Miracle-dz/UniFAR-FTUs huggingface dataset:
Operations in Lapan's class B*.

The non-constant holomorphic function /, defined in the unit disk, is in class ̂ * if for each point of a dense subset of the unit circle / is bounded on an arc which ends at the point. The class ̂ * is investigated with respect to its closure properties under certain elementary operations. The first theorem shows that the sum or product of functions in ̂ * need not be in ̂ *. The next theorem proves that the exponential of a function in J** must be in ^*. Theorems 3 and 4 both rely on a combination of a repeated use of Mergelyan's Approximation Theorem and a technique of Barth and Schneider. Theorem 3 shows that if / is in ̂ * and if we could define a holomorphic function log / it would still not have to be in 38*. Theorem 4 demonstrates that the integral of a function in ^* does not have to be in #*. Theorem 5 establishes the same result for the derivative.
Finding neural signatures for obesity through feature selection on source-localized EEG

Obesity is a serious issue in the modern society and is often associated to significantly reduced quality of life. Current research conducted to explore obesity-related neurological evidences using electroencephalography (EEG) data are limited to traditional approaches. In this study, we developed a novel machine learning model to identify brain networks of obese females using alpha band functional connectivity features derived from EEG data. An overall classification accuracy of 0.937 is achieved. Our finding suggests that the obese brain is characterized by a dysfunctional network in which the areas that responsible for processing self-referential information and environmental context information are impaired.
A PREDICTOR-CORRECTOR SCHEME FOR VORTEX IDENTIFICATION

A new algorithm for identifying and characterizing vortices in complex flows is presented. The scheme uses both the vorticity and pressure fields. A skeleton line along the center of a vortex is produced by a two-step predictor-corrector scheme. The technique uses the vector field to move in the direction of the skeleton line and the scalar field to correct the location in the plane perpendicular to the skeleton line. A general vortex cross section can be concisely defined with five parameters at each point along the skeleton line. The details of the method and examples of its use are discussed.
Binsec/Rel: Symbolic Binary Analyzer for Security with Applications to Constant-Time and Secret-Erasure

This article tackles the problem of designing efficient binary-level verification for a subset of information flow properties encompassing constant-time and secret-erasure. These properties are crucial for cryptographic implementations but are generally not preserved by compilers. Our proposal builds on relational symbolic execution enhanced with new optimizations dedicated to information flow and binary-level analysis, yielding a dramatic improvement over prior work based on symbolic execution. We implement a prototype, Binsec/Rel, for bug-finding and bounded-verification of constant-time and secret-erasure and perform extensive experiments on a set of 338 cryptographic implementations, demonstrating the benefits of our approach. Using Binsec/Rel, we also automate two prior manual studies on preservation of constant-time and secret-erasure by compilers for a total of 4,148 and 1,156 binaries, respectively. Interestingly, our analysis highlights incorrect usages of volatile data pointer for secret-erasure and shows that scrubbing mechanisms based on volatile function pointers can introduce additional register spilling that might break secret-erasure. We also discovered that gcc -O0 and backend passes of clang introduce violations of constant-time in implementations that were previously deemed secure by a state-of-the-art constant-time verification tool operating at LLVM level, showing the importance of reasoning at binary level.
Multilevel Richardson-Romberg and Importance Sampling in Derivative Pricing

In this paper, we propose and analyze a novel combination of multilevel Richardson-Romberg (ML2R) and importance sampling algorithm, with the aim of reducing the overall computational time, while achieving desired root-mean-squared error while pricing. We develop an idea to construct the Monte-Carlo estimator that deals with the parametric change of measure. We rely on the Robbins-Monro algorithm with projection, in order to approximate optimal change of measure parameter, for various levels of resolution in our multilevel algorithm. Furthermore, we propose incorporating discretization schemes with higher-order strong convergence, in order to simulate the underlying stochastic differential equations (SDEs) thereby achieving better accuracy. In order to do so, we study the Central Limit Theorem for the general multilevel algorithm. Further, we study the asymptotic behavior of our estimator, thereby proving the Strong Law of Large Numbers. Finally, we present numerical results to substantiate the efﬁcacy of our developed algorithm.
Inferring Tabular Analysis Metadata by Infusing Distribution and Knowledge Information

Many data analysis tasks heavily rely on a deep understanding of tables (multi-dimensional data). Across the tasks, there exist comonly used metadata attributes of table fields / columns. In this paper, we identify four such analysis metadata: Measure/dimension dichotomy, common field roles, semantic field type, and default aggregation function. While those metadata face challenges of insufficient supervision signals, utilizing existing knowledge and understanding distribution. To inference these metadata for a raw table, we propose our multi-tasking Metadata model which fuses field distribution and knowledge graph information into pre-trained tabular models. For model training and evaluation, we collect a large corpus ( ∼ 582k tables from private spreadsheet and public tabular datasets) of analysis metadata by using diverse smart supervisions from downstream tasks. Our best model has accuracy = 98%, hit rate at top-1 > 67%, accuracy > 80%, and accuracy = 88% for the four analysis metadata inference tasks, respectively. It outperforms a series of baselines that are based on rules, traditional machine learning methods, and pre-trained tabular models. Analysis metadata models are deployed in a popular data analysis product, helping downstream intelligent features such as insights mining, chart / pivot table recommendation
A Low-Cost Multi-Agent System for Physical Security in Smart Buildings

Modern organizations face numerous physical security threats, from fire hazards to more intricate concerns regarding surveillance and unauthorized personnel. Conventional standalone fire and intrusion detection solutions must be installed and maintained independently, which leads to high capital and operational costs. Nonetheless, due to recent developments in smart sensors, computer vision techniques, and wireless communication technologies, these solutions can be integrated in a modular and low-cost manner. This work introduces Integrated Physical Security System (IP2S), a multi-agent system capable of coordinating diverse Internet of Things (IoT) sensors and actuators for an efficient mitigation of multiple physical security events. The proposed system was tested in a live case study that combined fire and intrusion detection in an industrial shop floor environment with four different sectors, two surveillance cameras, and a firefighting robot. The experimental results demonstrate that the integration of several events in a single automated system can be advantageous for the security of smart buildings, reducing false alarms and delays.
Semi-Centralised Multi-Agent Reinforcement Learning with Policy-Embedded Training

Centralised training (CT) is the basis for many popular multi-agent reinforcement learning (MARL) methods because it allows agents to quickly learn high-performing policies. However, CT relies on agents learning from one-off observations of other agents’ actions at a given state. Because MARL agents explore and update their policies during training, these observations often provide poor predictions about other agents’ behaviour and the expected return for a given action. CT methods therefore suffer from high variance and error-prone estimates, harming learning. CT methods also suffer from explosive growth in complexity due to the reliance on global observations, unless strong factorisation restrictions are imposed (e.g., monotonic reward functions for QMIX). We address these challenges with a new semi-centralised MARL framework that performs policy-embedded training and decentralised execution. Our method, policy embedded reinforcement learning algorithm (PERLA), is an enhancement tool for Actor-Critic MARL algorithms that leverages a novel parameter sharing protocol and policy embedding method to maintain estimates that account for other agents’ behaviour. Our theory proves PERLA dramatically reduces the variance in value estimates. Unlike various CT methods, PERLA, which seamlessly adopts MARL algorithms, scales easily with the number of agents without the need for restrictive factorisation assumptions. We demonstrate PERLA’s superior empirical performance and efﬁcient scaling in benchmark environments including Star-Craft Micromanagement II and Multi-agent Mujoco .
A Review of: “Comparing Clinical Measurement Methods: A Practical Guide, by B. Carstensen”

The basic learning assumption in this book is that concepts regarding comparison of clinical measurement methods will be best understood in the context of the underlying statistical models that could generate the measurements. The intent is to focus on the understanding of parameters in the model as well as their practical interpretation. Chapter 2 provides an introduction to method comparisons, introducing some of the basic issues such as one versus replicate measures per method, exchangeable versus linked replicates, and comparisons of more than two methods. No formal models are introduced in this chapter. Basic methods to deal with comparison of clinical measurements are contained in chapters 4 and 5. Chapter 4 presents approaches when there are only two methods to compare and only one measurement per method. Limits of agreement and prediction methods are presented. This chapter is the first to introduce statistical models. Methods to deal with violation of assumptions of the basic model and limits of agreement (nonconstant mean or variance over the range of measurements) are presented. Chapter 5 extends the material to replicate measurements. Methods to handle exchangeable versus linked replicates are developed. Random-effect models are introduced and choices between fixed and random effects are discussed. Constant bias and variance are assumed in this chapter. The central and most important chapter in the book is chapter 7. It is here that the most general model is proposed. All previous models are shown to be special cases of this model. Parameterization of the mean, reporting variance components, and comparison with a gold standard are presented. Estimation methods are presented, including alternating regression and Bayesian methods using BUGS. Issues with overfitting and degrees of freedom are also discussed, particularly in the context of estimation. A fairly extensive example is presented that demonstrates many of the methods and choices. Chapter 8 discusses transformations, which had been introduced earlier as a way to deal with nonconstant means and variances. Specific examples of the log transformation (interpretable in terms of ratios) and the logit transformation (particularly useful when data are percentages) are illustrated. Carstensen suggests that issues with lack of interpretability of transformed data can be resolved by constructing prediction limits using a model for the transformed data, and then providing a plot that back-transforms to the original scale. Chapter 10 presents some measures of association and agreement found in the literature. Carstensen shows that these measures are ratios of subsets of the variance
Accidental persistent spin textures in the proustite mineral family

Persistent spin textures (PSTs) in momentum space have the potential to enable spintronic devices which are currently limited by low spin lifetimes in nonmagnetic spin-orbit coupled materials. We perform a first-principles study on the proustite mineral family, Ag$_3$BQ$_3$} (B=As,\,Sb; Q=S,\,Se), and show these chalcogenides exhibit a non-symmetry protected PST, which we refer to as symmetry-assisted PSTs. Chemical substitution can be used to tune the PST quality and properties, e.g., spin lifetime, and we find that a Rashba anisotropy criterion correlates with the PST area and spin lifetime for two of the three proustites examined. Last, we show that a first-order effective SOC Hamiltonian, often used in two-dimensional systems, is insufficient to describe the PST state in all proustites, suggesting that higher order models are necessary to fully describe PSTs in bulk three-dimensional materials.
Universal Fourier Attack for Time Series

A wide variety of adversarial attacks have been proposed and explored using image and audio data. These attacks are notoriously easy to generate digitally when the attacker can directly manipulate the input to a model, but are much more difficult to implement in the real-world. In this paper we present a universal, time invariant attack for general time series data such that the attack has a frequency spectrum primarily composed of the frequencies present in the original data. The universality of the attack makes it fast and easy to implement as no computation is required to add it to an input, while time invariance is useful for real-world deployment. Additionally, the frequency constraint ensures the attack can withstand filtering. We demonstrate the effectiveness of the attack in two different domains, speech recognition and unintended radiated emission, and show that the attack is robust against common transform-and-compare defense pipelines.
PCDNF: Revisiting Learning-based Point Cloud Denoising via Joint Normal Filtering

Point cloud denoising is a fundamental and challenging problem in geometry processing. Existing methods typically involve direct denoising of noisy input or filtering raw normals followed by point position updates. Recognizing the crucial relationship between point cloud denoising and normal filtering, we re-examine this problem from a multitask perspective and propose an end-to-end network called PCDNF for joint normal filtering-based point cloud denoising. We introduce an auxiliary normal filtering task to enhance the network's ability to remove noise while preserving geometric features more accurately. Our network incorporates two novel modules. First, we design a shape-aware selector to improve noise removal performance by constructing latent tangent space representations for specific points, taking into account learned point and normal features as well as geometric priors. Second, we develop a feature refinement module to fuse point and normal features, capitalizing on the strengths of point features in describing geometric details and normal features in representing geometric structures, such as sharp edges and corners. This combination overcomes the limitations of each feature type and better recovers geometric information. Extensive evaluations, comparisons, and ablation studies demonstrate that the proposed method outperforms state-of-the-art approaches in both point cloud denoising and normal filtering.
ARST: auto-regressive surgical transformer for phase recognition from laparoscopic videos

ABSTRACT Phase recognition plays an essential role for surgical workflow analysis in computer assisted intervention. Transformer, originally proposed for sequential data modelling in natural language processing, has been successfully applied to surgical phase recognition. Existing works based on transformer mainly focus on modeling attention dependency, without introducing auto-regression. In this work, an Auto-Regressive Surgical Transformer, referred as ARST, is first proposed for on-line surgical phase recognition from laparoscopic videos, modeling the inter-phase correlation implicitly by conditional probability distribution. To reduce inference bias and to enhance phase consistency, we further develop a consistency constraint inference strategy based on auto-regression. We conduct comprehensive validations on a well-known public dataset Cholec80. Experimental results show that our method outperforms the state-of-the-art methods both quantitatively and qualitatively, and achieves an inference rate of 66 frames per second (fps).
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering over Knowledge Graphs

Answering natural language questions on knowledge graphs (KGQA) remains a great challenge in terms of understanding complex questions via multi-hop reasoning. Previous efforts usually exploit large-scale entity-related text corpus or knowledge graph (KG) embeddings as auxiliary information to facilitate answer selection. However, the rich semantics implied in off-the-shelf relation paths between entities is far from well explored. This paper proposes improving multi-hop KGQA by exploiting relation paths’ hybrid semantics. Specifically, we integrate explicit textual information and implicit KG structural features of relation paths based on a novel rotate-and-scale entity link prediction framework. Extensive experiments on three existing KGQA datasets demonstrate the superiority of our method, especially in multi-hop scenarios. Further investigation confirms our method’s systematical coordination between questions and relation paths to identify answer entities.
Robust joint modelling of sparsely observed paired functional data

A reduced‐rank mixed‐effects model is developed for robust modelling of sparsely observed paired functional data. In this model, the curves for each functional variable are summarized using a few functional principal components, and the association of the two functional variables is modelled through the association of the principal component scores. A multivariate‐scale mixture of normal distributions is used to model the principal component scores and the measurement errors in order to handle outlying observations and achieve robust inference. The mean functions and principal component functions are modelled using splines, and roughness penalties are applied to avoid overfitting. An EM algorithm is developed for computation of model fitting and prediction. A simulation study shows that the proposed method outperforms an existing method, which is not designed for robust estimation. The effectiveness of the proposed method is illustrated through an application of fitting multiband light curves of Type Ia supernovae.
A Discussion of Discrimination and Fairness in Insurance Pricing

Indirect discrimination is an issue of major concern in algorithmic models. This is par-ticularly the case in insurance pricing where protected policyholder characteristics are not allowed to be used for insurance pricing. Simply disregarding protected policyholder information is not an appropriate solution because this still allows for the possibility of inferring the protected characteristics from the non-protected ones. This leads to so-called proxy or indirect discrimination. Though proxy discrimination is qualitatively diﬀerent from the group fairness concepts in machine learning, these group fairness concepts are proposed to ‘smooth out’ the impact of protected characteristics in the calculation of insurance prices. The purpose of this note is to share some thoughts about group fairness concepts in the light of insurance pricing and to discuss their implications. We present a statistical model that is free of proxy discrimination, thus, unproblematic from an insurance pricing point of view. However, we ﬁnd that the canonical price in this statistical model does not satisfy any of the three most popular group fairness axioms. This seems puzzling and we welcome feedback on our example and on the usefulness of these group fairness axioms for non-discriminatory insurance pricing.
Addressing Hidden Imperfections in Online Experimentation

Technology companies are increasingly using randomized controlled trials (RCTs) as part of their development process. Despite having fine control over engineering systems and data instrumentation, these RCTs can still be imperfectly executed. In fact, online experimentation suffers from many of the same biases seen in biomedical RCTs including opt-in and user activity bias, selection bias, non-compliance with the treatment, and more generally, challenges in the ability to test the question of interest. The result of these imperfections can lead to a bias in the estimated causal effect, a loss in statistical power, an attenuation of the effect, or even a need to reframe the question that can be answered. This paper aims to make practitioners of experimentation more aware of imperfections in technology-industry RCTs, which can be hidden throughout the engineering stack or in the design process.
DecVi: Adaptive Video Conferencing on Open Peer-to-Peer Networks

Video conferencing has become the preferred way of interacting virtually. Current video conferencing applications, like Zoom, Teams or WebEx, are centralized, cloud-based platforms whose performance crucially depends on the proximity of clients to their data centers. Clients from low-income countries are particularly affected as most data centers from major cloud providers are located in economically advanced nations. Centralized conferencing applications also suffer from occasional outages and are embattled by serious privacy violation allegations. In recent years, decentralized video conferencing applications built over p2p networks and incentivized through blockchain are becoming popular. A key characteristic of these networks is their openness: anyone can host a media server on the network. The reason, however, also leads to a security problem: a server may obfuscate its true location in order to gain an unfair business advantage. We propose DecVi, a decentralized multicast tree construction protocol that adaptively discovers efficient tree structures based on an exploration-exploitation framework. DecVi is motivated by the combinatorial multi-armed bandit problem and uses a succinct learning model to compute effective actions. Despite operating in a multi-agent setting with each server having only limited knowledge of the global network and without cooperation among servers, experimentally we show DecVi achieves similar quality-of-experience compared to a centralized globally optimal algorithm while achieving higher reliability and flexibility.
Kraken: A Direct Event/Frame-Based Multi-sensor Fusion SoC for Ultra-Efficient Visual Processing in Nano-UAVs

Small-size unmanned aerial vehicles (UAV) have the potential to dramatically increase safety and reduce cost in applications like critical infrastructure maintenance and post-disaster search and rescue. Many scenarios require UAVs to shrink toward nano and pico-size form factors. The key open challenge to achieve true autonomy on Nano-UAVs is to run complex visual tasks like object detection, tracking, navigation and obstacle avoidance fully on board, at high speed and robustness, under tight payload and power constraints. With the Kraken SoC, fabricated in 22nm FDX technology, we demonstrate a multi-visual-sensor capability exploiting both event-based and BW/RGB imagers, combining their output for multi-functional visual tasks previously impossible on a single low-power chip for Nano-UAVs. Kraken is an ultra-low-power, heterogeneous SoC architecture integrating three acceleration engines and a vast set of peripherals to enable efficient interfacing with standard frame-based sensors and novel event-based DVS. Kraken enables highly sparse event-driven sub-uJ/inf SNN inference on a dedicated neuromorphic energy-proportional accelerator. Moreover, it can perform frame-based inference by combining a 1.8TOp\s\W 8-cores RISC-V processor cluster with mixed-precision DNN extensions with a 1036TOp\s\W} TNN accelerator.