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Visual analytics (VA) requires analysts to iteratively propose analysis tasks based on observations and execute tasks by creating visualizations and interactive exploration to gain insights. This process demands skills in programming, data processing, and visualization tools, highlighting the need for a more intelligent, streamlined VA approach. Large language models (LLMs) have recently been developed as agents to handle various tasks with dynamic planning and tool-using capabilities, offering the potential to enhance the efficiency and versatility of VA. We propose LightVA, a lightweight VA framework that supports task decomposition, data analysis, and interactive exploration through human-agent collaboration. Our method is designed to help users progressively translate high-level analytical goals into low-level tasks, producing visualizations and deriving insights. Specifically, we introduce an LLM agent-based task planning and execution strategy, employing a recursive process involving a planner, executor, and controller. The planner is responsible for recommending and decomposing tasks, the executor handles task execution, including data analysis, visualization generation and multi-view composition, and the controller coordinates the interaction between the planner and executor. Building on the framework, we develop a system with a hybrid user interface that includes a task flow diagram for monitoring and managing the task planning process, a visualization panel for interactive data exploration, and a chat view for guiding the model through natural language instructions. We examine the effectiveness of our method through a usage scenario and an expert study.

In the last decade, the generalization and adaptation abilities of deep learning models were typically evaluated on fixed training and test distributions. Contrary to traditional deep learning, large language models (LLMs) are (i) even more overparameterized, (ii) trained on unlabeled text corpora curated from the Internet with minimal human intervention, and (iii) trained in an online fashion. These stark contrasts prevent researchers from transferring lessons learned on model generalization and adaptation in deep learning contexts to LLMs. To this end, our short paper introduces empirical observations that aim to shed light on further training of already pretrained language models. Specifically, we demonstrate that training a model on a text domain could degrade its perplexity on the test portion of the same domain. We observe with our subsequent analysis that the performance degradation is positively correlated with the similarity between the additional and the original pretraining dataset of the LLM. Our further token-level perplexity observations reveals that the perplexity degradation is due to a handful of tokens that are not informative about the domain. We hope these findings will guide us in determining when to adapt a model vs when to rely on its foundational capabilities., Comment: Accepted to EMNLP 2024 Main Conference

Electroadhesion has emerged as a viable technique for displaying tactile feedback on touch surfaces, particularly capacitive touchscreens found in smartphones and tablets. This involves applying a voltage signal to the conductive layer of the touchscreen to generate tactile sensations on the fingerpads of users. In our investigation, we explore the tactile perception of electroadhesion under DC and AC stimulations. Our tactile perception experiments with 10 participants demonstrate a significantly lower voltage detection threshold for AC signals compared to their DC counterparts. This discrepancy is elucidated by the underlying electro-mechanical interactions between the finger and the voltage-induced touchscreen and considering the response of mechanoreceptors in the fingerpad to electrostatic forces generated by electroadhesion. Additionally, our study highlights the impact of moisture on electroadhesive tactile perception. Participants with moist fingers exhibited markedly higher threshold levels. Our electrical impedance measurements show a substantial reduction in impedance magnitude when sweat is present at the finger-touchscreen interface, indicating increased conductivity. These findings not only contribute to our understanding of tactile perception under electroadhesion but also shed light on the underlying physics. In this regard, the results of this study extend beyond mobile devices to encompass other applications of this technology, including robotics, automation, space missions, and textiles.

Existing text-to-SQL benchmarks have largely been constructed using publicly available tables from the web with human-generated tests containing question and SQL statement pairs. They typically show very good results and lead people to think that LLMs are effective at text-to-SQL tasks. In this paper, we apply off-the-shelf LLMs to a benchmark containing enterprise data warehouse data. In this environment, LLMs perform poorly, even when standard prompt engineering and RAG techniques are utilized. As we will show, the reasons for poor performance are largely due to three characteristics: (1) public LLMs cannot train on enterprise data warehouses because they are largely in the "dark web", (2) schemas of enterprise tables are more complex than the schemas in public data, which leads the SQL-generation task innately harder, and (3) business-oriented questions are often more complex, requiring joins over multiple tables and aggregations. As a result, we propose a new dataset BEAVER, sourced from real enterprise data warehouses together with natural language queries and their correct SQL statements which we collected from actual user history. We evaluated this dataset using recent LLMs and demonstrated their poor performance on this task. We hope this dataset will facilitate future researchers building more sophisticated text-to-SQL systems which can do better on this important class of data.

Reinforcement Learning (RL) has experienced significant advancement over the past decade, prompting a growing interest in applications within finance. This survey critically evaluates 167 publications, exploring diverse RL applications and frameworks in finance. Financial markets, marked by their complexity, multi-agent nature, information asymmetry, and inherent randomness, serve as an intriguing test-bed for RL. Traditional finance offers certain solutions, and RL advances these with a more dynamic approach, incorporating machine learning methods, including transfer learning, meta-learning, and multi-agent solutions. This survey dissects key RL components through the lens of Quantitative Finance. We uncover emerging themes, propose areas for future research, and critique the strengths and weaknesses of existing methods., Comment: This work is currently submitted to and under-review for ACM Computing Surveys. This copy is an unedited, pre-print version and it is the author's version of the work. I

Unidirectional imagers form images of input objects only in one direction, e.g., from field-of-view (FOV) A to FOV B, while blocking the image formation in the reverse direction, from FOV B to FOV A. Here, we report unidirectional imaging under spatially partially coherent light and demonstrate high-quality imaging only in the forward direction (A->B) with high power efficiency while distorting the image formation in the backward direction (B->A) along with low power efficiency. Our reciprocal design features a set of spatially engineered linear diffractive layers that are statistically optimized for partially coherent illumination with a given phase correlation length. Our analyses reveal that when illuminated by a partially coherent beam with a correlation length of ~1.5 w or larger, where w is the wavelength of light, diffractive unidirectional imagers achieve robust performance, exhibiting asymmetric imaging performance between the forward and backward directions - as desired. A partially coherent unidirectional imager designed with a smaller correlation length of less than 1.5 w still supports unidirectional image transmission, but with a reduced figure of merit. These partially coherent diffractive unidirectional imagers are compact (axially spanning less than 75 w), polarization-independent, and compatible with various types of illumination sources, making them well-suited for applications in asymmetric visual information processing and communication., Comment: 25 Pages, 8 Figures

Data discovery is crucial for data management and analysis and can benefit from better utilization of metadata. For example, users may want to search data using queries like ``find the tables created by Alex and endorsed by Mike that contain sales numbers.'' They may also want to see how the data they view relates to other data, its lineage, or the quality and compliance of its upstream datasets, all metadata. Yet, effectively surfacing metadata through interactive user interfaces (UIs) to augment data discovery poses challenges. Constantly revamping UIs with each update to metadata sources (or providers) consumes significant development resources and lacks scalability and extensibility. In response, we introduce Humboldt, a new framework enabling interactive data systems to effectively leverage metadata for data discovery and rapidly evolve their UIs to support metadata changes. Humboldt decouples metadata sources from the implementation of data discovery UIs that support search and dataset visualization using metadata fields. It automatically generates interactive data discovery interfaces from declarative specifications, avoiding costly metadata-specific (re)implementations., Comment: TaDA Workshop at VLDB 2024

This workshop will grow and consolidate a community of interdisciplinary CSCW researchers focusing on the topic of contestable AI. As an outcome of the workshop, we will synthesize the most pressing opportunities and challenges for contestability along AI value chains in the form of a research roadmap. This roadmap will help shape and inspire imminent work in this field. Considering the length and depth of AI value chains, it will especially spur discussions around the contestability of AI systems along various sites of such chains. The workshop will serve as a platform for dialogue and demonstrations of concrete, successful, and unsuccessful examples of AI systems that (could or should) have been contested, to identify requirements, obstacles, and opportunities for designing and deploying contestable AI in various contexts. This will be held primarily as an in-person workshop, with some hybrid accommodation. The day will consist of individual presentations and group activities to stimulate ideation and inspire broad reflections on the field of contestable AI. Our aim is to facilitate interdisciplinary dialogue by bringing together researchers, practitioners, and stakeholders to foster the design and deployment of contestable AI., Comment: 5 pages, 0 figure, to be held as a workshop at CSCW'24

Large language models (LLMs) know little about enterprise database tables in the private data ecosystem, which substantially differ from web text in structure and content. As LLMs' performance is tied to their training data, a crucial question is how useful they can be in improving enterprise database management and analysis tasks. To address this, we contribute experimental results on LLMs' performance for text-to-SQL and semantic column-type detection tasks on enterprise datasets. The performance of LLMs on enterprise data is significantly lower than on benchmark datasets commonly used. Informed by our findings and feedback from industry practitioners, we identify three fundamental challenges -- latency, cost, and quality -- and propose potential solutions to use LLMs in enterprise data workflows effectively., Comment: Poster at North East Database Day 2024

This paper proposes an adaptive admittance controller for improving efficiency and safety in physical human-robot interaction (pHRI) tasks in small-batch manufacturing that involve contact with stiff environments, such as drilling, polishing, cutting, etc. We aim to minimize human effort and task completion time while maximizing precision and stability during the contact of the machine tool attached to the robot's end-effector with the workpiece. To this end, a two-layered learning-based human intention recognition mechanism is proposed, utilizing only the kinematic and kinetic data from the robot and two force sensors. A ``subtask detector" recognizes the human intent by estimating which phase of the task is being performed, e.g., \textit{Idle}, \textit{Tool-Attachment}, \textit{Driving}, and \textit{Contact}. Simultaneously, a ``motion estimator" continuously quantifies intent more precisely during the \textit{Driving} to predict when \textit{Contact} will begin. The controller is adapted online according to the subtask while allowing early adaptation before the \textit{Contact} to maximize precision and safety and prevent potential instabilities. Three sets of pHRI experiments were performed with multiple subjects under various conditions. Spring compression experiments were performed in virtual environments to train the data-driven models and validate the proposed adaptive system, and drilling experiments were performed in the physical world to test the proposed methods' efficacy in real-life scenarios. Experimental results show subtask classification accuracy of 84\% and motion estimation R\textsuperscript{2} score of 0.96. Furthermore, 57\% lower human effort was achieved during \textit{Driving} as well as 53\% lower oscillation amplitude at \textit{Contact} as a result of the proposed system.

Gram staining has been one of the most frequently used staining protocols in microbiology for over a century, utilized across various fields, including diagnostics, food safety, and environmental monitoring. Its manual procedures make it vulnerable to staining errors and artifacts due to, e.g., operator inexperience and chemical variations. Here, we introduce virtual Gram staining of label-free bacteria using a trained deep neural network that digitally transforms darkfield images of unstained bacteria into their Gram-stained equivalents matching brightfield image contrast. After a one-time training effort, the virtual Gram staining model processes an axial stack of darkfield microscopy images of label-free bacteria (never seen before) to rapidly generate Gram staining, bypassing several chemical steps involved in the conventional staining process. We demonstrated the success of the virtual Gram staining workflow on label-free bacteria samples containing Escherichia coli and Listeria innocua by quantifying the staining accuracy of the virtual Gram staining model and comparing the chromatic and morphological features of the virtually stained bacteria against their chemically stained counterparts. This virtual bacteria staining framework effectively bypasses the traditional Gram staining protocol and its challenges, including stain standardization, operator errors, and sensitivity to chemical variations., Comment: 25 Pages, 5 Figures

Drone detection is a challenging object detection task where visibility conditions and quality of the images may be unfavorable, and detections might become difficult due to complex backgrounds, small visible objects, and hard to distinguish objects. Both provide high confidence for drone detections, and eliminating false detections requires efficient algorithms and approaches. Our previous work, which uses YOLOv5, uses both real and synthetic data and a Kalman-based tracker to track the detections and increase their confidence using temporal information. Our current work improves on the previous approach by combining several improvements. We used a more diverse dataset combining multiple sources and combined with synthetic samples chosen from a large synthetic dataset based on the error analysis of the base model. Also, to obtain more resilient confidence scores for objects, we introduced a classification component that discriminates whether the object is a drone or not. Finally, we developed a more advanced scoring algorithm for object tracking that we use to adjust localization confidence. Furthermore, the proposed technique won 1st Place in the Drone vs. Bird Challenge (Workshop on Small-Drone Surveillance, Detection and Counteraction Techniques at ICIAP 2021).

This work aims to improve generalization and interpretability of dynamical systems by recovering the underlying lower-dimensional latent states and their time evolutions. Previous work on disentangled representation learning within the realm of dynamical systems focused on the latent states, possibly with linear transition approximations. As such, they cannot identify nonlinear transition dynamics, and hence fail to reliably predict complex future behavior. Inspired by the advances in nonlinear ICA, we propose a state-space modeling framework in which we can identify not just the latent states but also the unknown transition function that maps the past states to the present. We introduce a practical algorithm based on variational auto-encoders and empirically demonstrate in realistic synthetic settings that we can (i) recover latent state dynamics with high accuracy, (ii) correspondingly achieve high future prediction accuracy, and (iii) adapt fast to new environments.

Music has been identified as a promising medium to enhance the accessibility and experience of visual art for people who are blind or have low vision (BLV). However, composing music and designing soundscapes for visual art is a time-consuming, resource intensive process - limiting its scalability for large exhibitions. In this paper, we investigate the use of automated soundscapes to increase the accessibility of visual art. We built a prototype system and ran a qualitative study to evaluate the aesthetic experience provided by the automated soundscapes with 10 BLV participants. From the study, we identified a set of design considerations that reveal requirements from BLV people for the development of automated soundscape systems, setting new directions in which creative systems could enrich the aesthetic experience conveyed by these., Comment: Paper accepted into ICCC 24

We present the Euler-Lagrange and Hamilton's equations for a system whose configuration space is a unified product Lie group $G=M\bowtie_{\gamma} H$, for some $\gamma:M\times M \to H$. By reduction, then, we obtain the Euler-Lagrange type and Hamilton's type equations of the same form for the quotient space $M\cong G/H$, although it is not necessarily a Lie group. We observe, through further reduction, that it is possible to formulate the Euler-Poincar\'{e} type and Lie-Poisson type equations on the corresponding quotient $\mathfrak{m}\cong \mathfrak{g}/\mathfrak{h}$ of Lie algebras, which is not a priori a Lie algebra. Moreover, we realize the $n$th order iterated tangent group $T^{(n)}G$ of a Lie group $G$ as an extension of the $n$th order tangent group $T^nG$ of the same type. More precisely, $\mathfrak{g}$ being the Lie algebra of $G$, $T^{(n)}G \cong \mathfrak{g}^{\times \,2^n-1-n} \bowtie_\gamma T^nG$ for some $\gamma:\mathfrak{g}^{\times \,2^n-1-n} \times \mathfrak{g}^{\times \,2^n-1-n} \to T^nG$. We thus obtain the $n$th order Euler-Lagrange (and then the $n$th order Euler-Poincar\'e) equations over $T^nG$ by reduction from those on $T(T^{n-1}G)$. Finally, we illustrate our results in the realm of the Kepler problem, and the non-linear tokamak plasma dynamics.

The integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging. A compelling trend in this field involves deliberately compromising certain measurement metrics to engineer better bioimaging tools in terms of cost, speed, and form-factor, followed by compensating for the resulting defects through the utilization of deep learning models trained on a large amount of ideal, superior or alternative data. This strategic approach has found increasing popularity due to its potential to enhance various aspects of biophotonic imaging. One of the primary motivations for employing this strategy is the pursuit of higher temporal resolution or increased imaging speed, critical for capturing fine dynamic biological processes. This approach also offers the prospect of simplifying hardware requirements/complexities, thereby making advanced imaging standards more accessible in terms of cost and/or size. This article provides an in-depth review of the diverse measurement aspects that researchers intentionally impair in their biophotonic setups, including the point spread function, signal-to-noise ratio, sampling density, and pixel resolution. By deliberately compromising these metrics, researchers aim to not only recuperate them through the application of deep learning networks, but also bolster in return other crucial parameters, such as the field-of-view, depth-of-field, and space-bandwidth product. Here, we discuss various biophotonic methods that have successfully employed this strategic approach. These techniques span broad applications and showcase the versatility and effectiveness of deep learning in the context of compromised biophotonic data. Finally, by offering our perspectives on the future possibilities of this rapidly evolving concept, we hope to motivate our readers to explore novel ways of balancing hardware compromises with compensation via AI., Comment: 17 Pages, 4 Figures, 1 Table

Visual analytics supports data analysis tasks within complex domain problems. However, due to the richness of data types, visual designs, and interaction designs, users need to recall and process a significant amount of information when they visually analyze data. These challenges emphasize the need for more intelligent visual analytics methods. Large language models have demonstrated the ability to interpret various forms of textual data, offering the potential to facilitate intelligent support for visual analytics. We propose LEVA, a framework that uses large language models to enhance users' VA workflows at multiple stages: onboarding, exploration, and summarization. To support onboarding, we use large language models to interpret visualization designs and view relationships based on system specifications. For exploration, we use large language models to recommend insights based on the analysis of system status and data to facilitate mixed-initiative exploration. For summarization, we present a selective reporting strategy to retrace analysis history through a stream visualization and generate insight reports with the help of large language models. We demonstrate how LEVA can be integrated into existing visual analytics systems. Two usage scenarios and a user study suggest that LEVA effectively aids users in conducting visual analytics., Comment: Accepted to IEEE TVCG 2024

This paper studies the evolving domain of Continual Learning (CL) in large language models (LLMs), with a focus on developing strategies for efficient and sustainable training. Our primary emphasis is on continual domain-adaptive pretraining, a process designed to equip LLMs with the ability to integrate new information from various domains while retaining previously learned knowledge and enhancing cross-domain knowledge transfer without relying on domain-specific identification. Unlike previous studies, which mostly concentrate on a limited selection of tasks or domains and primarily aim to address the issue of forgetting, our research evaluates the adaptability and capabilities of LLMs to changing data landscapes in practical scenarios. To this end, we introduce a new benchmark designed to measure the adaptability of LLMs to these evolving data environments, offering a comprehensive framework for evaluation. We examine the impact of model size on learning efficacy and forgetting, as well as how the progression and similarity of emerging domains affect the knowledge transfer within these models. Our findings uncover several key insights: (i) when the sequence of domains shows semantic similarity, continual pretraining enables LLMs to better specialize in the current domain compared to stand-alone fine-tuning, (ii) training across a diverse range of domains enhances both backward and forward knowledge transfer, and (iii) smaller models are particularly sensitive to continual pretraining, showing the most significant rates of both forgetting and learning. We posit that our research marks a shift towards establishing a more realistic benchmark for investigating CL in LLMs, and has the potential to play a key role in guiding the direction of future research in the field.

Phase imaging is widely used in biomedical imaging, sensing, and material characterization, among other fields. However, direct imaging of phase objects with subwavelength resolution remains a challenge. Here, we demonstrate subwavelength imaging of phase and amplitude objects based on all-optical diffractive encoding and decoding. To resolve subwavelength features of an object, the diffractive imager uses a thin, high-index solid-immersion layer to transmit high-frequency information of the object to a spatially-optimized diffractive encoder, which converts/encodes high-frequency information of the input into low-frequency spatial modes for transmission through air. The subsequent diffractive decoder layers (in air) are jointly designed with the encoder using deep-learning-based optimization, and communicate with the encoder layer to create magnified images of input objects at its output, revealing subwavelength features that would otherwise be washed away due to diffraction limit. We demonstrate that this all-optical collaboration between a diffractive solid-immersion encoder and the following decoder layers in air can resolve subwavelength phase and amplitude features of input objects in a highly compact design. To experimentally demonstrate its proof-of-concept, we used terahertz radiation and developed a fabrication method for creating monolithic multi-layer diffractive processors. Through these monolithically fabricated diffractive encoder-decoder pairs, we demonstrated phase-to-intensity transformations and all-optically reconstructed subwavelength phase features of input objects by directly transforming them into magnified intensity features at the output. This solid-immersion-based diffractive imager, with its compact and cost-effective design, can find wide-ranging applications in bioimaging, endoscopy, sensing and materials characterization., Comment: 32 Pages, 9 Figures

The ability of machine learning systems to learn continually is hindered by catastrophic forgetting, the tendency of neural networks to overwrite previously acquired knowledge when learning a new task. Existing methods mitigate this problem through regularization, parameter isolation, or rehearsal, but they are typically evaluated on benchmarks comprising only a handful of tasks. In contrast, humans are able to learn over long time horizons in dynamic, open-world environments, effortlessly memorizing unfamiliar objects and reliably recognizing them under various transformations. To make progress towards closing this gap, we introduce Infinite dSprites, a parsimonious tool for creating continual classification and disentanglement benchmarks of arbitrary length and with full control over generative factors. We show that over a sufficiently long time horizon, the performance of all major types of continual learning methods deteriorates on this simple benchmark. This result highlights an important and previously overlooked aspect of continual learning: given a finite modelling capacity and an arbitrarily long learning horizon, efficient learning requires memorizing class-specific information and accumulating knowledge about general mechanisms. In a simple setting with direct supervision on the generative factors, we show how learning class-agnostic transformations offers a way to circumvent catastrophic forgetting and improve classification accuracy over time. Our approach sets the stage for continual learning over hundreds of tasks with explicit control over memorization and forgetting, emphasizing open-set classification and one-shot generalization., Comment: 10 pages, 10 figures

This paper presents a comparative analysis of existing nudity classification techniques for classifying images based on the presence of nudity, with a focus on their application in content moderation. The evaluation focuses on CNN-based models, vision transformer, and popular open-source safety checkers from Stable Diffusion and Large-scale Artificial Intelligence Open Network (LAION). The study identifies the limitations of current evaluation datasets and highlights the need for more diverse and challenging datasets. The paper discusses the potential implications of these findings for developing more accurate and effective image classification systems on online platforms. Overall, the study emphasizes the importance of continually improving image classification models to ensure the safety and well-being of platform users. The project page, including the demonstrations and results is publicly available at https://github.com/fcakyon/content-moderation-deep-learning., Comment: Published at ICASSP 2023

Aging, as defined in terms of the slope of the probability of death versus time (hazard curve), is a generic phenomenon observed in nearly all complex systems. Theoretical models of aging predict hazard curves that monotonically increase in time, in discrepancy with the peculiar ups and downs observed in empirically. Here we introduce the concept of co-aging, where the demographic trajectories of multiple cohorts couple together, and show that co-aging dynamics can account for the anomalous hazard curves exhibited by some species. In our model, multiple interdependency networks inflict damage on one other proportional to their number of functional nodes. We then fit our model predictions to three datasets describing (1) co-aging worm-pathogen populations (2) competing tree species. Lastly, we gather the mortality statistics of (3) machine-against-machine chess games to demonstrate that co-aging dynamics is not exclusive to biological systems.

We aim to explore the properties of the Be/X-ray binary system MXB 0656-072 from a timing analysis perspective through an investigation of the RXTE/PCA and Fermi/GBM data during its 2007-2008 type I outbursts. We applied two new techniques, for the first time, along with the conventional Deeter method to produce higher-resolution power density spectra (PDS) of the torque fluctuations. We also investigated the spin frequency evolution of the source by utilising a pulse timing technique. The PDSs show a red noise pattern, with a steepness of $\Gamma \sim -2$ and a saturation timescale of $\sim$150 d, indicating that MXB 0656-072 is a disc-fed source. With the obtained long term spin frequency evolution, we reveal the torque-luminosity correlation of MXB 0656-072 for the first time. We also demonstrate that the frequency evolution is largely consistent with the Ghosh-Lamb model. In the RXTE/PCA observations, the pulsed emission disappears below $\sim$5$\times 10^{35}$ erg s$^{-1}$, while the profiles remain stable above this value in our analysis time frame. We show that the magnetic field strength deduced from the torque model is compatible with the field strength of the pulsar derived from the cyclotron resonance scattering feature. Utilising the new distance of MXB 0656-072 measured by Gaia, we show that the spectral transition of MXB 0656-072 occurs at a luminosity that matches the expected theoretical transition from the subcritical to supercritical accretion regime., Comment: 7 pages, 5 figures. Accepted for publication in A&A

Understanding the semantics of relational tables is instrumental for automation in data exploration and preparation systems. A key source for understanding a table is the semantics of its columns. With the rise of deep learning, learned table representations are now available, which can be applied for semantic type detection and achieve good performance on benchmarks. Nevertheless, we observe a gap between this performance and its applicability in practice. In this paper, we propose AdaTyper to address one of the most critical deployment challenges: adaptation. AdaTyper uses weak-supervision to adapt a hybrid type predictor towards new semantic types and shifted data distributions at inference time, using minimal human feedback. The hybrid type predictor of AdaTyper combines rule-based methods and a light machine learning model for semantic column type detection. We evaluate the adaptation performance of AdaTyper on real-world database tables hand-annotated with semantic column types through crowdsourcing and find that the f1-score improves for new and existing types. AdaTyper approaches an average precision of 0.6 after only seeing 5 examples, significantly outperforming existing adaptation methods based on human-provided regular expressions or dictionaries., Comment: Submitted to VLDB'24

We introduce a novel motion estimation method, MaskFlow, that is capable of estimating accurate motion fields, even in very challenging cases with small objects, large displacements and drastic appearance changes. In addition to lower-level features, that are used in other Deep Neural Network (DNN)-based motion estimation methods, MaskFlow draws from object-level features and segmentations. These features and segmentations are used to approximate the objects' translation motion field. We propose a novel and effective way of incorporating the incomplete translation motion field into a subsequent motion estimation network for refinement and completion. We also produced a new challenging synthetic dataset with motion field ground truth, and also provide extra ground truth for the object-instance matchings and corresponding segmentation masks. We demonstrate that MaskFlow outperforms state of the art methods when evaluated on our new challenging dataset, whilst still producing comparable results on the popular FlyingThings3D benchmark dataset.

Monitoring the electrical pulse of fetal heart through a non-invasive fetal electrocardiogram (fECG) can easily detect abnormalities in the developing heart to significantly reduce the infant mortality rate and post-natal complications. Due to the overlapping of maternal and fetal R-peaks, the low amplitude of the fECG, systematic and ambient noises, typical signal extraction methods, such as adaptive filters, independent component analysis, empirical mode decomposition, etc., are unable to produce satisfactory fECG. While some techniques can produce accurate QRS waves, they often ignore other important aspects of the ECG. Our approach, which is based on 1D CycleGAN, can reconstruct the fECG signal from the mECG signal while maintaining the morphology due to extensive preprocessing and appropriate framework. The performance of our solution was evaluated by combining two available datasets from Physionet, "Abdominal and Direct Fetal ECG Database" and "Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations", where it achieved an average PCC and Spectral-Correlation score of 88.4% and 89.4%, respectively. It detects the fQRS of the signal with accuracy, precision, recall and F1 score of 92.6%, 97.6%, 94.8% and 96.4%, respectively. It can also accurately produce the estimation of fetal heart rate and R-R interval with an error of 0.25% and 0.27%, respectively. The main contribution of our work is that, unlike similar studies, it can retain the morphology of the ECG signal with high fidelity. The accuracy of our solution for fetal heart rate and R-R interval length is comparable to existing state-of-the-art techniques. This makes it a highly effective tool for early diagnosis of fetal heart diseases and regular health checkups of the fetus., Comment: 24 pages, 11 figures

Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images - implemented at the speed of light propagation within a thin diffractive visual processor. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30-40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays., Comment: 21 Pages, 7 Figures

Advancing human induced pluripotent stem cell derived cardiomyocyte (hiPSC-CM) technology will lead to significant progress ranging from disease modeling, to drug discovery, to regenerative tissue engineering. Yet, alongside these potential opportunities comes a critical challenge: attaining mature hiPSC-CM tissues. At present, there are multiple techniques to promote maturity of hiPSC-CMs including physical platforms and cell culture protocols. However, when it comes to making quantitative comparisons of functional behavior, there are limited options for reliably and reproducibly computing functional metrics that are suitable for direct cross-system comparison. In addition, the current standard functional metrics obtained from time-lapse images of cardiac microbundle contraction reported in the field (i.e., post forces, average tissue stress) do not take full advantage of the available information present in these data (i.e., full-field tissue displacements and strains). Thus, we present "MicroBundleCompute," a computational framework for automatic quantification of morphology-based mechanical metrics from movies of cardiac microbundles. Briefly, this computational framework offers tools for automatic tissue segmentation, tracking, and analysis of brightfield and phase contrast movies of beating cardiac microbundles. It is straightforward to implement, requires little to no parameter tuning, and runs quickly on a personal computer. In this paper, we describe the methods underlying this computational framework, show the results of our extensive validation studies, and demonstrate the utility of exploring heterogeneous tissue deformations and strains as functional metrics. With this manuscript, we disseminate "MicroBundleCompute" as an open-source computational tool with the aim of making automated quantitative analysis of beating cardiac microbundles more accessible to the community., Comment: 16 main pages, 7 main figures, Supplementary Information included as appendices

This paper introduces a multilingual automatic speech recognizer (ASR) for maritime radio communi-cation that automatically converts received VHF radio signals into text. The challenges of maritime radio communication are described at first, and the deep learning architecture of marFM consisting of audio processing techniques and machine learning algorithms is presented. Subsequently, maritime radio data of interest is analyzed and then used to evaluate the transcription performance of our ASR model for various maritime radio data.

Many data extraction tasks of practical relevance require not only syntactic pattern matching but also semantic reasoning about the content of the underlying text. While regular expressions are very well suited for tasks that require only syntactic pattern matching, they fall short for data extraction tasks that involve both a syntactic and semantic component. To address this issue, we introduce semantic regexes, a generalization of regular expressions that facilitates combined syntactic and semantic reasoning about textual data. We also propose a novel learning algorithm that can synthesize semantic regexes from a small number of positive and negative examples. Our proposed learning algorithm uses a combination of neural sketch generation and compositional type-directed synthesis for fast and effective generalization from a small number of examples. We have implemented these ideas in a new tool called Smore and evaluated it on representative data extraction tasks involving several textual datasets. Our evaluation shows that semantic regexes can better support complex data extraction tasks than standard regular expressions and that our learning algorithm significantly outperforms existing tools, including state-of-the-art neural networks and program synthesis tools.

Free-space optical systems are emerging for high data rate communication and transfer of information in indoor and outdoor settings. However, free-space optical communication becomes challenging when an occlusion blocks the light path. Here, we demonstrate, for the first time, a direct communication scheme, passing optical information around a fully opaque, arbitrarily shaped obstacle that partially or entirely occludes the transmitter's field-of-view. In this scheme, an electronic neural network encoder and a diffractive optical network decoder are jointly trained using deep learning to transfer the optical information or message of interest around the opaque occlusion of an arbitrary shape. The diffractive decoder comprises successive spatially-engineered passive surfaces that process optical information through light-matter interactions. Following its training, the encoder-decoder pair can communicate any arbitrary optical information around opaque occlusions, where information decoding occurs at the speed of light propagation. For occlusions that change their size and/or shape as a function of time, the encoder neural network can be retrained to successfully communicate with the existing diffractive decoder, without changing the physical layer(s) already deployed. We also validate this framework experimentally in the terahertz spectrum using a 3D-printed diffractive decoder to communicate around a fully opaque occlusion. Scalable for operation in any wavelength regime, this scheme could be particularly useful in emerging high data-rate free-space communication systems., Comment: 23 Pages, 9 Figures

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit., Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figures

Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. In this work, we demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network (CNN) based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning, our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and the optical-decoder model were experimentally validated using a 3D-printed diffractive network that axially spans less than 70 x lambda, where lambda = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media., Comment: 32 Pages, 9 Figures

Neural ordinary differential equations (NODEs) have been proven useful for learning non-linear dynamics of arbitrary trajectories. However, current NODE methods capture variations across trajectories only via the initial state value or by auto-regressive encoder updates. In this work, we introduce Modulated Neural ODEs (MoNODEs), a novel framework that sets apart dynamics states from underlying static factors of variation and improves the existing NODE methods. In particular, we introduce $\textit{time-invariant modulator variables}$ that are learned from the data. We incorporate our proposed framework into four existing NODE variants. We test MoNODE on oscillating systems, videos and human walking trajectories, where each trajectory has trajectory-specific modulation. Our framework consistently improves the existing model ability to generalize to new dynamic parameterizations and to perform far-horizon forecasting. In addition, we verify that the proposed modulator variables are informative of the true unknown factors of variation as measured by $R^2$ scores.

Data discovery is a major challenge in enterprise data analysis: users often struggle to find data relevant to their analysis goals or even to navigate through data across data sources, each of which may easily contain thousands of tables. One common user need is to discover tables joinable with a given table. This need is particularly critical because join is a ubiquitous operation in data analysis, and join paths are mostly obscure to users, especially across databases. Furthermore, users are typically interested in finding ``semantically'' joinable tables: with columns that can be transformed to become joinable even if they are not joinable as currently represented in the data store. We present WarpGate, a system prototype for data discovery over cloud data warehouses. WarpGate implements an embedding-based solution to semantic join discovery, which encodes columns into high-dimensional vector space such that joinable columns map to points that are near each other. Through experiments on several table corpora, we show that WarpGate (i) captures semantic relationships between tables, especially those across databases, and (ii) is sample efficient and thus scalable to very large tables of millions of rows. We also showcase an application of WarpGate within an enterprise product for cloud data analytics., Comment: CIDR'23

We propose TROD, a novel transaction-oriented framework for debugging modern distributed web applications and online services. Our critical insight is that if applications store all state in databases and only access state transactionally, TROD can use lightweight always-on tracing to track the history of application state changes and data provenance, and then leverage the captured traces and transaction logs to faithfully replay or even test modified code retroactively on any past event. We demonstrate how TROD can simplify programming and debugging in production applications, list several research challenges and directions, and encourage the database and systems communities to drastically rethink the synergy between the way people develop and debug applications., Comment: CIDR'23

Domain-specific languages (DSLs) for visualization aim to facilitate visualization creation by providing abstractions that offload implementation and execution details from users to the system layer. Therefore, DSLs often execute user-defined specifications by transforming them into intermediate representations (IRs) in successive lowering operations. However, DSL-specified visualizations can be difficult to profile and, hence, optimize due to the layered abstractions. To better understand visualization profiling workflows and challenges, we conduct formative interviews with visualization engineers who use Vega in production. Vega is a popular visualization DSL that transforms specifications into dataflow graphs, which are then executed to render visualization primitives. Our formative interviews reveal that current developer tools are ill-suited for visualization profiling since they are disconnected from the semantics of Vega's specification and its IRs at runtime. To address this gap, we introduce VegaProf, the first performance profiler for Vega visualizations. VegaProf instruments the Vega library by associating a declarative specification with its compilation and execution. Integrated into a Vega code playground, VegaProf coordinates visual performance inspection at three abstraction levels: function, dataflow graph, and visualization specification. We evaluate VegaProf through use cases and feedback from visualization engineers as well as original developers of the Vega library. Our results suggest that VegaProf makes visualization profiling more tractable and actionable by enabling users to interactively probe time performance across layered abstractions of Vega. Furthermore, we distill recommendations from our findings and advocate for co-designing visualization DSLs together with their introspection tools., Comment: Published at UIST'23

What-if analysis (WIA), crucial for making data-driven decisions, enables users to understand how changes in variables impact outcomes and explore alternative scenarios. However, existing WIA research focuses on supporting the workflows of data scientists or analysts, largely overlooking significant non-technical users, like business users. We conduct a two-part user study with 22 business users (marketing, sales, product, and operations managers). The first study examines existing WIA techniques employed, tools used, and challenges faced. Findings reveal that business users perform many WIA techniques independently using rudimentary tools due to various constraints. We implement representative WIA techniques identified previously in a visual analytics prototype to use as a probe to conduct a follow-up study evaluating business users' practical use of the techniques. These techniques improve decision-making efficiency and confidence while highlighting the need for better support in data preparation, risk assessment, and domain knowledge integration. Finally, we offer design recommendations to enhance future business analytics systems.

Rating a video based on its content is an important step for classifying video age categories. Movie content rating and TV show rating are the two most common rating systems established by professional committees. However, manually reviewing and evaluating scene/film content by a committee is a tedious work and it becomes increasingly difficult with the ever-growing amount of online video content. As such, a desirable solution is to use computer vision based video content analysis techniques to automate the evaluation process. In this paper, related works are summarized for action recognition, multi-modal learning, movie genre classification, and sensitive content detection in the context of content moderation and movie content rating. The project page is available at https://github.com/fcakyon/content-moderation-deep-learning.

Training dynamic models, such as neural ODEs, on long trajectories is a hard problem that requires using various tricks, such as trajectory splitting, to make model training work in practice. These methods are often heuristics with poor theoretical justifications, and require iterative manual tuning. We propose a principled multiple shooting technique for neural ODEs that splits the trajectories into manageable short segments, which are optimised in parallel, while ensuring probabilistic control on continuity over consecutive segments. We derive variational inference for our shooting-based latent neural ODE models and propose amortized encodings of irregularly sampled trajectories with a transformer-based recognition network with temporal attention and relative positional encoding. We demonstrate efficient and stable training, and state-of-the-art performance on multiple large-scale benchmark datasets.

There have been many studies on intelligent robotic systems for patients with motor impairments, where different sensor types and different human-machine interface (HMI) methods have been developed. However, these studies fail to achieve complex activity detection at the minimum sensing level. In this paper, exploratory approaches are adopted to investigate ocular activity dynamics and complex activity estimation using a single-channel EOG device. First, the stationarity of ocular activities during a static motion is investigated and some activities are found to be non-stationary. Further, no statistical difference is found between the envelope sequences in the temporal domain. However, when utilized as an alternative to a low-pass filter, high-frequency harmonic components in the frequency domain are found to improve contrasting ocular activities and the performance of the EOG-HMI-based activity detection system substantially. The activities are trained with different classifiers and their prediction success is evaluated with leave-one-session-out cross-validation. Accordingly, the two-dimensional CNN model achieved the highest performance with the accuracy of 72.35\%. Furthermore, the clustering performance is assessed using unsupervised learning and the results are evaluated in terms of how well the feature sets are grouped. The system is further tested in real-time with the graphical user interface and the scores and survey data of the subjects are used to verify the effectiveness., Comment: 15 pages, 11 figures, 5 tables

We recently proposed a new cluster operating system stack, DBOS, centered on a DBMS. DBOS enables unique support for ML applications by encapsulating ML code within stored procedures, centralizing ancillary ML data, providing security built into the underlying DBMS, co-locating ML code and data, and tracking data and workflow provenance. Here we demonstrate a subset of these benefits around two ML applications. We first show that image classification and object detection models using GPUs can be served as DBOS stored procedures with performance competitive to existing systems. We then present a 1D CNN trained to detect anomalies in HTTP requests on DBOS-backed web services, achieving SOTA results. We use this model to develop an interactive anomaly detection system and evaluate it through qualitative user feedback, demonstrating its usefulness as a proof of concept for future work to develop learned real-time security services on top of DBOS., Comment: AIDB@VLDB 2022

Drilling a hole on a curved surface with a desired angle is prone to failure when done manually, due to the difficulties in drill alignment and also inherent instabilities of the task, potentially causing injury and fatigue to the workers. On the other hand, it can be impractical to fully automate such a task in real manufacturing environments because the parts arriving at an assembly line can have various complex shapes where drill point locations are not easily accessible, making automated path planning difficult. In this work, an adaptive admittance controller with 6 degrees of freedom is developed and deployed on a KUKA LBR iiwa 7 cobot such that the operator is able to manipulate a drill mounted on the robot with one hand comfortably and open holes on a curved surface with haptic guidance of the cobot and visual guidance provided through an AR interface. Real-time adaptation of the admittance damping provides more transparency when driving the robot in free space while ensuring stability during drilling. After the user brings the drill sufficiently close to the drill target and roughly aligns to the desired drilling angle, the haptic guidance module fine tunes the alignment first and then constrains the user movement to the drilling axis only, after which the operator simply pushes the drill into the workpiece with minimal effort. Two sets of experiments were conducted to investigate the potential benefits of the haptic guidance module quantitatively (Experiment I) and also the practical value of the proposed pHRI system for real manufacturing settings based on the subjective opinion of the participants (Experiment II)., Comment: RA-L IROS 2022

Drone detection has become an essential task in object detection as drone costs have decreased and drone technology has improved. It is, however, difficult to detect distant drones when there is weak contrast, long range, and low visibility. In this work, we propose several sequence classification architectures to reduce the detected false-positive ratio of drone tracks. Moreover, we propose a new drone vs. bird sequence classification dataset to train and evaluate the proposed architectures. 3D CNN, LSTM, and Transformer based sequence classification architectures have been trained on the proposed dataset to show the effectiveness of the proposed idea. As experiments show, using sequence information, bird classification and overall F1 scores can be increased by up to 73% and 35%, respectively. Among all sequence classification models, R(2+1)D-based fully convolutional model yields the best transfer learning and fine-tuning results.

High-resolution synthesis/projection of images over a large field-of-view (FOV) is hindered by the restricted space-bandwidth-product (SBP) of wavefront modulators. We report a deep learning-enabled diffractive display design that is based on a jointly-trained pair of an electronic encoder and a diffractive optical decoder to synthesize/project super-resolved images using low-resolution wavefront modulators. The digital encoder, composed of a trained convolutional neural network (CNN), rapidly pre-processes the high-resolution images of interest so that their spatial information is encoded into low-resolution (LR) modulation patterns, projected via a low SBP wavefront modulator. The diffractive decoder processes this LR encoded information using thin transmissive layers that are structured using deep learning to all-optically synthesize and project super-resolved images at its output FOV. Our results indicate that this diffractive image display can achieve a super-resolution factor of ~4, demonstrating a ~16-fold increase in SBP. We also experimentally validate the success of this diffractive super-resolution display using 3D-printed diffractive decoders that operate at the THz spectrum. This diffractive image decoder can be scaled to operate at visible wavelengths and inspire the design of large FOV and high-resolution displays that are compact, low-power, and computationally efficient., Comment: 26 Pages, 9 Figures

Online travel agencies (OTA's) advertise their website offers on meta-search bidding engines. The problem of predicting the number of clicks a hotel would receive for a given bid amount is an important step in the management of an OTA's advertisement campaign on a meta-search engine, because bid times number of clicks defines the cost to be generated. Various regressors are ensembled in this work to improve click prediction performance. Following the preprocessing procedures, the feature set is divided into train and test groups depending on the logging date of the samples. The data collection is then subjected to feature elimination via utilizing XGBoost, which significantly reduces the dimension of features. The optimum hyper-parameters are then found by applying Bayesian hyperparameter optimization to XGBoost, LightGBM, and SGD models. The different trained models are tested separately as well as combined to form ensemble models. Four alternative ensemble solutions have been suggested. The same test set is used to test both individual and ensemble models, and the results of 46 model combinations demonstrate that stack ensemble models yield the desired R2 score of all. In conclusion, the ensemble model improves the prediction performance by about 10%., Comment: 24 pages, 3 figures, 1 algorithm, 4 equations

In this paper, we propose a supervised learning approach based on an Artificial Neural Network (ANN) model for real-time classification of subtasks in a physical human-robot interaction (pHRI) task involving contact with a stiff environment. In this regard, we consider three subtasks for a given pHRI task: Idle, Driving, and Contact. Based on this classification, the parameters of an admittance controller that regulates the interaction between human and robot are adjusted adaptively in real time to make the robot more transparent to the operator (i.e. less resistant) during the Driving phase and more stable during the Contact phase. The Idle phase is primarily used to detect the initiation of task. Experimental results have shown that the ANN model can learn to detect the subtasks under different admittance controller conditions with an accuracy of 98% for 12 participants. Finally, we show that the admittance adaptation based on the proposed subtask classifier leads to 20% lower human effort (i.e. higher transparency) in the Driving phase and 25% lower oscillation amplitude (i.e. higher stability) during drilling in the Contact phase compared to an admittance controller with fixed parameters.