Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance versus resilience in modern quark-gluon tagging

Discriminating quark-like from gluon-like jets is, in many ways, a key challenge for many LHC analyses. First, we use a known difference in PYTHIA and HERWIG simulations to show how decorrelated taggers would break down when the most distinctive feature is aligned with theory uncertainties. We propose conditional training on interpolated samples, combined with a controlled Bayesian network, as a more resilient framework. The interpolation parameter can be used to optimize the training evaluated on a calibration dataset, and to test the stability of this optimization. The interpolated training might also be useful to track generalization errors when training networks on simulations.
Graalvisor: Virtualized Polyglot Runtime for Serverless Applications

Serverless is a new attractive computing model that offers great scalability and elasticity, taking the infrastructure management burden away from users, and enabling a pay-as-you-use billing model. As a result, Serverless is becoming increasingly popular, and new use cases have recently been proposed. Examples include video and image processing, Machine Learning inference and training, and data analytics. However, Serverless is currently supported by bloated virtualization stacks composed of a combination of virtual machines and/or containers, and language runtimes. None of these were to host lightweight and fast-executing Serverless workloads. To reduce the virtualization stack bloat, we propose Graalvisor, a virtualized polyglot language runtime capable of running multiple concurrent functions with minimal overhead. Graalvisor is designed to efficiently run lightweight and short-running Serverless functions, each running in a tiny execution environment that launches under 500 us. A single Graalvisor instance can run thousands of functions written in many different languages. By virtualizing a single runtime across many function invocations, Graalvisor reduces virtualization stack redundancy, resulting in lower memory consumption and less cold-starts. On a set of established Serverless functions, Graalvisor improves the throughput per memory (ops/sec/GB) on average by 170$\times$ for Java functions, 26.6$\times$ for JavaScript functions, and 2.07$\times$ for Python functions. When reproducing a public Serverless trace, Graalvisor reduces the overall memory footprint by 83% and reduces the tail latency (99 percentile) by 68%.
OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection

Compared to typical multi-sensor systems, monocular 3D object detection has attracted much attention due to its simple configuration. However, there is still a significant gap between LiDAR-based and monocular-based methods. In this paper, we find that the ill-posed nature of monocular imagery can lead to depth ambiguity. Specifically, objects with different depths can appear with the same bounding boxes and similar visual features in the 2D image. Unfortunately, the network cannot accurately distinguish different depths from such non-discriminative visual features, resulting in unstable depth training. To facilitate depth learning, we propose a simple yet effective plug-and-play module, <underline>O</underline>ne <underline>B</underline>ounding Box <underline>M</underline>ultiple <underline>O</underline>bjects (OBMO). Concretely, we add a set of suitable pseudo labels by shifting the 3D bounding box along the viewing frustum. To constrain the pseudo-3D labels to be reasonable, we carefully design two label scoring strategies to represent their quality. In contrast to the original hard depth labels, such soft pseudo labels with quality scores allow the network to learn a reasonable depth range, boosting training stability and thus improving final performance. Extensive experiments on KITTI and Waymo benchmarks show that our method significantly improves state-of-the-art monocular 3D detectors by a significant margin (The improvements under the moderate setting on KITTI validation set are <inline-formula> <tex-math notation="LaTeX">${1.82\sim 10.91\%}$ </tex-math></inline-formula> mAP in BEV and <inline-formula> <tex-math notation="LaTeX">${1.18\sim 9.36\%}$ </tex-math></inline-formula> mAP in 3D). Codes have been released at <uri>https://github.com/mrsempress/OBMO</uri>.
Settling the Reward Hypothesis

The reward hypothesis posits that,"all of what we mean by goals and purposes can be well thought of as maximization of the expected value of the cumulative sum of a received scalar signal (reward). "We aim to fully settle this hypothesis. This will not conclude with a simple affirmation or refutation, but rather specify completely the implicit requirements on goals and purposes under which the hypothesis holds.
Extreme accretion events:
 TDEs
 and
 changing‐look AGN

We present a review of the topics of X-ray stellar tidal disruption events (TDEs) and changing-look active galactic nuclei (AGN). Stars approaching a supermassive black hole (SMBH) can be tidally disrupted and accreted. TDEs were first discovered in the X-ray regime and appear as luminous, giant-amplitude flares from inactive galaxies. The early X-ray observations with ROSAT also established the extreme X-ray spectral softness of these events with temperatures of order 50-100 eV that continues to be seen in the majority of more recently identified events. While the majority of X-ray TDEs has been identified from {\it inactive} galaxies and some showed the highest amplitudes of variability recorded from galaxy cores (amplitudes exceeding factors of 1000--6000), a small fraction of {\it active} galactic nuclei (AGN) has been found to be highly variable as well. In AGN, this so-called changing-look phenomenon often comes with a strong change in the optical broad emission lines, leading to Seyfert-type changes between class 1 and class 2. These two forms of activity represent the extremes of variability among active and quiescent galaxies, and have opened up a new window on understanding accretion physics under extreme conditions. Finally, we introduce the term ``frozen-look AGN'' to describe systems that show constant line emission despite strong/dramatic changes in the observed ionizing continuum. These systems are best explained by strong changes of absorption along our line-of-sight.
ThreatKG: A Threat Knowledge Graph for Automated Open-Source Cyber Threat Intelligence Gathering and Management

Despite the increased adoption of open-source cyber threat intelligence (OSCTI) for acquiring knowledge about cyber threats, little effort has been made to harvest knowledge from a large number of unstructured OSCTI reports available in the wild (e.g., security articles, threat reports). These reports provide comprehensive threat knowledge in a variety of entities (e.g., IOCs, threat actors, TTPs) and relations, which, however, are hard to gather due to diverse report formats, large report quantities, and complex structures and nuances in the natural language report text. To bridge the gap, we propose ThreatKG, a system for automated open-source cyber threat knowledge gathering and management. ThreatKG automatically collects a large number of OSCTI reports from various sources, extracts high-fidelity threat knowledge, constructs a threat knowledge graph, and updates the knowledge graph by continuously ingesting new knowledge. To address multiple challenges, ThreatKG provides: (1) a hierarchical ontology for modeling a variety of threat knowledge entities and relations; (2) an accurate deep learning-based pipeline for threat knowledge extraction; (3) a scalable and extensible system architecture for threat knowledge graph construction, persistence, updating, and exploration. Evaluations on a large number of reports demonstrate the effectiveness of ThreatKG in threat knowledge gathering and management
Managing Large Dataset Gaps in Urban Air Quality Prediction: DCU-Insight-AQ at MediaEval 2022

Calculating an Air Quality Index (AQI) typically uses data streams from air quality sensors deployed at fixed locations and the calculation is a real time process. If one or a number of sensors are broken or offline, then the real time AQI value cannot be computed. Estimating AQI values for some point in the future is a predictive process and uses historical AQI values to train and build models. In this work we focus on gap filling in air quality data where the task is to predict the AQI at 1, 5 and 7 days into the future. The scenario is where one or a number of air, weather and traffic sensors are offline and explores prediction accuracy under such situations. The work is part of the MediaEval'2022 Urban Air: Urban Life and Air Pollution task submitted by the DCU-Insight-AQ team and uses multimodal and crossmodal data consisting of AQI, weather and CCTV traffic images for air pollution prediction.
A Pattern Discovery Approach to Multivariate Time Series Forecasting

Multivariate time series forecasting constitutes important functionality in cyber-physical systems, whose prediction accuracy can be improved significantly by capturing temporal and multivariate correlations among multiple time series. State-of-the-art deep learning methods fail to construct models for full time series because model complexity grows exponentially with time series length. Rather, these methods construct local temporal and multivariate correlations within subsequences, but fail to capture correlations among subsequences, which significantly affect their forecasting accuracy. To capture the temporal and multivariate correlations among subsequences, we design a pattern discovery model, that constructs correlations via diverse pattern functions. While the traditional pattern discovery method uses shared and fixed pattern functions that ignore the diversity across time series. We propose a novel pattern discovery method that can automatically capture diverse and complex time series patterns. We also propose a learnable correlation matrix, that enables the model to capture distinct correlations among multiple time series. Extensive experiments show that our model achieves state-of-the-art prediction accuracy.
Continual Mean Estimation Under User-Level Privacy

We consider the problem of continually releasing an estimate of the population mean of a stream of samples that is user-level differentially private (DP). At each time instant, a user contributes a sample, and the users can arrive in arbitrary order. Until now these requirements of continual release and user-level privacy were considered in isolation. But, in practice, both these requirements come together as the users often contribute data repeatedly and multiple queries are made. We provide an algorithm that outputs a mean estimate at every time instant t such that the overall release is user-level <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>-DP and has the following error guarantee: Denoting by <inline-formula> <tex-math notation="LaTeX">$m_{t}$ </tex-math></inline-formula> the maximum number of samples contributed by a user, as long as <inline-formula> <tex-math notation="LaTeX">$\tilde {\Omega }(1/\varepsilon)$ </tex-math></inline-formula> users have <inline-formula> <tex-math notation="LaTeX">$m_{t}/2$ </tex-math></inline-formula> samples each, the error at time t is <inline-formula> <tex-math notation="LaTeX">$\tilde {O}(1/\sqrt {t}+\sqrt {m}_{t}/t\varepsilon)$ </tex-math></inline-formula>. This is a universal error guarantee which is valid for all arrival patterns of the users. Furthermore, it (almost) matches the existing lower bounds for the single-release setting at all time instants when users have contributed equal number of samples.
CausalDialogue: Modeling Utterance-level Causality in Conversations

Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans. In this research, we examine user utterances as causes and generated responses as effects, recognizing that changes in a cause should produce a different effect. To further explore this concept, we have compiled and expanded upon a new dataset called CausalDialogue through crowd-sourcing. This dataset includes multiple cause-effect pairs within a directed acyclic graph (DAG) structure. Our analysis reveals that traditional loss functions struggle to effectively incorporate the DAG structure, leading us to propose a causality-enhanced method called Exponential Maximum Average Treatment Effect (ExMATE) to enhance the impact of causality at the utterance level in training neural conversation models. To evaluate the needs of considering causality in dialogue generation, we built a comprehensive benchmark on CausalDialogue dataset using different models, inference, and training methods. Through experiments, we find that a causality-inspired loss like ExMATE can improve the diversity and agility of conventional loss function and there is still room for improvement to reach human-level quality on this new dataset.
Proportional Control for Stochastic Regulation on Allocation of Multi-Robots

Any strategy used to distribute a robot ensemble over a set of sequential tasks is subject to inaccuracy due to robot-level uncertainties and environmental influences on the robots' behavior. We approach the problem of inaccuracy during task allocation by modeling and controlling the overall ensemble behavior. Our model represents the allocation problem as a stochastic jump process and we regulate the mean and variance of such a process. The main contributions of this paper are: Establishing a structure for the transition rates of the equivalent stochastic jump process and formally showing that this approach leads to decoupled parameters that allow us to adjust the first- and second-order moments of the ensemble distribution over tasks, which gives the flexibility to decrease the variance in the desired final distribution. This allows us to directly shape the impact of uncertainties on the group allocation over tasks. We introduce a detailed procedure to design the gains to achieve the desired mean and show how the additional parameters impact the covariance matrix, which is directly associated with the degree of task allocation precision. Our simulation and experimental results illustrate the successful control of several robot ensembles during task allocation.
Uno-Qa: an Unsupervised Anomaly-Aware Framework with Test-Time Clustering for Octa Image Quality Assessment

Medical image quality assessment (MIQA) is a vital prerequisite in various medical image analysis applications. Most existing MIQA algorithms are fully supervised that request a large amount of annotated data. However, annotating medical images is time-consuming and labor-intensive. In this paper, we propose an unsupervised anomaly-aware framework with test-time clustering for optical coherence tomography angiography (OCTA) image quality assessment in a setting wherein only a set of high-quality samples are accessible in the training phase. Specifically, a feature-embedding-based low-quality representation module is proposed to quantify the quality of OCTA images and then to discriminate between outstanding quality and non-outstanding quality. Within the non-outstanding quality class, to further distinguish gradable images from ungradable ones, we perform dimension reduction and clustering of multi-scale image features extracted by the trained OCTA quality representation network. Extensive experiments are conducted on one publicly accessible dataset sOCTA-3×3-10k, with superiority of our proposed framework being successfully established.
Emotion Selectable End-to-End Text-based Speech Editing

Text-based speech editing allows users to edit speech by intuitively cutting, copying, and pasting text to speed up the process of editing speech. In the previous work, CampNet (context-aware mask prediction network) is proposed to realize text-based speech editing, significantly improving the quality of edited speech. This paper aims at a new task: adding emotional effect to the editing speech during the text-based speech editing to make the generated speech more expressive. To achieve this task, we propose Emo-CampNet (emotion CampNet), which can provide the option of emotional attributes for the generated speech in text-based speech editing and has the one-shot ability to edit unseen speakers' speech. Firstly, we propose an end-to-end emotion-selectable text-based speech editing model. The key idea of the model is to control the emotion of generated speech by introducing additional emotion attributes based on the context-aware mask prediction network. Secondly, to prevent the emotion of the generated speech from being interfered by the emotional components in the original speech, a neutral content generator is proposed to remove the emotion from the original speech, which is optimized by the generative adversarial framework. Thirdly, two data augmentation methods are proposed to enrich the emotional and pronunciation information in the training set, which can enable the model to edit the unseen speaker's speech. The experimental results that 1) Emo-CampNet can effectively control the emotion of the generated speech in the process of text-based speech editing; And can edit unseen speakers' speech. 2) Detailed ablation experiments further prove the effectiveness of emotional selectivity and data augmentation methods. The demo page is available at https://hairuo55.github. io/Emo-CampNet/
ATLAS: An IoT Architecture and Secure Open-source Networking Stack for Anonymous Localization and Tracking Using Smartphones and Bluetooth Beacons

Bluetooth (BT) has revolutionized close-range communication enabling smart capabilities in everyday devices through wireless technology. One of the most important sub-domains of Internet-of-Things (IoT) specializes in the usage of BT technologies to develop smart homes and environments, which include hospitals, buildings, shopping facilities, etc. to offer a wide-range of features, like instantaneous and remote access to ventilation, lighting, security, localization, and tracking. However, the deployment of such features in smart infrastructures are typically unaccompanied by appropriate security measures that safeguard the data and protect its users. Towards this, we propose the ATLAS framework, which is composed of our novel IoT architecture and secure networking stack that can be used to anonymously localize and track smartphones and wearables by deploying multiple Bluetooth Low Energy (BLE) beacons across the environment. The proposed networking stack enables varying levels of encryption across all layers of the communication stack to ensure an easy-to-adopt, secure-by-design network architecture. We also deploy a novel data transformation and fingerprinting-based localization algorithm, which is highly effective in localizing user devices within a given area. The ATLAS framework is open-sourced at https://atlas-tuw.sourceforge. io to enable wide-spread adoption and further research and development.
Parsel: A Unified Natural Language Framework for Algorithmic Reasoning

Despite recent success in large language model (LLM) reasoning, LLMs still struggle with hierarchical multi-step reasoning like generating complex programs. In these cases, humans often start with a high-level algorithmic design and implement each part gradually. We introduce Parsel 2 , a framework enabling automatic implementation and validation of complex algorithms with code LLMs, based on hierarchical function descriptions in natural language. Parsel can be used across domains requiring hierarchical reasoning, e.g. code synthesis, theorem proving, and robotic planning. We demonstrate Parsel’s capabilities by using it to generate complex programs that cannot currently be automatically implemented from one description and backtranslating Python programs in the APPS dataset. Beyond modeling capabilities, Parsel allows problem-solving with high-level algorithmic designs, beneﬁting both students and professional programmers.
What to Read in a Contract? Party-Specific Summarization of Important Obligations, Entitlements, and Prohibitions in Legal Documents

Legal contracts, such as employment or lease agreements, are important documents as they govern the obligations and entitlements of the various contracting parties. However, these documents are typically long and written in legalese resulting in lots of manual hours spent in understanding them. In this paper, we address the task of summarizing legal contracts for each of the contracting parties, to enable faster reviewing and improved understanding of them. Speciﬁcally, we collect a dataset consisting of pairwise importance comparison annotations by legal experts for ∼ 293 K sentence pairs from lease agreements. We pro-pose a novel extractive summarization system to automatically produce a summary consisting of the most important obligations, entitle-ments, and prohibitions in a contract. It consists of two modules: (1) a content categorizer to identify sentences containing each of the categories ( i.e. , obligation , entitlement , and pro-hibition ) for a party, and (2) an importance ranker to compare the importance among sentences of each category for a party to obtain a ranked list. The ﬁnal summary is produced by selecting the most important sentences of a category for each of the parties. We demonstrate the effectiveness of our proposed system by comparing it against several text ranking baselines via automatic and human evaluation.
Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation

Multimodal machine translation (MMT) aims to improve translation quality by incorporating information from other modalities, such as vision. Previous MMT systems mainly focus on better access and use of visual information and tend to validate their methods on image-related datasets. These studies face two challenges. First, they can only utilize triple data (bilingual texts with images), which is scarce; second, current benchmarks are relatively restricted and do not correspond to realistic scenarios. Therefore, this paper correspondingly establishes new methods and new datasets for MMT. First, we propose a framework 2/3-Triplet with two new approaches to enhance MMT by utilizing large-scale non-triple data: monolingual image-text data and parallel text-only data. Second, we construct an English-Chinese {e}-commercial {m}ulti{m}odal {t}ranslation dataset (including training and testing), named EMMT, where its test set is carefully selected as some words are ambiguous and shall be translated mistakenly without the help of images. Experiments show that our method is more suitable for real-world scenarios and can significantly improve translation performance by using more non-triple data. In addition, our model also rivals various SOTA models in conventional multimodal translation benchmarks.