Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a fiber barrel detector for next-generation high-pressure gaseous xenon TPCs

The NEXT (Neutrino Experiment with a Xenon TPC) project is an international collaboration aimed at finding evidence of neutrinoless double beta decay using gaseous xenon. The current phase of the project involves the construction and operation of NEXT-100, which is designed to hold 100 kg of xenon at 15 bar and is expected to start commissioning in the first quarter of 2024. NEXT-HD will be a tonne scale experiment following NEXT-100 and will incorporate a symmetric design, with one cathode and two anodes. For this detector, the collaboration is considering to implement a barrel of wavelength-shifting fibers read-out by silicon photomultipliers to measure the energy of the particles interacting in the gaseous xenon. In this document, we will discuss the characteristics of this approach and provide an update on the related R&D efforts.
The CO Outflow Components Ejected by a Recent Accretion Event in B335

Protostellar outflows often present a knotty appearance, providing evidence of sporadic accretion in stellar mass growth. To understand the direct relation between mass accretion and ejection, we analyze the contemporaneous accretion activity and associated ejection components in B335. B335 has brightened in the mid-IR by 2.5 mag since 2010, indicating increased luminosity, presumably due to an increased mass accretion rate onto the protostar. Atacama Large Millimeter/submillimeter Array (ALMA) observations of 12CO emission in the outflow reveal high-velocity emission, estimated to have been ejected 4.6–2 yr before the ALMA observations and consistent with the jump in mid-IR brightness. The consistency in timing suggests that the detected high-velocity ejection components are directly linked to the most recent accretion activity. We calculated the kinetic energy, momentum, and force for the ejection component associated with the most recent accretion activity and found that, at least, about 1.0% of the accreted mass has been ejected. More accurate information on the jet inclination and the temperature of the ejected gas components will better constrain the ejected mass induced by the recently enhanced accretion event.
All Rivers Run to the Sea: Private Learning with Asymmetric Flows

Data privacy is of great concern in cloud machine-learning service platforms, when sensitive data are exposed to service providers. While private computing environments (e.g., secure enclaves), and cryptographic approaches (e.g., homomorphic encryption) provide strong privacy protection, their computing performance still falls short compared to cloud GPUs. To achieve privacy protection with high computing performance, we propose Delta, a new private training and inference framework, with comparable model performance as non-private centralized training. Delta features two asymmetric data flows: the main information-sensitive flow and the residual flow. The main part flows into a small model while the residuals are offloaded to a large model. Specifically, Delta embeds the information-sensitive representations into a low-dimensional space while pushing the information-insensitive part into high-dimension residuals. To ensure privacy protection, the low-dimensional information-sensitive part is secured and fed to a small model in a private environment. On the other hand, the residual part is sent to fast cloud GPUs, and processed by a large model. To further enhance privacy and reduce the communication cost, Delta applies a random binary quantization technique along with a DP-based technique to the residuals before sharing them with the public platform. We theoretically show that Delta guarantees differential privacy in the public environment and greatly reduces the complexity in the private environment. We conduct empirical analyses on CIFAR-10, CIFAR-100 and ImageNet datasets and ResNet-18 and ResNet-34, showing that Delta achieves strong privacy protection, fast training, and inference without significantly compromising the model utility.
R2Human: Real-Time 3D Human Appearance Rendering from a Single Image

Rendering 3D human appearance in different views is crucial for achieving holographic communication and immersive VR/AR. Existing methods either rely on multi-camera setups or have low-quality rendered images from a single image. In this paper, we propose R2Human, the first approach for real-time inference and rendering of photorealistic 3D human appearance from a single image. The core of our approach is to combine the strengths of implicit texture fields and explicit neural rendering with our novel representation, namely Z-map. Based on this, we present an end-to-end network that performs high-fidelity color reconstruction of visible areas and provides reliable color inference for occluded regions. To further enhance the 3D perception ability of our network, we leverage the Fourier occupancy field as a prior for generating the texture field and providing a sampling surface in the rendering stage. We also propose a consistency loss and a spatio-temporal fusion strategy to ensure the multi-view coherence. Experimental results show that our method outperforms the state-of-the-art methods on both synthetic data and challenging real-world images, in real time.
Percentile Risk-Constrained Budget Pacing for Guaranteed Display Advertising in Online Optimization

Guaranteed display (GD) advertising is a critical component of advertising since it provides publishers with stable revenue and enables advertisers to target specific audiences with guaranteed impressions. However, smooth pacing control for online ad delivery presents a challenge due to significant budget disparities, user arrival distribution drift, and dynamic change between supply and demand. This paper presents robust risk-constrained pacing (RCPacing) that utilizes Lagrangian dual multipliers to fine-tune probabilistic throttling through monotonic mapping functions within the percentile space of impression performance distribution. RCPacing combines distribution drift resilience and compatibility with guaranteed allocation mechanism, enabling us to provide near-optimal online services. We also show that RCPacing achieves O(sqrt(T)) dynamic regret where T is the length of the horizon. RCPacing's effectiveness is validated through offline evaluations and online A/B testing conducted on Taobao brand advertising platform.
Stealing Maggie's Secrets - On the Challenges of IP Theft Through FPGA Reverse Engineering

Intellectual Property (IP) theft is a cause for major financial and reputational damage, reportedly in the range of hundreds of billions of dollars annually in the U.S. alone. Field Programmable Gate Arrays (FPGAs) are particularly exposed to IP theft, because their configuration file contains the IP in a proprietary format that can be mapped to a gate-level netlist with moderate effort. Despite this threat, the scientific understanding of this issue lacks behind reality, thereby preventing an in-depth assessment of IP theft from FPGAs in academia. We address this discrepancy through a real-world case study on a Lattice iCE40 FPGA found inside iPhone 7. Apple refers to this FPGA as Maggie. By reverse engineering the proprietary signal-processing algorithm implemented on Maggie, we generate novel insights into the actual efforts required to commit FPGA IP theft and the challenges an attacker faces on the way. Informed by our case study, we then introduce generalized netlist reverse engineering techniques that drastically reduce the required manual effort and are applicable across a diverse spectrum of FPGA implementations and architectures. We evaluate these techniques on seven benchmarks that are representative for different FPGA applications and have been synthesized for Xilinx and Lattice FPGAs. Finally, we provide a comprehensive open-source tool-suite of netlist reverse engineering techniques to foster future research, enable the community to perform realistic threat assessments, and facilitate the evaluation of novel countermeasures.
Breast Cancer Detection Using Deep Learning Technique Based On Ultrasound Image

Breast cancer ranks as the most prevalent form of cancer diagnosed in women, and diagnosis faces several challenges, a change in the size, shape and appearance of breasts, dense breast tissue, lumps or thickening in the breast especially if in only one breast, lumps and nodules in the breast. The major challenge that faces deep learning diagnosis of breast cancer was its shape, size and position non-uniformity especially malignant cancer. This work proposed a deep learning system that increased the accuracy of classification of breast cancer types from ultrasound images. It reaches 99.29% accuracy, exceeding other previous work. First, image processing was applied to in enhance the quality of input images. Second, the image segmentation was performed using U-Net architecture. Third, many features are extracted using Mobilenet. Finally, the accuracy of proposed system was evaluated.
R2-Talker: Realistic Real-Time Talking Head Synthesis with Hash Grid Landmarks Encoding and Progressive Multilayer Conditioning

Dynamic NeRFs have recently garnered growing attention for 3D talking portrait synthesis. Despite advances in rendering speed and visual quality, challenges persist in enhancing efficiency and effectiveness. We present R2-Talker, an efficient and effective framework enabling realistic real-time talking head synthesis. Specifically, using multi-resolution hash grids, we introduce a novel approach for encoding facial landmarks as conditional features. This approach losslessly encodes landmark structures as conditional features, decoupling input diversity, and conditional spaces by mapping arbitrary landmarks to a unified feature space. We further propose a scheme of progressive multilayer conditioning in the NeRF rendering pipeline for effective conditional feature fusion. Our new approach has the following advantages as demonstrated by extensive experiments compared with the state-of-the-art works: 1) The lossless input encoding enables acquiring more precise features, yielding superior visual quality. The decoupling of inputs and conditional spaces improves generalizability. 2) The fusing of conditional features and MLP outputs at each MLP layer enhances conditional impact, resulting in more accurate lip synthesis and better visual quality. 3) It compactly structures the fusion of conditional features, significantly enhancing computational efficiency.
ROSE: A Recognition-Oriented Speech Enhancement Framework in Air Traffic Control Using Multi-Objective Learning

Radio speech echo is a specific phenomenon in the air traffic control (ATC) domain, which degrades speech quality and further impacts automatic speech recognition (ASR) accuracy. In this work, a recognition-oriented speech enhancement (ROSE) framework is proposed to improve speech intelligibility and also advance ASR accuracy, which serves as a plug-and-play tool in ATC scenarios and does not require additional retraining of the ASR model. Specifically, an encoder-decoder-based U-Net framework is proposed to eliminate the radio speech echo based on the real-world collected corpus. By incorporating the SE-oriented and ASR-oriented loss, ROSE is implemented in a multi-objective manner by learning shared representations across the two optimization objectives. An attention-based skip-fusion (ABSF) mechanism is applied to skip connections to refine the features. A channel and sequence attention (CSAtt) block is innovatively designed to guide the model to focus on informative representations and suppress disturbing features. The experimental results show that the ROSE significantly outperforms other state-of-the-art methods for both the SE and ASR tasks. In addition, the proposed approach can contribute to the desired performance improvements on public datasets.
ProtoCode: Leveraging Large Language Models for Automated Generation of Machine-Readable Protocols from Scientific Publications

Protocol standardization and sharing are crucial for reproducibility in life sciences. In spite of numerous efforts for standardized protocol description, adherence to these standards in literature remains largely inconsistent. Curation of protocols are especially challenging due to the labor intensive process, requiring expert domain knowledge of each experimental procedure. Recent advancements in Large Language Models (LLMs) offer a promising solution to interpret and curate knowledge from complex scientific literature. In this work, we develop ProtoCode, a tool leveraging fine-tune LLMs to curate protocols which can be interpretable by both human and machine interfaces. Our proof-of-concept, focused on polymerase chain reaction (PCR) protocols, retrieves information from PCR protocols at an accuracy ranging 69-100% depending on the information content. In all the tested protocols, we demonstrate that ProtoCode successfully converts literature-based protocols into correct operational files for multiple thermal cycler systems. In conclusion, ProtoCode can alleviate labor intensive curation and standardization of life science protocols to enhance research reproducibility by providing a reliable, automated means to process and standardize protocols. ProtoCode is freely available as a web server at https://curation.taxila. io/ProtoCode/.
Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning

Recent advancements in motion planning for Autonomous Vehicles (AVs) show great promise in using expert driver behaviors in non-stationary driving environments. However, learning only through expert drivers needs more generalizability to recover from domain shifts and near-failure scenarios due to the dynamic behavior of traffic participants and weather conditions. A deep Graph-based Prediction and Planning Policy Network (GP3Net) framework is proposed for non-stationary environments that encodes the interactions between traffic participants with contextual information and provides a decision for safe maneuver for AV. A spatio-temporal graph models the interactions between traffic participants for predicting the future trajectories of those participants. The predicted trajectories are utilized to generate a future occupancy map around the AV with uncertainties embedded to anticipate the evolving non-stationary driving environments. Then the contextual information and future occupancy maps are input to the policy network of the GP3Net framework and trained using Proximal Policy Optimization (PPO) algorithm. The proposed GP3Net performance is evaluated on standard CARLA benchmarking scenarios with domain shifts of traffic patterns (urban, highway, and mixed). The results show that the GP3Net outperforms previous state-of-the-art imitation learning-based planning models for different towns. Further, in unseen new weather conditions, GP3Net completes the desired route with fewer traffic infractions. Finally, the results emphasize the advantage of including the prediction module to enhance safety measures in non-stationary environments.
ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation

Monocular 3D human pose estimation (3D-HPE) is an inherently ambiguous task, as a 2D pose in an image might originate from different possible 3D poses. Yet, most 3D-HPE methods rely on regression models, which assume a one-to-one mapping between inputs and outputs. In this work, we provide theoretical and empirical evidence that, because of this ambiguity, common regression models are bound to predict topologically inconsistent poses, and that traditional evaluation metrics, such as the MPJPE, P-MPJPE and PCK, are insufficient to assess this aspect. As a solution, we propose ManiPose, a novel manifold-constrained multi-hypothesis model capable of proposing multiple candidate 3D poses for each 2D input, together with their corresponding plausibility. Unlike previous multi-hypothesis approaches, our solution is completely supervised and does not rely on complex generative models, thus greatly facilitating its training and usage. Furthermore, by constraining our model to lie within the human pose manifold, we can guarantee the consistency of all hypothetical poses predicted with our approach, which was not possible in previous works. We illustrate the usefulness of ManiPose in a synthetic 1D-to-2D lifting setting and demonstrate on real-world datasets that it outperforms state-of-the-art models in pose consistency by a large margin, while still reaching competitive MPJPE performance.
A New Projection Pursuit Index for Big Data

Visualization of extremely large datasets in static or dynamic form is a huge challenge because most traditional methods cannot deal with big data problems. A new visualization method for big data is proposed based on Projection Pursuit, Guided Tour and Data Nuggets methods, that will help display interesting hidden structures such as clusters, outliers, and other nonlinear structures in big data. The Guided Tour is a dynamic graphical tool for high-dimensional data combining Projection Pursuit and Grand Tour methods. It displays a dynamic sequence of low-dimensional projections obtained by using Projection Pursuit (PP) index functions to navigate the data space. Different PP indices have been developed to detect interesting structures of multivariate data but there are computational problems for big data using the original guided tour with these indices. A new PP index is developed to be computable for big data, with the help of a data compression method called Data Nuggets that reduces large datasets while maintaining the original data structure. Simulation studies are conducted and a real large dataset is used to illustrate the proposed methodology. Static and dynamic graphical tools for big data can be developed based on the proposed PP index to detect nonlinear structures.
On the Ground and in the Sky: A Tutorial on Radio Localization in Ground-Air-Space Networks

The inherent limitations in scaling up ground infrastructure for future wireless networks, combined with decreasing operational costs of aerial and space networks, are driving considerable research interest in multisegment ground-air-space (GAS) networks. In GAS networks, where ground and aerial users share network resources, ubiquitous and accurate user localization becomes indispensable, not only as an end-user service but also as an enabler for location-aware communications. This breaks the convention of having localization as a byproduct in networks primarily designed for communications. To address these imperative localization needs, the design and utilization of ground, aerial, and space anchors require thorough investigation. In this tutorial, we provide an in-depth systemic analysis of the radio localization problem in GAS networks, considering ground and aerial users as targets to be localized. Starting from a survey of the most relevant works, we then define the key characteristics of anchors and targets in GAS networks. Subsequently, we detail localization fundamentals in GAS networks, considering 3D positions and orientations. Afterward, we thoroughly analyze radio localization systems in GAS networks, detailing the system model, design aspects, and considerations for each of the three GAS anchors. Preliminary results are presented to provide a quantifiable perspective on key design aspects in GAS-based localization scenarios. We then identify the vital roles 6G enablers are expected to play in radio localization in GAS networks.
Complex-valued Neural Networks - Theory and Analysis

Complex-valued neural networks (CVNNs) have recently been successful in various pioneering areas which involve wave-typed information and frequency-domain processing. This work addresses different structures and classification of CVNNs. The theory behind complex activation functions, implications related to complex differentiability and special activations for CVNN output layers are presented. The work also discusses CVNN learning and optimization using gradient and non-gradient based algorithms. Complex Backpropagation utilizing complex chain rule is also explained in terms of Wirtinger calculus. Moreover, special modules for building CVNN models, such as complex batch normalization and complex random initialization are also discussed. The work also highlights libraries and software blocks proposed for CVNN implementations and discusses future directions. The objective of this work is to understand the dynamics and most recent developments of CVNNs.
Dynamic diurnal social taxonomy of urban environments

this paper, we explore how the nature of places changes by time of day due to the collective impact of the social, temporal and spatial dimensions of activity decisions. We focus on how social contexts impact the temporal and spatial decisions of activities by activity types and compare the transition of spatial patterns over time of day. We use very detailed social interaction information in the CentreSIM dataset, which is a household-based activity diary survey collected in Centre County, Pennsylvania, USA in 2003. We offer a methodology to extract the dynamic social taxonomy of places from behavioral data, and suggest how urban and transportation models can be informed from the dynamics of places by observing "what is taking place" combined with "what exists" or "what is available". The results can also be used to improve design of urban environments, temporal planning of places, transportation policies that are sensitive to time of day, and modeling of spatio-temporal decisions of social activities in travel demand models.
Pipeline for performance evaluation of flavour tagging dedicated Graph Neural Network algorithms

Machine Learning is a rapidly expanding field with a wide range of applications in science. In the field of physics, the Large Hadron Collider, the world's largest particle accelerator, utilizes Neural Networks for various tasks, including flavour tagging. Flavour tagging is the process of identifying the flavour of the hadron that initiates a jet in a collision event, and it is an essential aspect of various Standard Model and Beyond the Standard Model studies. Graph Neural Networks are currently the primary machine-learning tool used for flavour tagging. Here, we present the AUTOGRAPH pipeline, a completely customizable tool designed with a user-friendly interface to provide easy access to the Graph Neural Networks algorithm used for flavour tagging.
Towards a Unified Naming Scheme for Thermo-Active Soft Actuators: A Review of Materials, Working Principles, and Applications

Soft robotics is a rapidly growing field that spans the fields of chemistry, materials science, and engineering. Due to the diverse background of the field, there have been contrasting naming schemes such as 'intelligent', 'smart' and 'adaptive' materials which add vagueness to the broad innovation among literature. Therefore, a clear, functional and descriptive naming scheme is proposed in which a previously vague name -- Soft Material for Soft Actuators -- can remain clear and concise -- Phase-Change Elastomers for Artificial Muscles. By synthesizing the working principle, material, and application into a naming scheme, the searchability of soft robotics can be enhanced and applied to other fields. The field of thermo-active soft actuators spans multiple domains and requires added clarity. Thermo-active actuators have potential for a variety of applications spanning virtual reality haptics to assistive devices. This review offers a comprehensive guide to selecting the type of thermo-active actuator when one has an application in mind. Additionally, it discusses future directions and improvements that are necessary for implementation.