Miracle-dz/UniFAR-FTUs huggingface dataset:
An OpenCL 3D FFT for Molecular Dynamics Simulations on Multiple FPGAs

3D FFTs are used to accelerate MD electrostatic forces computations but are difficult to parallelize due to communications requirements. We present a distributed OpenCL 3D FFT implementation on Intel Stratix 10 FPGAs for grids up to {\boldmath $128^3$}. We use FPGA hardware features such as HBM2 memory and multiple 100 Gbps links to provide scalable memory accesses and communications. Our implementation outperforms GPUs for smaller FFTs, even without distribution. For {\boldmath$32^3$} we achieve 4.4 microseconds on a single FPGA, similar to Anton 1 on 512 nodes. For 8 parallel pipelines (hardware limited), we reach the same performance both locally and distributed, showing that communications are not limiting the performance. Our FFT implementation is designed to be part of the electrostatic force pipeline of a scalable MD engin
Odd primary analogs of real orientations

We define, in $C_p$-equivariant homotopy theory for $p>2$, a notion of $\mu_p$-orientation analogous to a $C_2$-equivariant Real orientation. The definition hinges on a $C_p$-space $\mathbb{CP}^{\infty}_{\mu_p}$, which we prove to be homologically even in a sense generalizing recent $C_2$-equivariant work on conjugation spaces. We prove that the height $p-1$ Morava $E$-theory is $\mu_p$-oriented and that $\mathrm{tmf}(2)$ is $\mu_3$-oriented. We explain how a single equivariant map $v_1^{\mu_p}:S^{2\rho} \to \Sigma^{\infty} \mathbb{CP}^{\infty}_{\mu_p}$ completely generates the homotopy of $E_{p-1}$ and $\mathrm{tmf}(2)$, expressing a height-shifting phenomenon pervasive in equivariant chromatic homotopy theory.
Associated production in pp and heavy ion collisions

Associated particle production processes in pp and heavy ion collisions at the LHC are in particular interesting in the sense that they provide unique tools to study double parton scattering (DPS) mechanism. In this talk, I will first review the recent theoretical, phenomenological and experimental developments of DPS in pp collisions. Then, I will focus on the DPS studies in heavy ion collisions, and stress their roles in understanding the cold nuclear matter effects, such as the (poorly known) impact-parameter dependent nuclear parton densities.
High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy

Raman spectroscopy enables nondestructive, label-free imaging with unprecedented molecular contrast, but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep-learning-enabled Raman spectroscopy, termed DeepeR, trained on a large data set of hyperspectral Raman images, with over 1.5 million spectra (400 h of acquisition) in total. We first perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 10× improvement in the mean-squared error over common Raman filtering methods. Next, we develop a neural network for robust 2–4× spatial super-resolution of hyperspectral Raman images that preserve molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 40–90×, enabling good-quality cellular imaging with a high-resolution, high signal-to-noise ratio in under 1 min. We further demonstrate Raman imaging speed-up of 160×, useful for lower resolution imaging applications such as the rapid screening of large areas or for spectral pathology. Finally, transfer learning is applied to extend DeepeR from cell to tissue-scale imaging. DeepeR provides a foundation that will enable a host of higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine.
Learning to Detect Objects with a 1 Megapixel Event Camera

Event cameras encode visual information with high temporal precision, low data-rate, and high-dynamic range. Thanks to these characteristics, event cameras are particularly suited for scenarios with high motion, challenging lighting conditions and requiring low latency. However, due to the novelty of the field, the performance of event-based systems on many vision tasks is still lower compared to conventional frame-based solutions. The main reasons for this performance gap are: the lower spatial resolution of event sensors, compared to frame cameras; the lack of large-scale training datasets; the absence of well established deep learning architectures for event-based processing. In this paper, we address all these problems in the context of an event-based object detection task. First, we publicly release the first high-resolution large-scale dataset for object detection. The dataset contains more than 14 hours recordings of a 1 megapixel event camera, in automotive scenarios, together with 25M bounding boxes of cars, pedestrians, and two-wheelers, labeled at high frequency. Second, we introduce a novel recurrent architecture for event-based detection and a temporal consistency loss for better-behaved training. The ability to compactly represent the sequence of events into the internal memory of the model is essential to achieve high accuracy. Our model outperforms by a large margin feed-forward event-based architectures. Moreover, our method does not require any reconstruction of intensity images from events, showing that training directly from raw events is possible, more efficient, and more accurate than passing through an intermediate intensity image. Experiments on the dataset introduced in this work, for which events and gray level images are available, show performance on par with that of highly tuned and studied frame-based detectors.
A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution in English

Pronoun Coreference Resolution (PCR) is the task of resolving pronominal expressions to all mentions they refer to. Compared with the general coreference resolution task, the main challenge of PCR is the coreference relation prediction rather than the mention detection. As one important natural language understanding (NLU) component, pronoun resolution is crucial for many downstream tasks and still challenging for existing models, which motivates us to survey existing approaches and think about how to do better. In this survey, we first introduce representative datasets and models for the ordinary pronoun coreference resolution task. Then we focus on recent progress on hard pronoun coreference resolution problems (e.g., Winograd Schema Challenge) to analyze how well current models can understand commonsense. We conduct extensive experiments to show that even though current models are achieving good performance on the standard evaluation set, they are still not ready to be used in real applications (e.g., all SOTA models struggle on correctly resolving pronouns to infrequent objects). All experiment codes will be available upon acceptance.
Hierarchical correction of $p$-values via a tree running Ornstein-Uhlenbeck process.

Statistical testing is classically used as an exploratory tool to search for association between a phenotype and many possible explanatory variables. This approach often leads to multiple dependence testing under dependence. We assume a hierarchical structure between tests via an Ornstein-Uhlenbeckprocess on a tree. The process correlation structure is used for smoothing the p-values. We design a penalized estimation of the mean of the OU process for p-value computation. The performances of the algorithm are assessed via simulations. Its ability to discover new associations is demonstrated on a metagenomic dataset. The corresponding R package is available from this https URL.
Using Resource-Rational Analysis to Understand Cognitive Biases inInteractive Data Visualizations

Cognitive biases are systematic errors in judgment. Researchers in data visualizations have explored whether cognitive biases transfer to decision-making tasks with interactive data visualizations. At the same time, cognitive scientists have reinterpreted cognitive biases as the product of resource-rational strategies under finite time and computational costs. In this paper, we argue for the integration of resource-rational analysis through constrained Bayesian cognitive modeling to understand cognitive biases in data visualizations. The benefit would be a more realistic "bounded rationality" representation of data visualization users and provides a research roadmap for studying cognitive biases in data visualizations through a feedback loop between future experiments and theory
A Distributed Computationally Aware Quantizer Design via Hyper Binning

We design a distributed function-aware quantization scheme for distributed functional compression. We consider 2 correlated sources <inline-formula><tex-math notation="LaTeX">$X_{1}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$X_{2}$</tex-math></inline-formula> and a destination that seeks an estimate <inline-formula><tex-math notation="LaTeX">$\hat{f}$</tex-math></inline-formula> for the outcome of a continuous function <inline-formula><tex-math notation="LaTeX">$f(X_{1},\,X_{2})$</tex-math></inline-formula>. We develop a compression scheme called hyper binning in order to quantize <inline-formula><tex-math notation="LaTeX">$f$</tex-math></inline-formula> via minimizing the entropy of joint source partitioning. Hyper binning is a natural generalization of Cover's random code construction for the asymptotically optimal Slepian-Wolf encoding scheme that makes use of orthogonal binning. The key idea behind this approach is to use linear discriminant analysis in order to characterize different source feature combinations. This scheme captures the correlation between the sources and the function's structure as a means of dimensionality reduction. We investigate the performance of hyper binning for different source distributions and identify which classes of sources entail more partitioning to achieve better function approximation. Our approach brings an information theory perspective to the traditional vector quantization technique from signal processing.
A Review of Complex Systems Approaches to Cancer Networks

Cancers remain the leading cause of disease-related pediatric death in North America. The emerging field of complex systems has redefined cancer networks as a computational system. Herein, a tumor and its heterogeneous phenotypes are discussed as dynamical systems having multiple strange attractors. Machine learning, network science and algorithmic information dynamics are discussed as current tools for cancer network reconstruction. Deep learning architectures and computational fluid models are proposed for better forecasting gene expression patterns in cancer ecosystems. Cancer cell decision-making is investigated within the framework of complex systems and complexity theory.
Critical spin periods of sub-km-sized cohesive rubble-pile asteroids: dependences on material parameters

In this work, we employ a soft-sphere discrete element method with a cohesion implementation to model the dynamical process of sub-km-sized cohesive rubble piles under continuous spinup. The dependencies of critical spin periods $T_c$ on several material parameters for oblate rubble piles with different bulk diameters $D$ are explored. Our numerical simulations show that both the increase of interparticle cohesion and particle shape parameter in our model can strengthen the bodies, especially for the smaller ones. In addition, we find there exists some critical diameter $D_{cri,\rho}$ at which the variation trend of $T_c$ with the bulk density $\rho$ reverses. Though a greater static friction coefficient $\mu_S$ can strengthen the body, this effect attains a minimum at a critical diameter $D_{cri,\phi}$ close to $D_{cri,\rho}$. The continuum theory (analytical method) is used for comparison and two equivalent critical diameters are obtained. The numerical results were fitted with the analytical method and the ratio of the interparticle cohesion $c$ to the bulk cohesion $C$ is estimated to be roughly 88.3. We find this ratio keeps constant for different $c$ and $\rho$, while it strongly depends on the friction angle $\phi$. Also, our numerical results further show that the dependency of $T_c$ on $\phi$ is opposite from that predicted by the continuum theory when $D$ < $D_{cri,\phi}$. Finally, we find that the two critical diameters happen to be close to the diameter when the mean normal stress of the body equals zero, which is the separation between the compressive regime and the tensile regime.
Recurrent Inference in Text Editing

In neural text editing, prevalent sequence-to-sequence based approaches directly map the unedited text either to the edited text or the editing operations, in which the performance is degraded by the limited source text encoding and long, varying decoding steps. To address this problem, we propose a new inference method, Recurrence, that iteratively performs editing actions, significantly narrowing the problem space. In each iteration, encoding the partially edited text, Recurrence decodes the latent representation, generates an action of short, fixed-length, and applies the action to complete a single edit. For a comprehensive comparison, we introduce three types of text editing tasks: Arithmetic Operators Restoration (AOR), Arithmetic Equation Simplification (AES), Arithmetic Equation Correction (AEC). Extensive experiments on these tasks with varying difficulties demonstrate that Recurrence achieves improvements over conventional inference methods.
Defining and Quantifying Conversation Quality in Spontaneous Interactions

Social interactions in general are multifaceted and there exists a wide set of factors and events that influence them. In this paper, we quantify social interactions with a holistic viewpoint on individual experiences, particularly focusing on non-task-directed spontaneous interactions. To achieve this, we design a novel perceived measure, the perceived Conversation Quality, which intends to quantify spontaneous interactions by accounting for several socio-dimensional aspects of individual experiences. To further quantitatively study spontaneous interactions, we devise a questionnaire which measures the perceived Conversation Quality, at both the individual- and at the group- level. Using the questionnaire, we collected perceived annotations for conversation quality in a publicly available dataset using naive annotators. The results of the analysis performed on the distribution and the inter-annotator agreeability shows that naive annotators tend to agree less in cases of low conversation quality samples, especially while annotating for group-level conversation quality.
Neural Proof Nets

Linear logic and the linear λ-calculus have a long standing tradition in the study of natural language form and meaning. Among the proof calculi of linear logic, proof nets are of particular interest, offering an attractive geometric representation of derivations that is unburdened by the bureaucratic complications of conventional prooftheoretic formats. Building on recent advances in set-theoretic learning, we propose a neural variant of proof nets based on Sinkhorn networks, which allows us to translate parsing as the problem of extracting syntactic primitives and permuting them into alignment. Our methodology induces a batch-efficient, end-to-end differentiable architecture that actualizes a formally grounded yet highly efficient neuro-symbolic parser. We test our approach on ÆThel, a dataset of type-logical derivations for written Dutch, where it manages to correctly transcribe raw text sentences into proofs and terms of the linear λ-calculus with an accuracy of as high as 70%.
RENT - Repeated Elastic Net Technique for Feature Selection

Feature selection is an essential step in data science pipelines to reduce the complexity associated with large datasets. While much research on this topic focuses on optimizing predictive performance, few studies investigate stability in the context of the feature selection process. In this study, we present the Repeated Elastic Net Technique (RENT) for Feature Selection. RENT uses an ensemble of generalized linear models with elastic net regularization, each trained on distinct subsets of the training data. The feature selection is based on three criteria evaluating the weight distributions of features across all elementary models. This fact leads to the selection of features with high stability that improve the robustness of the final model. Furthermore, unlike established feature selectors, RENT provides valuable information for model interpretation concerning the identification of objects in the data that are difficult to predict during training. In our experiments, we benchmark RENT against six established feature selectors on eight multivariate datasets for binary classification and regression. In the experimental comparison, RENT shows a well-balanced trade-off between predictive performance and stability. Finally, we underline the additional interpretational value of RENT with an exploratory post-hoc analysis of a healthcare dataset.
Architectural Design Alternatives Based on Cloud/Edge/Fog Computing for Connected Vehicles

As vehicles playing an increasingly important role in people’s daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can provide enabling technologies to realize these requirements and have attracted widespread attentions from both academia and industry. These requirements ask for a well-designed computing architecture to support the Quality-of-Service (QoS) of CV applications. Computation offloading techniques, such as cloud, edge, and fog computing, can help CVs process computation-intensive and large-scale computing tasks. Additionally, different cloud/edge/fog computing architectures are suitable for supporting different types of CV applications with highly different QoS requirements, which demonstrates the importance of the computing architecture design. However, most of the existing surveys on cloud/edge/fog computing for CVs overlook the computing architecture design, where they (i) only focus on one specific computing architecture and (ii) lack discussions on benefits, research challenges, and system requirements of different architectural alternatives. In this article, we provide a comprehensive survey on different architectural design alternatives based on cloud/edge/fog computing for CVs. The contributions of this article are: (i) providing a comprehensive literature survey on existing proposed architectural design alternatives based on cloud/edge/fog computing for CVs, (ii) proposing a new classification of computing architectures based on cloud/edge/fog computing for CVs: computation-aided and computation-enabled architectures, (iii) presenting a holistic comparison among different cloud/edge/fog computing architectures for CVs based on functional requirements of CV systems, including advantages, disadvantages, and research challenges, (iv) presenting a holistic overview on the design of CV systems from both academia and industry perspectives, including activities in industry, functional requirements, service requirements, and design considerations, and (v) proposing several open research issues of designing cloud/edge/fog computing architectures for CVs.
Dense-View GEIs Set: View Space Covering for Gait Recognition based on Dense-View GAN

Gait recognition has proven to be effective for long-distance human recognition. But view variance of gait features would change human appearance greatly and reduce its performance. Most existing gait datasets usually collect data with a dozen different angles, or even more few. Limited view angles would prevent learning better view invariant feature. It can further improve robustness of gait recognition if we collect data with various angles at 1° interval. But it is time consuming and labor consuming to collect this kind of dataset. In this paper, we, therefore, introduce a Dense-View GEIs Set (DV-GEIs) to deal with the challenge of limited view angles. This set can cover the whole view space, view angle from 0° to 180° with 1° interval. In addition, Dense-View GAN (DV-GAN) is proposed to synthesize this dense view set. DV-GAN consists of Generator, Discriminator and Monitor, where Monitor is designed to preserve human identification and view information. The proposed method is evaluated on the CASIA-B and OU-ISIR dataset. The experimental results show that DV-GEIs synthesized by DV-GAN is an effective way to learn better view invariant feature. We believe the idea of dense view generated samples will further improve the development of gait recognition.
Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation

Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost. We employ a Data Science (DS) driven solution to predict user load and leverage these predictions to optimize resource allocation. To this end, we built the Seagull infrastructure that processes per-server telemetry, validates the data, trains and deploys ML models. The models are used to predict customer load per server (24h into the future), and optimize service operations. Seagull continually re-evaluates accuracy of predictions, fallback to previously known good models and triggers alerts as appropriate. We deployed this infrastructure in production for PostgreSQL and MySQL servers across all Azure regions, and applied it to the problem of scheduling server backups during low-load time. This minimizes interference with user-induced load and improves customer experience.
Lightweight assistive technology: A wearable, optical-fiber gesture recognition system

The goal of this project is to create an inexpensive, lightweight, wearable assistive device that can measure hand or finger movements accurately enough to identify a range of hand gestures. One eventual application is to provide assistive technology and sign language detection for the hearing impaired. My system, called LiTe (Light-based Technology), uses optical fibers embedded into a wristband. The wrist is an optimal place for the band since the light propagation in the optical fibers is impacted even by the slight movements of the tendons in the wrist when gestures are performed. The prototype incorporates light dependent resistors to measure these light propagation changes. When creating LiTe, I considered a variety of fiber materials, light frequencies, and physical shapes to optimize the tendon movement detection so that it can be accurately correlated with different gestures. I implemented and evaluated two approaches for gesture recognition. The first uses an algorithm that combines moving averages of sensor readings with gesture sensor reading signatures to determine the current gesture. The second uses a neural network trained on a labelled set of gesture readings to recognize gestures. Using the signature-based approach, I was able to achieve a 99.8% accuracy at recognizing distinct gestures. Using the neural network the recognition accuracy was 98.8%. This shows that high accuracy is feasible using both approaches. The results indicate that this novel method of using fiber optics-based sensors is a promising first step to creating a gesture recognition system.