Search

Spatio-Temporal Scene Graphs (STSGs) provide a concise and expressive representation of dynamic scenes by modelling objects and their evolving relationships over time. However, real-world visual relationships often exhibit a long-tailed distribution, causing existing methods for tasks like Video Scene Graph Generation (VidSGG) and Scene Graph Anticipation (SGA) to produce biased scene graphs. To this end, we propose ImparTail, a novel training framework that leverages curriculum learning and loss masking to mitigate bias in the generation and anticipation of spatio-temporal scene graphs. Our approach gradually decreases the dominance of the head relationship classes during training and focuses more on tail classes, leading to more balanced training. Furthermore, we introduce two new tasks, Robust Spatio-Temporal Scene Graph Generation and Robust Scene Graph Anticipation, designed to evaluate the robustness of STSG models against distribution shifts. Extensive experiments on the Action Genome dataset demonstrate that our framework significantly enhances the unbiased performance and robustness of STSG models compared to existing methods., Comment: Under review

To explain reported solar interfacial-evaporation rates from porous materials beyond an apparent 100% efficiency using the thermal evaporation mechanism, many publications hypothesize that intermediate water inside porous materials have a reduced latent heat. Key supporting evidence is that water-only surfaces have lower dark evaporation rates than porous evaporators, with the ratio of the two rates taken as the latent heat reduction. Through simulations and experiments, we present benchmark evaporation rates of water and show that reported differences in natural evaporation are likely due to experimental error from recessed evaporating surfaces rather than from reduced latent heat. A few millimeters recession of the evaporating surface can drop evaporation rates over 50% due to a stagnant air layer, suggesting that the comparative experiments are prone to error and the latent heat reduction hypothesis cannot be substantiated. Our results indicate that new mechanistic directions need to be pursued to understand superthermal evaporation., Comment: 49 pages, 5 main figures, 12 SI figures

Recent advances in generative modeling with diffusion processes (DPs) enabled breakthroughs in image synthesis. Despite impressive image quality, these models have various prompt compliance problems, including low recall in generating multiple objects, difficulty in generating text in images, and meeting constraints like object locations and pose. For fine-grained editing and manipulation, they also require fine-grained semantic or instance maps that are tedious to produce manually. While prompt compliance can be enhanced by addition of loss functions at inference, this is time consuming and does not scale to complex scenes. To overcome these limitations, this work introduces a new family of \textit{Factor Graph Diffusion Models} (FG-DMs) that models the joint distribution of images and conditioning variables, such as semantic, sketch, depth or normal maps via a factor graph decomposition. This joint structure has several advantages, including support for efficient sampling based prompt compliance schemes, which produce images of high object recall, semi-automated fine-grained editing, text-based editing of conditions with noise inversion, explainability at intermediate levels, ability to produce labeled datasets for the training of downstream models such as segmentation or depth, training with missing data, and continual learning where new conditioning variables can be added with minimal or no modifications to the existing structure. We propose an implementation of FG-DMs by adapting a pre-trained Stable Diffusion (SD) model to implement all FG-DM factors, using only COCO dataset, and show that it is effective in generating images with 15\% higher recall than SD while retaining its generalization ability. We introduce an attention distillation loss that encourages consistency among the attention maps of all factors, improving the fidelity of the generated conditions and image., Comment: Accepted to NeurIPS 2024 conference. Project Page: https://deepaksridhar.github.io/factorgraphdiffusion.github.io/

Navigating indoor environments presents significant challenges for visually impaired individuals due to complex layouts and the absence of GPS signals. This paper introduces a novel system that provides turn-by-turn navigation inside buildings using only a smartphone equipped with a camera, leveraging multimodal models, deep learning algorithms, and large language models (LLMs). The smartphone's camera captures real-time images of the surroundings, which are then sent to a nearby Raspberry Pi capable of running on-device LLM models, multimodal models, and deep learning algorithms to detect and recognize architectural features, signage, and obstacles. The interpreted visual data is then translated into natural language instructions by an LLM running on the Raspberry Pi, which is sent back to the user, offering intuitive and context-aware guidance via audio prompts. This solution requires minimal workload on the user's device, preventing it from being overloaded and offering compatibility with all types of devices, including those incapable of running AI models. This approach enables the client to not only run advanced models but also ensure that the training data and other information do not leave the building. Preliminary evaluations demonstrate the system's effectiveness in accurately guiding users through complex indoor spaces, highlighting its potential for widespread application

Evaluating the creativity of large language models (LLMs) in story writing is difficult because LLM-generated stories could seemingly look creative but be very similar to some existing stories in their huge and proprietary training corpus. To overcome this challenge, we introduce a novel benchmark dataset with varying levels of prompt specificity: CS4 ($\mathbf{C}$omparing the $\mathbf{S}$kill of $\mathbf{C}$reating $\mathbf{S}$tories by $\mathbf{C}$ontrolling the $\mathbf{S}$ynthesized $\mathbf{C}$onstraint $\mathbf{S}$pecificity). By increasing the number of requirements/constraints in the prompt, we can increase the prompt specificity and hinder LLMs from retelling high-quality narratives in their training data. Consequently, CS4 empowers us to indirectly measure the LLMs' creativity without human annotations. Our experiments on LLaMA, Gemma, and Mistral not only highlight the creativity challenges LLMs face when dealing with highly specific prompts but also reveal that different LLMs perform very differently under different numbers of constraints and achieve different balances between the model's instruction-following ability and narrative coherence. Additionally, our experiments on OLMo suggest that Learning from Human Feedback (LHF) can help LLMs select better stories from their training data but has limited influence in boosting LLMs' ability to produce creative stories that are unseen in the training corpora. The benchmark is released at https://github.com/anirudhlakkaraju/cs4_benchmark.

Smart focal-plane and in-chip image processing has emerged as a crucial technology for vision-enabled embedded systems with energy efficiency and privacy. However, the lack of special datasets providing examples of the data that these neuromorphic sensors compute to convey visual information has hindered the adoption of these promising technologies. Neuromorphic imager variants, including event-based sensors, produce various representations such as streams of pixel addresses representing time and locations of intensity changes in the focal plane, temporal-difference data, data sifted/thresholded by temporal differences, image data after applying spatial transformations, optical flow data, and/or statistical representations. To address the critical barrier to entry, we provide an annotated, temporal-threshold-based vision dataset specifically designed for face detection tasks derived from the same videos used for Aff-Wild2. By offering multiple threshold levels (e.g., 4, 8, 12, and 16), this dataset allows for comprehensive evaluation and optimization of state-of-the-art neural architectures under varying conditions and settings compared to traditional methods. The accompanying tool flow for generating event data from raw videos further enhances accessibility and usability. We anticipate that this resource will significantly support the development of robust vision systems based on smart sensors that can process based on temporal-difference thresholds, enabling more accurate and efficient object detection and localization and ultimately promoting the broader adoption of low-power, neuromorphic imaging technologies. To support further research, we publicly released the dataset at \url{https://dx.doi.org/10.21227/bw2e-dj78}., Comment: 8 pages

We introduce a new parallelized approach to photothermal microscopy that utilizes mid-infrared dual-comb lasers, enabling simultaneous measurements at hundreds of wavelengths. This technology allows for high-speed, label-free chemical identification with super-resolution infrared imaging, overcoming the limitations of slow, single-wavelength-laser methods.

Whisper to normal speech conversion is an active area of research. Various architectures based on generative adversarial networks have been proposed in the recent past. Especially, recent study shows that MaskCycleGAN, which is a mask guided, and cyclic consistency keeping, generative adversarial network, performs really well for voice conversion from spectrogram representations. In the current work we present a MaskCycleGAN approach for the conversion of whispered speech to normal speech. We find that tuning the mask parameters, and pre-processing the signal with a voice activity detector provides superior performance when compared to the existing approach. The wTIMIT dataset is used for evaluation. Objective metrics such as PESQ and G-Loss are used to evaluate the converted speech, along with subjective evaluation using mean opinion score. The results show that the proposed approach offers considerable benefits., Comment: submitted to TENCON 2024

Together with EuGa4 and EuAl4, EuAl2Ga2 belongs to the BaAl4 structure type with space group symmetry I4/mmm. EuAl2Ga2 develops an incommensurate charge density wave (CDW) at temperatures below TCDW = 51 K. On the basis of temperature dependent single-crystal X-ray diffraction (SXRD) data, the incommensurately modulated CDW crystal structure of EuAl2Ga2 is determined to possess orthorhombic superspace symmetry Immm(00g)s00. This symmetry is different from the orthorhombic Fmmm based symmetry of the CDW state of EuAl4. Nevertheless, both symmetries Immm(00g)s00 and Fmmm(00g)s00 lead to the same conclusion, that the CDW is supported by the layers of Al1 type atoms, while the Eu and Al2 or Ga atoms are not directly involved in CDW formation. The different symmetries of the CDW states of EuAl4 and EuAl2Ga2, as well as the observation of negative thermal expansion in the CDW state of EuAl2Ga2 might be explained by the effects of Ga substitution in the latter compound., Comment: 24 pages, 6 figures

Page Stream Segmentation (PSS) is an essential prerequisite for automated document processing at scale. However, research progress has been limited by the absence of realistic public benchmarks. This paper works towards addressing this gap by introducing TABME++, an enhanced benchmark featuring commercial Optical Character Recognition (OCR) annotations. We evaluate the performance of large language models (LLMs) on PSS, focusing on decoder-based models fine-tuned with parameter-efficient methods. Our results show that decoder-based LLMs outperform smaller multimodal encoders. Through a review of existing PSS research and datasets, we identify key challenges and advancements in the field. Our findings highlight the key importance of robust OCR, providing valuable insights for the development of more effective document processing systems.

This research tackles the challenge of speckle noise in Synthetic Aperture Radar (SAR) space data, a prevalent issue that hampers the clarity and utility of SAR images. The study presents a comparative analysis of six distinct speckle noise reduction techniques: Lee Filtering, Frost Filtering, Kuan Filtering, Gaussian Filtering, Median Filtering, and Bilateral Filtering. These methods, selected for their unique approaches to noise reduction and image preservation, were applied to SAR datasets sourced from the Alaska Satellite Facility (ASF). The performance of each technique was evaluated using a comprehensive set of metrics, including Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), Equivalent Number of Looks (ENL), and Speckle Suppression Index (SSI). The study concludes that both the Lee and Kuan Filters are effective, with the choice of filter depending on the specific application requirements for image quality and noise suppression. This work provides valuable insights into optimizing SAR image processing, with significant implications for remote sensing, environmental monitoring, and geological surveying.

Mid-infrared microscopy is an important tool for biological analyses, allowing a direct probe of molecular bonds in their low energy landscape. In addition to the label-free extraction of spectroscopic information, the application of broadband sources can provide a third dimension of chemical specificity. However, to enable widespread deployment, mid-infrared microscopy platforms need to be compact and robust while offering high speed, broad bandwidth and high signal-to-noise ratio (SNR). In this study, we experimentally showcase the integration of a broadband, high-repetition-rate dual-comb spectrometer (DCS) in the mid-infrared range with a scanning microscope. We employ a set of 1-GHz mid-infrared frequency combs, demonstrating their capability for high-speed and broadband hyperspectral imaging of polymers and ovarian tissue. The system covers 1000 $\mathrm{cm^{-1}}$ at $\mathrm{\nu_c=2941 \; cm^{-1}}$ with 12.86 kHz spectra acquisition rate and 5 $\mathrm{\mu m}$ spatial resolution. Taken together, our experiments and analysis elucidate the trade-off between bandwidth and speed in DCS as it relates to microscopy. This provides a roadmap for the future advancement and application of high-repetition-rate DCS hyperspectral imaging.

This paper explores the wave packet dynamics of a math-type q- deformed field interacting with atoms in a Kerr-type nonlinear medium. The primary focus is on the generation and dynamics of entanglement using the q- deformed field, with the quantification of entanglement accomplished through the von Neumann entropy. Two distinct initial q-deformed states, the q-deformed Fock state, and the q-deformed coherent state, are investigated. The entanglement dynamics reveal characteristics of periodic, quasi-periodic, and chaotic behavior. Non-deformed initial states display wave packet near revivals and fractional revivals in entanglement dynamics while introducing q-deformation eliminates these features. The q-deformation significantly influences wave packet revivals and fractional revivals, with even a slight introduction causing their disappearance. For large values of q, the entanglement dynamics exhibit a chaotic nature. In the case of a beam splitter-type interaction applied to the initial deformed Fock state, an optimal deformation parameter q is identified, leading to maximum entanglement exceeding the non-deformed scenario., Comment: 8 pages, 5 figures

Large language models (LLMs) have shown remarkable potential in various domains, but they often lack the ability to access and reason over domain-specific knowledge and tools. In this paper, we introduced CACTUS (Chemistry Agent Connecting Tool-Usage to Science), an LLM-based agent that integrates cheminformatics tools to enable advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama2-7b, and Mistral-7b, on a benchmark of thousands of chemistry questions. Our results demonstrate that CACTUS significantly outperforms baseline LLMs, with the Gemma-7b and Mistral-7b models achieving the highest accuracy regardless of the prompting strategy used. Moreover, we explore the impact of domain-specific prompting and hardware configurations on model performance, highlighting the importance of prompt engineering and the potential for deploying smaller models on consumer-grade hardware without significant loss in accuracy. By combining the cognitive capabilities of open-source LLMs with domain-specific tools, CACTUS can assist researchers in tasks such as molecular property prediction, similarity searching, and drug-likeness assessment. Furthermore, CACTUS represents a significant milestone in the field of cheminformatics, offering an adaptable tool for researchers engaged in chemistry and molecular discovery. By integrating the strengths of open-source LLMs with domain-specific tools, CACTUS has the potential to accelerate scientific advancement and unlock new frontiers in the exploration of novel, effective, and safe therapeutic candidates, catalysts, and materials. Moreover, CACTUS's ability to integrate with automated experimentation platforms and make data-driven decisions in real time opens up new possibilities for autonomous discovery.

Underwater images taken from autonomous underwater vehicles (AUV's) often suffer from low light, high turbidity, poor contrast, motion-blur and excessive light scattering and hence require image enhancement techniques for object recognition. Machine learning methods are being increasingly used for object recognition under such adverse conditions. These enhanced object recognition methods of images taken from AUV's has potential applications in underwater pipeline and optical fibre surveillance, ocean bed resource extraction, ocean floor mapping, underwater species exploration, etc. While the classical machine learning methods are very efficient in terms of accuracy, they require large datasets and high computational time for image classification. In the current work, we use quantum-classical hybrid machine learning methods for real-time under-water object recognition on-board an AUV for the first time. We use real-time motion-blurred and low-light images taken from an on-board camera of AUV built in-house and apply existing hybrid machine learning methods for object recognition. Our hybrid methods consist of quantum encoding and flattening of classical images using quantum circuits and sending them to classical neural networks for image classification. The results of hybrid methods carried out using Pennylane based quantum simulators both on GPU and using pre-trained models on an on-board NVIDIA GPU chipset are compared with results from corresponding classical machine learning methods. We observe that the hybrid quantum machine learning methods show an efficiency greater than 65\% and reduction in run-time by one-thirds and require 50\% smaller dataset sizes for training the models compared to classical machine learning methods. We hope that our work opens up further possibilities in quantum enhanced real-time computer vision in autonomous vehicles.

Several emerging microscopy imaging methods rely on complex interactions between the incident light and the sample. These include interferometry, spectroscopy, and nonlinear optics. Reconstructing a sample from the measured scattered field relies on fast and accurate optical models. Fast approaches like ray tracing and the Born approximation have limitations that are limited when working with high numerical apertures. This paper presents sCWatter, an open-source tool that utilizes coupled wave theory (CWT) to simulate and visualize the 3D electric field scattered by complex samples. The sample refractive index is specified on a volumetric grid, while the incident field is provided as a 2D image orthogonal to the optical path. We introduce connection equations between layers that significantly reduce the dimensionality of the CW linear system, enabling efficient parallel processing on consumer hardware. Further optimizations using Intel MKL and CUDA significantly accelerate both field simulation and visualization.

Sentiment analysis, a vital component in natural language processing, plays a crucial role in understanding the underlying emotions and opinions expressed in textual data. In this paper, we propose an innovative ensemble approach for sentiment analysis for finding fake reviews that amalgamate the predictive capabilities of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree classifiers. Our ensemble architecture strategically combines these diverse models to capitalize on their strengths while mitigating inherent weaknesses, thereby achieving superior accuracy and robustness in fake review prediction. By combining all the models of our classifiers, the predictive performance is boosted and it also fosters adaptability to varied linguistic patterns and nuances present in real-world datasets. The metrics accounted for on fake reviews demonstrate the efficacy and competitiveness of the proposed ensemble method against traditional single-model approaches. Our findings underscore the potential of ensemble techniques in advancing the state-of-the-art in finding fake reviews using hybrid algorithms, with implications for various applications in different social media and e-platforms to find the best reviews and neglect the fake ones, eliminating puffery and bluffs.

Recent advances in Neural Radiance Fields (NeRF) have demonstrated promising results in 3D scene representations, including 3D human representations. However, these representations often lack crucial information on the underlying human pose and structure, which is crucial for AR/VR applications and games. In this paper, we introduce a novel approach, termed GHNeRF, designed to address these limitations by learning 2D/3D joint locations of human subjects with NeRF representation. GHNeRF uses a pre-trained 2D encoder streamlined to extract essential human features from 2D images, which are then incorporated into the NeRF framework in order to encode human biomechanic features. This allows our network to simultaneously learn biomechanic features, such as joint locations, along with human geometry and texture. To assess the effectiveness of our method, we conduct a comprehensive comparison with state-of-the-art human NeRF techniques and joint estimation algorithms. Our results show that GHNeRF can achieve state-of-the-art results in near real-time.

Strongly correlated electron systems with a kagome lattice can host abundant exotic quantum states such as superconductivity and spin/charge density waves (CDW) due to the complicated interactions between different degrees of freedoms in the framework of a unique two-dimensional geometrically frustrated lattice structure. Recently, successive orders of A-type antiferromagnetism (AFM), $2\times2\times2$ CDW and canted double-cone AFM have been manifested upon cooling in magnetic kagome FeGe. However, the mechanism of the CDW order and its interaction with magnetism are presently enigmatic at best. Here we investigate the evolution of CDW order with temperature across the spin canting transition in FeGe by single-crystal x-ray diffraction. Refinements of its modulated structure are presented using the superspace approach. Interestingly, the superlattice reflections originating from CDW-induced long-range structural modulation become extremely weak after the system enters the canted AFM while a $2\times2$ CDW in the $ab$ plane persists as a long-range order demonstrated by strong electronic modulation in the d$I$/d$V$ map of scanning tunneling spectroscopy. We discovered a novel CDW order without long-range structural modulation in FeGe probably because of the competition between CDW and canted AFM in determining the underlying crystal structure. In addition, occupational modulations of Ge1 atoms located in the kagome plane and displacive modulations of all the atoms were extracted from the refinements, confirming the existence of Ge atom dimerization along the $c$ axis as the major distortion and indicating a dynamic transformation between different CDW domains., Comment: 22 pages, 6 figures. Comments on the manuscript are welcome

Charge density wave (CDW) systems are proposed to exhibit application potential for electronic and optoelectronic devices. Therefore, identifying new materials that exhibit a CDW state at room temperature is crucial for the development of CDW-based devices. Here, we present a non-layered tetragonal polymorph of Gd2Os3Si5, which exhibits a CDW state at room temperature. Gd2Os3Si5 crystallizes in the U2Mn3Si5-type tetragonal crystal structure with the space group P4/mnc. Single-crystal x-ray diffraction (SXRD) analysis shows that Gd2Os3Si5 possesses an incommensurately modulated structure with modulation wave vector q = (0.53, 0, 0), while the modulation reduces the symmetry to orthorhombic Cccm({\sigma}00)0s0. This differs in contrast to isostructural Sm2Ru3Ge5, where the modulated phase has been reported to possess the superspace symmetry Pm({\alpha} 0 {\gamma})0. However, reinvestigation of Sm2Ru3Ge5 suggests that its modulated crystal structure can alternatively be described by Cccm({\sigma}00)0s0, with modulations similar to Gd2Os3Si5. The magnetic susceptibility, \c{hi}(T), exhibits a maximum at low temperatures that indicates an antiferromagnetic transition at TN = 5.5 K. The \c{hi}(T) furthermore shows an anomaly at around 345 K, suggesting a CDW transition at TCDW = 345 K, that corroborates the result from high-temperature SXRD measurements. Interestingly, R2T3X5 compounds are known to crystallize either in the tetragonal Sc2Fe3Si5 type structure or in the orthorhombic U2Co3Si5 structure type. Not all of the compounds in the R2T3X5 series undergo CDW phase transitions. We find that R2T3X5 compounds will exhibit a CDW transition, if the condition : 0.526 < c/sqrt(ab) < 0.543 is satisfied. We suggest the wave vector-dependent electron-phonon coupling to be the dominant mechanism of CDW formation in the tetragonal polymorph of Gd2Os3Si5.

The stable and fair division of profits/costs is a central concern in economics. The core, which ensures stability, has long been the gold standard for profit/cost sharing in cooperative games. Shapley and Shubik([SS71])'s classic work on the assignment game revealed that core imputations can be disproportionately favoring certain agents. Recent work ([Vaz24]) gave leximin and leximax core imputations for this game, achieving better fairness properties. We explore these fairness notions for the cores of three cooperative games: the max-flow game, the minimum spanning tree (MST) game, and the bipartite $b$-matching game. For all three games we give examples to show that an arbitrary core imputation can be excessively unfair to certain agents. Leximin and leximax core imputations are natural extensions of the widely used max-min and min-max fairness notions. We show that finding such imputations in the core is NP-hard for the max-flow and MST games, and likely so for $b$-matching as well. To address this, we introduce the concept of Dual-Consistent Core (DCC) imputations, which are characterized by solutions to the dual linear programs. We give polynomial time algorithms for computing leximin and leximax DCC imputations for all three games. These games have numerous applications and these imputations will provide a more fair way of distributing profit among agents for them., Comment: 50 pages

We introduce a new trajectory optimization method for robotic grasping based on a point-cloud representation of robots and task spaces. In our method, robots are represented by 3D points on their link surfaces. The task space of a robot is represented by a point cloud that can be obtained from depth sensors. Using the point-cloud representation, goal reaching in grasping can be formulated as point matching, while collision avoidance can be efficiently achieved by querying the signed distance values of the robot points in the signed distance field of the scene points. Consequently, a constrained nonlinear optimization problem is formulated to solve the joint motion and grasp planning problem. The advantage of our method is that the point-cloud representation is general to be used with any robot in any environment. We demonstrate the effectiveness of our method by performing experiments on a tabletop scene and a shelf scene for grasping with a Fetch mobile manipulator and a Franka Panda arm. The project page is available at \url{https://irvlutd.github.io/GraspTrajOpt}, Comment: Published in IROS 2024

Spatio-temporal scene graphs represent interactions in a video by decomposing scenes into individual objects and their pair-wise temporal relationships. Long-term anticipation of the fine-grained pair-wise relationships between objects is a challenging problem. To this end, we introduce the task of Scene Graph Anticipation (SGA). We adapt state-of-the-art scene graph generation methods as baselines to anticipate future pair-wise relationships between objects and propose a novel approach SceneSayer. In SceneSayer, we leverage object-centric representations of relationships to reason about the observed video frames and model the evolution of relationships between objects. We take a continuous time perspective and model the latent dynamics of the evolution of object interactions using concepts of NeuralODE and NeuralSDE, respectively. We infer representations of future relationships by solving an Ordinary Differential Equation and a Stochastic Differential Equation, respectively. Extensive experimentation on the Action Genome dataset validates the efficacy of the proposed methods., Comment: ECCV 2024, Code: https://github.com/rohithpeddi/SceneSayer

The development of superconducting qubit technology has shown great potential for the construction of practical quantum computers. As the complexity of quantum processors continues to grow, the need for stringent fabrication tolerances becomes increasingly critical. Utilizing advanced industrial fabrication processes could facilitate the necessary level of fabrication control to support the continued scaling of quantum processors. However, these industrial processes are currently not optimized to produce high coherence devices, nor are they a priori compatible with the commonly used approaches to make superconducting qubits. In this work, we demonstrate for the first time superconducting transmon qubits manufactured in a 300 mm CMOS pilot line, using industrial fabrication methods, with resulting relaxation and coherence times already exceeding 100 microseconds. We show across-wafer, large-scale statistics studies of coherence, yield, variability, and aging that confirm the validity of our approach. The presented industry-scale fabrication process, using exclusively optical lithography and reactive ion etching, shows performance and yield similar to the conventional laboratory-style techniques utilizing metal lift-off, angled evaporation, and electron-beam writing. Moreover, it offers potential for further upscaling by including three-dimensional integration and additional process optimization using advanced metrology and judicious choice of processing parameters and splits. This result marks the advent of more reliable, large-scale, truly CMOS-compatible fabrication of superconducting quantum computing processors., Comment: main text: 7 pages, 4 figures. bibliography: 55 references. supplement: 8 sections, 8 figures, 2 tables

Ovarian cancer detection has traditionally relied on a multi-step process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology. However, this technology is slow. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude. Our method significantly accelerates data collection by capturing a combination of high-resolution and interleaved, lower-resolution infrared band images and applying computational techniques for data interpolation. We effectively minimize data collection requirements by leveraging sparse data acquisition and employing curvelet-based reconstruction algorithms. This method enables the reconstruction of high-quality, high-resolution images from undersampled datasets and achieving a 10X improvement in data acquisition time. We assessed the performance of our sparse imaging methodology using a variety of quantitative metrics, including mean squared error (MSE), structural similarity index (SSIM), and tissue subtype classification accuracies, employing both random forest and convolutional neural network (CNN) models, accompanied by ROC curves. Our statistically robust analysis, based on data from 100 ovarian cancer patient samples and over 65 million data points, demonstrates the method's capability to produce superior image quality and accurately distinguish between different gynecological tissue types with segmentation accuracy exceeding 95%.

We consider the task of constructing confidence intervals with differential privacy. We propose two private variants of the non-parametric bootstrap, which privately compute the median of the results of multiple "little" bootstraps run on partitions of the data and give asymptotic bounds on the coverage error of the resulting confidence intervals. For a fixed differential privacy parameter $\epsilon$, our methods enjoy the same error rates as that of the non-private bootstrap to within logarithmic factors in the sample size $n$. We empirically validate the performance of our methods for mean estimation, median estimation, and logistic regression with both real and synthetic data. Our methods achieve similar coverage accuracy to existing methods (and non-private baselines) while providing notably shorter ($\gtrsim 10$ times) confidence intervals than previous approaches., Comment: 45 pages

Electron spins in silicon quantum dots are excellent qubits due to their long coherence times, scalability, and compatibility with advanced semiconductor technology. Although high gate fidelities can be achieved with spin qubits, charge noise in the semiconductor environment still hinders further improvements. Despite the importance of charge noise, key questions about the specific nature of the fluctuators that cause charge noise remain unanswered. Here, we probe individual two-level fluctuators (TLFs) in Si/SiGe quantum dots through simple quantum-dot transport measurement and analyses based on the Allan variance and factorial hidden Markov modeling. We find that the TLF switching times depend sensitively on gate voltages, decrease with temperature, and depend on the current through a nearby quantum dot. A model for the data of the primary TLF we study indicates that it may be a bistable charge dipole near the plunger gate electrode, heated by current through the sensor dot, and experiencing state transitions driven not by direct electron-phonon coupling but through some other mechanism such as coupling to electrons passing through the sensor dot.

We study popularity for matchings under preferences. This solution concept captures matchings that do not lose against any other matching in a majority vote by the agents. A popular matching is said to be robust if it is popular among multiple instances. We present a polynomial-time algorithm for deciding whether there exists a robust popular matching if instances only differ with respect to the preferences of a single agent while obtaining NP-completeness if two instances differ only by a downward shift of one alternative by four agents. Moreover, we find a complexity dichotomy based on preference completeness for the case where instances differ by making some options unavailable., Comment: Appears in: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024)

Following step-by-step procedures is an essential component of various activities carried out by individuals in their daily lives. These procedures serve as a guiding framework that helps to achieve goals efficiently, whether it is assembling furniture or preparing a recipe. However, the complexity and duration of procedural activities inherently increase the likelihood of making errors. Understanding such procedural activities from a sequence of frames is a challenging task that demands an accurate interpretation of visual information and the ability to reason about the structure of the activity. To this end, we collect a new egocentric 4D dataset, CaptainCook4D, comprising 384 recordings (94.5 hours) of people performing recipes in real kitchen environments. This dataset consists of two distinct types of activity: one in which participants adhere to the provided recipe instructions and another in which they deviate and induce errors. We provide 5.3K step annotations and 10K fine-grained action annotations and benchmark the dataset for the following tasks: supervised error recognition, multistep localization, and procedure learning, Comment: Accepted to the 2024 Neural Information Processing Systems Datasets and Benchmarks Track, Project Page: https://captaincook4d.github.io/captain-cook/

Artificial intelligence (AI) stands out as a game-changer in today's technology landscape. However, the integration of AI education in classroom curricula currently lags behind, leaving teenagers inadequately prepared for an imminent AI-driven future. In this pilot study, we designed a three-day bootcamp offered in the summer of 2023 to a cohort of 60 high school students. The curriculum was delivered in person through animated video content, easy-to-follow slides, interactive playgrounds, and quizzes. These were packaged in the early version of an online learning platform we are developing. Results from the post-bootcamp survey conveyed a 91.4% overall satisfaction. Despite the short bootcamp duration, 88.5% and 71.4% of teenagers responded that they had an improved understanding of AI concepts and programming, respectively. Overall, we found that employing diverse modalities effectively engaged students, and building foundational modules proved beneficial for introducing more complex topics. Furthermore, using Google Colab notebooks for coding assignments proved challenging to most students. Students' activity on the platform and their answers to quizzes showed proficient engagement and a grasp of the material. Our results strongly highlight the need for compelling and accessible AI education methods for the next generation and the potential for informal learning to fill the gap of providing early AI education to teenagers., Comment: 7 pages, 12 figures, 1 table

Graph data structures are widely used to store relational information between several entities. With data being generated worldwide on a large scale, we see a significant growth in the generation of knowledge graphs. Thing in the future is Orange's take on a knowledge graph in the domain of the Web Of Things (WoT), where the main objective of the platform is to provide a digital representation of the physical world and enable cross-domain applications to be built upon this massive and highly connected graph of things. In this context, as the knowledge graph grows in size, it is prone to have noisy and messy data. In this paper, we explore state-of-the-art knowledge graph embedding (KGE) methods to learn numerical representations of the graph entities and, subsequently, explore downstream tasks like link prediction, node classification, and triple classification. We also investigate Graph neural networks (GNN) alongside KGEs and compare their performance on the same downstream tasks. Our evaluation highlights the encouraging performance of both KGE and GNN-based methods on node classification, and the superiority of GNN approaches in the link prediction task. Overall, we show that state-of-the-art approaches are relevant in a WoT context, and this preliminary work provides insights to implement and evaluate them in this context.

This report focuses on enhancing a binary code comment quality classification model by integrating generated code and comment pairs, to improve model accuracy. The dataset comprises 9048 pairs of code and comments written in the C programming language, each annotated as "Useful" or "Not Useful." Additionally, code and comment pairs are generated using a Large Language Model Architecture, and these generated pairs are labeled to indicate their utility. The outcome of this effort consists of two classification models: one utilizing the original dataset and another incorporating the augmented dataset with the newly generated code comment pairs and labels., Comment: 11 pages, 2 figures, 2 tables, Has been accepted for the Information Retrieval in Software Engineering track at Forum for Information Retrieval Evaluation 2023

Multi-agent reinforcement learning (MARL) plays a pivotal role in tackling real-world challenges. However, the seamless transition of trained policies from simulations to real-world requires it to be robust to various environmental uncertainties. Existing works focus on finding Nash Equilibrium or the optimal policy under uncertainty in one environment variable (i.e. action, state or reward). This is because a multi-agent system itself is highly complex and unstationary. However, in real-world situation uncertainty can occur in multiple environment variables simultaneously. This work is the first to formulate the generalised problem of robustness to multi-modal environment uncertainty in MARL. To this end, we propose a general robust training approach for multi-modal uncertainty based on curriculum learning techniques. We handle two distinct environmental uncertainty simultaneously and present extensive results across both cooperative and competitive MARL environments, demonstrating that our approach achieves state-of-the-art levels of robustness.

Automated Essay Score (AES) is proven to be one of the cutting-edge technologies. Scoring techniques are used for various purposes. Reliable scores are calculated based on influential variables. Such variables can be computed by different methods based on the domain. The research is concentrated on the user's understanding of a given topic. The analysis is based on a scoring index by using Large Language Models. The user can then compare and contrast the understanding of a topic that they recently learned. The results are then contributed towards learning analytics and progression is made for enhancing the learning ability. In this research, the focus is on summarizing a PDF document and gauging a user's understanding of its content. The process involves utilizing a Langchain tool to summarize the PDF and extract the essential information. By employing this technique, the research aims to determine how well the user comprehends the summarized content.

This work was completed in May 2022. For safe and reliable deployment of language models in the real world, testing needs to be robust. This robustness can be characterized by the difficulty and diversity of the test cases we evaluate these models on. Limitations in human-in-the-loop test case generation has prompted an advent of automated test case generation approaches. In particular, we focus on Red Teaming Language Models with Language Models by Perez et al.(2022). Our contributions include developing a pipeline for automated test case generation via red teaming that leverages publicly available smaller language models (LMs), experimenting with different target LMs and red classifiers, and generating a corpus of test cases that can help in eliciting offensive responses from widely deployed LMs and identifying their failure modes.

Sensitive personal information of individuals and non-personal information of organizations or communities often needs to be legitimately exchanged among different stakeholders, to provide services, maintain public health, law and order, and so on. While such exchanges are necessary, they also impose enormous privacy and security challenges. Data protection laws like GDPR for personal data and Indian Non-personal data protection draft specify conditions and the \textit{legal capacity} in which personal and non-personal information can be solicited and disseminated further. But there is a dearth of formalisms for specifying legal capacities and jurisdictional boundaries, so that open-ended exchange of such data can be implemented. This paper proposes an extensible framework for consent management in Data Trusts in which data can flow across a network through "role tunnels" established based on corresponding legal capacities., Comment: An earlier version of this paper was published in ISIC 2021

We study the effect of nanowire shape on mechanical properties and deformation behaviour of Cu nanowires using atomistic simulations. Simulations were carried out on $[100]$ nanowires with different shapes such as triangular, square, pentagon, hexagon and circular.Results indicate yield strength is different for different shapes. In both cases, triangular nanowire exhibit the lowest yield strength, while circular nanowire is the strongest. Deformation in all the nanowires is dominated by partials slip and twinning. Due to twinning, different shapes expose different surfaces at the twinned region. All nanowires show ductile failure and square nanowire exhibits the highest failure strain, while it is lowest for triangular nanowire., Comment: 14 pages, 10 Figures

At ambient conditions SrAl4 adopts the BaAl4 structure type with space group I4/mmm. It undergoes a charge-density-wave (CDW) transition at TCDW = 243 K, followed by a structural transition at TS = 87 K. Temperature-dependent single-crystal X-ray diffraction (SXRD) leads to the observation of incommensurate superlattice reflections at q = \sigma c* with \sigma = 0.1116 at 200 K. The CDW has orthorhombic symmetry with the acentric superspace group F222(00sigma)00s, where F222 is a subgroup of Fmmm as well as of I4/mmm. Atomic displacements mainly represent a transverse wave, with displacements that are 90 deg out of phase between the two diagonal directions of the I-centered unit cell, resulting in a helical wave. Small longitudinal displacements are provided by the second harmonic modulation. The orthorhombic phase realized in SrAl4 is similar to that found in EuAl4. Electronic structure calculations and phonon calculations by density functional theory (DFT) have failed to reveal the mechanism of CDW formation. However, DFT reveals that Al atoms dominate the density of states near the Fermi level, thus, corroborating the SXRD measurements. SrAl4 remains incommensurately modulated at the structural transition, where the symmetry lowers from orthorhombic to b-unique monoclinic. We have identified a simple criterion, that correlates the presence of a phase transition with the interatomic distances. Only those compounds XAl4-xGax(X = Ba, Eu, Sr, Ca; 0 < x <4) undergo phase transitions, for which the ratio c/a falls within the narrow range 2.51 < c/a < 2.54.