using machine learning platforms universally for discovering and describing dynamical and complex systems., Comment: 27 pages, 9 figures, regular article

Convolutional Neural Networks (CNNs) and vision transformers (ViTs) have shown excellent capability in complex hyperspectral image (HSI) classification. However, these models require a significant number of training data and are computational resources. On the other hand, modern Multi-Layer Perceptrons (MLPs) have demonstrated great classification capability. These modern MLP-based models require significantly less training data compared to CNNs and ViTs, achieving the state-of-the-art classification accuracy. Recently, Kolmogorov-Arnold Networks (KANs) were proposed as viable alternatives for MLPs. Because of their internal similarity to splines and their external similarity to MLPs, KANs are able to optimize learned features with remarkable accuracy in addition to being able to learn new features. Thus, in this study, we assess the effectiveness of KANs for complex HSI data classification. Moreover, to enhance the HSI classification accuracy obtained by the KANs, we develop and propose a Hybrid architecture utilizing 1D, 2D, and 3D KANs. To demonstrate the effectiveness of the proposed KAN architecture, we conducted extensive experiments on three newly created HSI benchmark datasets: QUH-Pingan, QUH-Tangdaowan, and QUH-Qingyun. The results underscored the competitive or better capability of the developed hybrid KAN-based model across these benchmark datasets over several other CNN- and ViT-based algorithms, including 1D-CNN, 2DCNN, 3D CNN, VGG-16, ResNet-50, EfficientNet, RNN, and ViT. The code are publicly available at (https://github.com/aj1365/HSIConvKAN)

Recent advancements in robotics, including applications like self-driving cars, unmanned systems, and medical robots, have had a significant impact on the job market. On one hand, big robotics companies offer training programs based on the job requirements. However, these training programs may not be as beneficial as general robotics programs offered by universities or community colleges. On the other hand, community colleges and universities face challenges with required resources, especially qualified instructors, to offer students advanced robotics education. Furthermore, the diverse backgrounds of undergraduate students present additional challenges. Some students bring extensive industry experiences, while others are newcomers to the field. To address these challenges, we propose a student-centered personalized learning framework for robotics. This framework allows a general instructor to teach undergraduate-level robotics courses by breaking down course topics into smaller components with well-defined topic dependencies, structured as a graph. This modular approach enables students to choose their learning path, catering to their unique preferences and pace. Moreover, our framework's flexibility allows for easy customization of teaching materials to meet the specific needs of host institutions. In addition to teaching materials, a frequently-asked-questions document would be prepared for a general instructor. If students' robotics questions cannot be answered by the instructor, the answers to these questions may be included in this document. For questions not covered in this document, we can gather and address them through collaboration with the robotics community and course content creators. Our user study results demonstrate the promise of this method in delivering undergraduate-level robotics education tailored to individual learning outcomes and preferences., Comment: 11 pages, 3 figures, 1 table, 2024 ASEE Conference

The sixth generation (6G) mobile communication networks are expected to intelligently integrate into various aspects of modern digital society, including smart cities, homes, healthcare, transportation, and factories. While offering a multitude of services, it is likely that societies become increasingly reliant on 6G infrastructure. Any disruption to these digital services, whether due to human or technical failures, natural disasters, or terrorism, would significantly impact citizens' daily lives. Hence, 6G networks need not only to provide high-performance services but also to be resilient in maintaining essential services in the face of potentially unknown challenges. This paper introduces a comprehensive concept for designing resilient 6G communication networks, summarizing our initial studies within the German Open6GHub project. Adopting an interdisciplinary approach, we propose to embed physical and cyber resilience across all communication system layers, addressing electronics, physical channel, network components and functions, networks, services, and cross-layer and cross-infrastructure considerations. After reviewing the background on resilience concepts, definitions, and approaches, we introduce the proposed resilience-by-design (RBD) concept for 6G communication networks. We further elaborate on the proposed RBD concept along with selected 6G use-cases and present various open problems for future research on 6G resilience.

In brain neural networks, Local Field Potential (LFP) signals represent the dynamic flow of information. Analyzing LFP clinical data plays a critical role in improving our understanding of brain mechanisms. One way to enhance our understanding of these mechanisms is to identify a global model to predict brain signals in different situations. This paper identifies a global data-driven based on LFP recordings of the Nucleus Accumbens and Hippocampus regions in freely moving rats. The LFP is recorded from each rat in two different situations: before and after the process of getting a reward which can be either a drug (Morphine) or natural food (like popcorn or biscuit). A comparison of five machine learning methods including Long Short Term Memory (LSTM), Echo State Network (ESN), Deep Echo State Network (DeepESN), Radial Basis Function (RBF), and Local Linear Model Tree (LLM) is conducted to develop this model. LoLiMoT was chosen with the best performance among all methods. This model can predict the future states of these regions with one pre-trained model. Identifying this model showed that Morphine and natural rewards do not change the dynamic features of neurons in these regions.

The memristors are expected to be fundamental devices for neuromorphic systems and switching applications. For example, the device made of a sandwiched layer of poly(N-vinylcarbazole) and reduced graphene composite between asymmetric electrodes (ITO/PVK:rGO/Al) exhibits bistable resistive switching behavior. Depending on the resistance state of the (ON-state or OFF-state) at a constant applied voltage, it may show two different resistivities. The performance of the memristor can be optimized by controlling the doping amount of graphene oxide in the PVK polymer. To assess the performance of the device, when it switches between ON and OFF states, optical characterization approaches are highly promising due to their non-destructive and remote nature. Here, we characterize the memristor device by the use of speckle pattern (SP) analysis. The speckle pattern is the interference of multiple light waves with random relative phases, which is generated via different mechanisms such as scattering from diffusive materials. Therefore, SPs can be used to investigate such samples as they include a huge amount of information to be statistically elaborated. The experimental paradigm includes \textit{in situ} acquisition of SPs of the PVK:rGO in different states followed by statistical post-processing toward examining its conduction mechanism. The variations in these statistical parameters are attributed to the resistance state of the PVK:rGO samples under the applied voltage with regard to the physical switching mechanism of the device. The resistance/conduction state, in turn, depends on the activity and properties of PVK:rGO memristors as well as the additional non-uniformities induced through the variations of density of carriers. The present optical methodology can be potentially served as a bench-top device for characterization purposes of similar devices while they are operating.

Over the past two decades, the surge in video streaming applications has been fueled by the increasing accessibility of the internet and the growing demand for network video. As users with varying internet speeds and devices seek high-quality video, transcoding becomes essential for service providers. In this paper, we introduce a parametric rate-distortion (R-D) transcoding model. Our model excels at predicting transcoding distortion at various rates without the need for encoding the video. This model serves as a versatile tool that can be used to achieve visual quality improvement (in terms of PSNR) via trans-sizing. Moreover, we use our model to identify visually lossless and near-zero-slope bitrate ranges for an ingest video. Having this information allows us to adjust the transcoding target bitrate while introducing visually negligible quality degradations. By utilizing our model in this manner, quality improvements up to 2 dB and bitrate savings of up to 46% of the original target bitrate are possible. Experimental results demonstrate the efficacy of our model in video transcoding rate distortion prediction.

Effective collaboration among heterogeneous clients in a decentralized setting is a rather unexplored avenue in the literature. To structurally address this, we introduce Model Agnostic Peer-to-peer Learning (coined as MAPL) a novel approach to simultaneously learn heterogeneous personalized models as well as a collaboration graph through peer-to-peer communication among neighboring clients. MAPL is comprised of two main modules: (i) local-level Personalized Model Learning (PML), leveraging a combination of intra- and inter-client contrastive losses; (ii) network-wide decentralized Collaborative Graph Learning (CGL) dynamically refining collaboration weights in a privacy-preserving manner based on local task similarities. Our extensive experimentation demonstrates the efficacy of MAPL and its competitive (or, in most cases, superior) performance compared to its centralized model-agnostic counterparts, without relying on any central server. Our code is available and can be accessed here: https://github.com/SayakMukherjee/MAPL, Comment: Our code is available and can be accessed here: https://github.com/SayakMukherjee/MAPL

The line-of-sight (LOS) requirement of free-space optical (FSO) systems can be relaxed by employing optical intelligent reflecting surfaces (IRSs). In this paper, we model the impact of the IRS-induced delay dispersion and derive the channel impulse response (CIR) of IRS-assisted FSO links. The proposed model takes into account the characteristics of the incident and reflected beams' wavefronts, the position of transmitter and receiver, the size of the IRS, and the incident beamwidth on the IRS. Our simulation results reveal that a maximum effective delay spread of 0.7 ns is expected for a square IRS with area 1 $\mathrm{m}^2$, which induces inter-symbol interference for bit rates larger than 10 Gbps. We show that the IRS-induced delay dispersion can be mitigated via equalization at the receiver.

Future communication systems are envisioned to employ intelligent reflecting surfaces (IRSs) and the millimeter wave (mmWave) frequency band to provide reliable high-rate services. For mobile users, the time-varying channel state information (CSI) requires adequate adjustment of the reflection pattern of the IRS. We propose a novel codebook-based user tracking (UT) algorithm for IRS-assisted mmWave communication, allowing suitable reconfiguration of the IRS unit cell phase shifts, resulting in a high reflection gain. The presented algorithm acquires the direction information of the user based on a peak likelihood-based direction estimation. Using the direction information, the user's trajectory is extrapolated to proactively update the adopted codeword and adjust the IRS phase shift configuration accordingly. Furthermore, we conduct a theoretical analysis of the direction estimation error and utilize the obtained insights to design a codebook specifically optimized for direction estimation. Our numerical results reveal a lower direction estimation error of the proposed UT algorithm when employing our designed codebook compared to codebooks from the literature. Furthermore, the average achieved signal-to-noise ratio (SNR) as well as the average effective rate of the proposed UT algorithm are analyzed. The proposed UT algorithm requires only a low overhead for direction and channel estimation and avoids outdated IRS phase shifts. Furthermore, it is shown to outperform two benchmark schemes based on direct phase shift optimization and hierarchical codebook search, respectively, via computer simulations.

Multiple access techniques are cornerstones of wireless communications. Their performance depends on the channel properties, which can be improved by reconfigurable intelligent surfaces (RISs). In this work, we jointly optimize MA precoding at the base station (BS) and RIS configuration. We tackle difficulties of mutual coupling between RIS elements, scalability to more than 1000 RIS elements, and channel estimation. We first derive an RIS-assisted channel model considering mutual coupling, then propose an unsupervised machine learning (ML) approach to optimize the RIS. In particular, we design a dedicated neural network (NN) architecture RISnet with good scalability and desired symmetry. Moreover, we combine ML-enabled RIS configuration and analytical precoding at BS since there exist analytical precoding schemes. Furthermore, we propose another variant of RISnet, which requires the channel state information (CSI) of a small portion of RIS elements (in this work, 16 out of 1296 elements) if the channel comprises a few specular propagation paths. More generally, this work is an early contribution to combine ML technique and domain knowledge in communication for NN architecture design. Compared to generic ML, the problem-specific ML can achieve higher performance, lower complexity and symmetry., Comment: 13 pages, 16 figures

New strategy is presented to optimize the performance of Thermoelectric (TE) coolers. This approach breaks optimizing TE coolers free from traditional methods of controlling temperature or engineering materials and the structural properties of the junctions. We introduced a dimensionless figure, {\gamma}, that shows the ratio of the unavailable cooling capacity to the available cooling capacity. This parameter relates the TE coolers' coefficient of performance (COP) to the COP of the reversible cycle (second law of thermodynamics efficiency) for a given electrical current. The theoretical description of the model is presented, and it is shown that controlling {\gamma} during the TE performance minimizes entropy generation and energy loss, which leads to the maximum pumped heat. We validated this model against a designed TE cooler. In this cooler, contrary to conventional TE coolers, where the temperature of the cold space is generally controlled at a specific temperature, and the performance of the cooler overlooked, the entropy generation and heat loss are engineered, and the electrical current is tuned to minimize {\gamma} by the controller so that the TE cooler works near to its optimum performance at any time., Comment: 14 pages, 7 figures

Efficient beam training is the key challenge in the codebook-based configuration of reconfigurable intelligent surfaces (RISs) because the beam training overhead can have a strong impact on the achievable system performance. In this paper, we study the performance tradeoff between overhead and achievable signal-to-noise ratio (SNR) in RIS beam training while taking into account the size of the targeted coverage area, the RIS response time, and the delay for feedback transmissions. Thereby, we consider three common beam training strategies: full search (FS), hierarchical search (HS), and tracking-based search (TS). Our analysis shows that the codebook-based illumination of a given coverage area can be realized with wide- or narrow-beam designs, which result in two different scaling laws for the achievable SNR. Similarly, there are two regimes for the overhead, where the number of pilot symbols required for reliable beam training is dependent on and independent of the SNR, respectively. Based on these insights, we investigate the impact of the beam training overhead on the effective rate and provide an upper bound on the user velocity for which the overhead is negligible. Moreover, when the overhead is not negligible, we show that TS beam training achieves higher effective rates than HS and FS beam training, while HS beam training may or may not outperform FS beam training, depending on the RIS response time, feedback delay, and codebook size. Finally, we present numerical simulation results that verify our theoretical analysis. In particular, our results confirm the existence of the proposed regimes, reveal that fast RISs can lead to negligible overhead for FS beam training, and show that large feedback delays can significantly reduce the performance for HS beam training., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Mesoporous silica particles are promising candidates for drug delivery applications. In this paper, we first synthesize mesoporous silica MCM-41 and its derivative MCM-41GA with anchored glutaraldehyde bridges, and characterize them using a variety of techniques, including nitrogen adsorption/desorption, X-ray diffraction, NMR spectroscopy, scanning electron microscopy, and thermogravimetric analysis. Then, we employ random optical fields to sort mesoporous silica particles. Random optical fields by containing local intensity gradients throughout a wide range of field of view provide an elegant, easy-to-implement, and low-cost variant of multiple optical tweezers, which is known as speckle tweezers (ST). ST, similar to multiple optical tweezers, for manipulation tasks, such as trapping, sorting, and guiding of collection of micro and sub-micro objects in several disciplines including statistical physics, chemistry, microfluidics and material science. We show that ST can restrict, sieve, and sort MCM-41 and MCM-41GA particles. The different interaction of mesoporous silica variations with the applied ST may be attributed to the pre-applied modification and the differences in the porosity structure and distribution. Therefore, the results provide insight into the textural and chemical characteristics of mesoporous materials, contributing to a deeper understanding of their potential applications.

In today's world, where moving forward hinges on innovation and working together, this article introduces a new global online platform that is all about sparking teamwork to come up with new ideas. This platform goes beyond borders and barriers between different fields, creating an exciting space where people from all over the world can swap ideas, get helpful feedback, and team up on exciting projects. What sets our platform apart is its ability to tap into the combined brainpower of a diverse bunch of users, giving people the power to come up with game-changing ideas that tackle big global problems. By making it easy for people to share ideas and promoting a culture of working together, our platform is like a buddy for innovation, boosting creativity and problem-solving on a global level. This article spills the details on what the platform aims to do, how it works, and what makes it special, emphasizing how it can kickstart creativity, ramp up problem-solving skills, and get different fields collaborating. It is not just a tool it is a whole new way of teaming up to make daily life better and build a global community of problem-solving pals., Comment: 5 pages, 5 figures, ITNG 2024 21st International Conference on Information Technology. Las Vegas, Nevada, USA

Liquid crystal (LC) technology offers a cost-effective, scalable, energy-efficient, and continuous phase tunable realization of extremely large reconfigurable intelligent surfaces (RISs). However, LC response time to achieve a desired differential phase is significantly higher compared to competing silicon-based technologies (RF switches, PIN diodes, etc). The slow response time can be the performance bottleneck for applications where frequent reconfiguration of the RIS (e.g., to serve different users) is needed. In this paper, we develop an RIS phase-shift design that is aware of the transition behavior and aims to minimize the time to switch among multiple RIS configurations each serving a mobile user in a time-division multiple-access (TDMA) protocol. Our simulation results confirm that the proposed algorithm significantly reduces the time required for the users to achieve a threshold signal quality. This leads to a considerable improvement in the achievable throughput for applications, where the length of the TDMA time intervals is comparable with the RIS reconfiguration time.

Learning quickly from very few labeled samples is a fundamental attribute that separates machines and humans in the era of deep representation learning. Unsupervised few-shot learning (U-FSL) aspires to bridge this gap by discarding the reliance on annotations at training time. Intrigued by the success of contrastive learning approaches in the realm of U-FSL, we structurally approach their shortcomings in both pretraining and downstream inference stages. We propose a novel Dynamic Clustered mEmory (DyCE) module to promote a highly separable latent representation space for enhancing positive sampling at the pretraining phase and infusing implicit class-level insights into unsupervised contrastive learning. We then tackle the, somehow overlooked yet critical, issue of sample bias at the few-shot inference stage. We propose an iterative Optimal Transport-based distribution Alignment (OpTA) strategy and demonstrate that it efficiently addresses the problem, especially in low-shot scenarios where FSL approaches suffer the most from sample bias. We later on discuss that DyCE and OpTA are two intertwined pieces of a novel end-to-end approach (we coin as BECLR), constructively magnifying each other's impact. We then present a suite of extensive quantitative and qualitative experimentation to corroborate that BECLR sets a new state-of-the-art across ALL existing U-FSL benchmarks (to the best of our knowledge), and significantly outperforms the best of the current baselines (codebase available at: https://github.com/stypoumic/BECLR)., Comment: ICLR 2024 Spotlight Presentation

We propose to employ a recently developed IoT-based wireless technology, so called low-power wide-area networks (LPWANs), to exploit their long range, low power, and inherent compatibility to cloud storage and computing. We create a remotely-operated minimum-maintenance wireless solution for four major seismic applications of interest. By proposing appropriate network architecture and data coordination (aggregation and transmission) designs we show that neither the low data-rate nor the low duty-cycle of LPWANs impose fundamental issues in handling a considerable amount of data created by complex seismic scenarios as long as the application is delay-tolerant. In order to confirm this claim, we cast our ideas into a practical large-scale networking design for simultaneous seismic monitoring and interferometry and carry out an analysis on the data generation and transmission rates. Finally, we present some results from a small-scale field test in which we have employed our IoT-based wireless nodes for real-time seismic quality control (QC) over clouds.

In this chapter, we investigate the mathematical foundation of the modeling and design of reconfigurable intelligent surfaces (RIS) in both the far- and near-field regimes. More specifically, we first present RIS-assisted wireless channel models for the far- and near-field regimes, discussing relevant phenomena, such as line-of-sight (LOS) and non-LOS links, rich and poor scattering, channel correlation, and array manifold. Subsequently, we introduce two general approaches for the RIS reflective beam design, namely optimization-based and analytical, which offer different degrees of design flexibility and computational complexity. Furthermore, we provide a comprehensive set of simulation results for the performance evaluation of the studied RIS beam designs and the investigation of the impact of the system parameters.

The study focuses on NMC811, a promising material for high-capacity batteries, and investigates the challenges associated with its use, specifically the formation of the Cathode Electrolyte Interphase (CEI) layer due to chemical reactions. This layer is a consequence of the position of the LUMO energy level of NMC811 that is close to the HOMO level of liquid electrolyte, resulting in electrolyte oxidation and cathode surface alterations during charging. A stable CEI layer can mitigate further degradation by reducing the interaction between the reactive cathode material and the electrolyte. Our research analyzed the CEI layer on NMC811 using advanced techniques such as 4D-STEM ACOM and STEM- EDX, focusing on the effects of different charging voltages (4.3 V and 4.5 V). The findings revealed varying degrees of degradation and the formation of a fluorine-rich layer on the secondary particles. Detailed analysis showed the composition of this layer differed based on the voltage: only LiF at 4.5 V and a combination of LiF and LiOH at 4.3 V. Despite LiF's known stability as a CEI protective layer, our observations indicate it does not effectively prevent degradation in NMC811. The study concludes that impurities and unwanted chemical reactions leading to suboptimal CEI formation are inevitable. Therefore, future efforts should focus on developing protective strategies for NMC811, such as the use of specific additives or coatings., Comment: 21 pages, 8 figures

Data augmentation is crucial in training deep models, preventing them from overfitting to limited data. Recent advances in generative AI, e.g., diffusion models, have enabled more sophisticated augmentation techniques that produce data resembling natural images. We introduce GeNIe a novel augmentation method which leverages a latent diffusion model conditioned on a text prompt to merge contrasting data points (an image from the source category and a text prompt from the target category) to generate challenging samples. To achieve this, inspired by recent diffusion based image editing techniques, we limit the number of diffusion iterations to ensure the generated image retains low-level and background features from the source image while representing the target category, resulting in a hard negative sample for the source category. We further enhance the proposed approach by finding the appropriate noise level adaptively for each image (coined as GeNIe-Ada) leading to further performance improvement. Our extensive experiments, in both few-shot and long-tail distribution settings, demonstrate the effectiveness of our novel augmentation method and its superior performance over the prior art. Our code is available here: https://github.com/UCDvision/GeNIe, Comment: Our code is available https://github.com/UCDvision/GeNIe

Advances in high-throughput microscopy have enabled the rapid acquisition of large numbers of high-content microscopy images. Whether by deep learning or classical algorithms, image analysis pipelines then produce single-cell features. To process these single-cells for downstream applications, we present Pycytominer, a user-friendly, open-source python package that implements the bioinformatics steps, known as image-based profiling. We demonstrate Pycytominers usefulness in a machine learning project to predict nuisance compounds that cause undesirable cell injuries., Comment: We updated: Figures (e.g., remove panel from Figure 1) to increase clarity. Consolidated the introduction, results, and discussion into a single section. Added a new analysis to predict compounds that cause undesirable cell injuries. Added three tables including one to highlight image-based profiling software limitations. 14 pages, 2 main figures, 5 supplementary figures, 3 tables