Miracle-dz/UniFAR-FTUs huggingface dataset:
Cooperative Self-training of Machine Reading Comprehension

Pretrained language models have significantly improved the performance of downstream language understanding tasks, including extractive question answering, by providing high-quality contextualized word embeddings. However, training question answering models still requires large amounts of annotated data for specific domains. In this work, we propose a cooperative self-training framework, RGX, for automatically generating more non-trivial question-answer pairs to improve model performance. RGX is built upon a masked answer extraction task with an interactive learning environment containing an answer entity Recognizer, a question Generator, and an answer eXtractor. Given a passage with a masked entity, the generator generates a question around the entity, and the extractor is trained to extract the masked entity with the generated question and raw texts. The framework allows the training of question generation and answering models on any text corpora without annotation. We further leverage a self-training technique to improve the performance of both question generation and answer extraction models. Experiment results show that RGX outperforms the state-of-the-art (SOTA) pretrained language models and transfer learning approaches on standard question-answering benchmarks, and yields the new SOTA performance under given model size and transfer learning settings.
Creation and Analysis of an International Corpus of Privacy Laws

The landscape of privacy laws and regulations around the world is complex and ever-changing. National and super-national laws, agreements, decrees, and other government-issued rules form a patchwork that companies must follow to operate internationally. To examine the status and evolution of this patchwork, we introduce the Government Privacy Instructions Corpus, or GPI Corpus, of 1,043 privacy laws, regulations, and guidelines, covering 182 jurisdictions. This corpus enables a large-scale quantitative and qualitative examination of legal foci on privacy. We examine the temporal distribution of when GPIs were created and illustrate the dramatic increase in privacy legislation over the past 50 years, although a finer-grained examination reveals that the rate of increase varies depending on the personal data types that GPIs address. Our exploration also demonstrates that most privacy laws respectively address relatively few personal data types, showing that comprehensive privacy legislation remains rare. Additionally, topic modeling results show the prevalence of common themes in GPIs, such as finance, healthcare, and telecommunications. Finally, we release the corpus to the research community to promote further study. 1
Social-aware Cooperative Caching in Fog Radio Access Networks

In this paper, the cooperative caching problem in fog radio access networks (F-RANs) is investigated to jointly optimize the transmission delay and energy consumption. Exploiting the potential social relationships among fog access points (F-APs), we firstly propose a clustering scheme based on hedonic coalition game (HCG) to improve the potential cooperation gain. Then, considering that the optimization problem is non-deterministic polynomial hard (NP-hard), we further propose an improved firefly algorithm (FA) based cooperative caching scheme, which utilizes a mutation strategy based on local content popularity to avoid pre-mature convergence. Simulation results show that our proposed scheme can effectively reduce the content transmission delay and energy consumption in comparison with the baselines.
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting

Recent studies have shown great promise in applying graph neural networks for multivariate time series forecasting, where the interactions of time series are described as a graph structure and the variables are represented as the graph nodes. Along this line, existing methods usually assume that the graph structure (or the adjacency matrix), which determines the aggregation manner of graph neural network, is fixed either by definition or self-learning. However, the interactions of variables can be dynamic and evolutionary in real-world scenarios. Furthermore, the interactions of time series are quite different if they are observed at different time scales. To equip the graph neural network with a flexible and practical graph structure, in this paper, we investigate how to model the evolutionary and multi-scale interactions of time series. In particular, we first provide a hierarchical graph structure cooperated with the dilated convolution to capture the scale-specific correlations among time series. Then, a series of adjacency matrices are constructed under a recurrent manner to represent the evolving correlations at each layer. Moreover, a unified neural network is provided to integrate the components above to get the final prediction. In this way, we can capture the pair-wise correlations and temporal dependency simultaneously. Finally, experiments on both single-step and multi-step forecasting tasks demonstrate the superiority of our method over the state-of-the-art approaches.
The Case for RISP: A Reduced Instruction Spiking Processor

In this paper, we introduce RISP, a reduced instruction spiking processor. While most spiking neuroprocessors are based on the brain, or notions from the brain, we present the case for a spiking processor that simplifies rather than complicates. As such, it features discrete integration cycles, configurable leak, and little else. We present the computing model of RISP and highlight the benefits of its simplicity. We demonstrate how it aids in developing hand built neural networks for simple computational tasks, detail how it may be employed to simplify neural networks built with more complicated machine learning techniques, and demonstrate how it performs similarly to other spiking neurprocessors.
Spatial Positioning Token (SPToken) for Smart Parking

In this paper, we describe an approach to guide drivers searching for a parking space (PS). The proposed system suggests a sequence of routes that drivers should traverse in order to maximise the expected likelihood of finding a PS and minimise the travel distance. This system is built on our recent architecture SPToken, which combines both Distributed Ledger Technology (DLT) and Reinforcement Learning (RL) to realise a system for the estimation of an unknown distribution without disturbing the environment. For this, we use a number of virtual tokens that are passed from vehicle to vehicle to enable a massively parallelised RL system that estimates the best route for a given origin-destination (OD) pair, using crowdsourced information from participant vehicles. Additionally, a moving window with reward memory mechanism is included to better cope with non-stationary environments. Simulation results are given to illustrate the efficacy of our system.
Nowhere to Hide: Detecting Obfuscated Fingerprinting Scripts

As the web moves away from stateful tracking, browser fingerprinting is becoming more prevalent. Unfortunately, existing approaches to detect browser fingerprinting do not take into account potential evasion tactics such as code obfuscation. To address this gap, we investigate the robustness of a state-of-the-art fingerprinting detection approach against various off-the-shelf obfuscation tools. Overall, we find that the combination of static and dynamic analysis is robust against different types of obfuscation. While some obfuscators are able to induce false negatives in static analysis, dynamic analysis is still able detect these cases. Since obfuscation does not induce significant false positives, the combination of static and dynamic analysis is still able to accurately detect obfuscated fingerprinting scripts.
Proximity-induced superconductivity generated by thin films: Effects of Fermi surface mismatch and disorder in the superconductor

We investigate the eﬀects of disorder characterising a superconducting thin ﬁlm on the proximity-induced superconductivity generated by the ﬁlm (in, e.g., a semiconductor) based on the exact numerical analysis of a three-dimensional microscopic model. To make the problem numerically tractable, we use a recursive Green’s function method in combination with a “patching approach” that exploits the short-range nature of the interface Green’s function in the presence of disorder. As a result of the Fermi surface mismatch between the superconductor (SC) and the semiconductor (SM) in combination with the conﬁnement-induced quantization of the transverse SC modes, the proximity eﬀect induced by a clean SC ﬁlm is typically one to three orders of magnitude smaller that the corresponding quantity for a bulk SC and exhibits huge thickness-dependent variations. The presence of disorder has competing eﬀects: on the one hand it enhances the proximity-induced superconductivity and suppresses its strong thickness dependence, on the other hand it generates proximity-induced eﬀective disorder in the SM. The eﬀect of proximity-induced disorder on the topological superconducting phase and the associated Majorana modes is studied nonperturbatively.
The Galactic population of canonical pulsars

Context. Current wisdom suggests that the observed population of neutron stars are manifestations of their birth scenarios and their thermal and magnetic ﬁeld evolution. Neutron stars can be observed at various wavebands as pulsars, and radio pulsars represent by far the largest population of neutron stars. Aims. In this paper, we aim to constrain the observed population of the canonical neutron star period, its magnetic ﬁeld, and its spatial distribution at birth in order to understand the radio and high-energy emission processes in a pulsar magnetosphere. For this purpose we design a population synthesis method, self-consistently taking into account the secular evolution of a force-free magnetosphere and the magnetic ﬁeld decay. Methods. We generated a population of pulsars and evolved them from their birth to the present time, using the force-free approxima- tion. We assumed a given initial distribution for the spin period, surface magnetic ﬁeld, and spatial Galactic location. Radio emission properties were accounted for by the polar cap geometry, whereas the gamma-ray emission was assumed to be produced within the striped wind model. Results. We ﬁnd that a decaying magnetic ﬁeld gives better agreement with observations compared to a constant magnetic ﬁeld model. Starting from an initial mean magnetic ﬁeld strength of B = 2 . 5 × 10 8 T with a characteristic decay timescale of 4 . 6 × 10 5 yr, a neutron star birth rate of 1 / 70 yr and a mean initial spin period of 60 ms, we ﬁnd that the force-free model satisfactorily reproduces the distribution of pulsars in the P − ˙ P diagram with simulated populations of radio-loud, radio-only, and radio quiet gamma-ray pulsars similar to the observed populations.
Defining a data-driven maintenance policy: an application to an oil refinery plant

Purpose The purpose of this paper is developing a data-driven maintenance policy through the analysis of vast amount of data and its application to an oil refinery plant. The maintenance policy, analyzing data regarding sub-plant stoppages and components breakdowns within a defined time interval, supports the decision maker in determining whether it is better to perform predictive maintenance or corrective interventions on the basis of probability measurements. Design/methodology/approach The formalism applied to pursue this aim is association rules mining since it allows to discover the existence of relationships between sub-plant stoppages and components breakdowns. Findings The application of the maintenance policy to a three-year case highlighted that the extracted rules depend on both the kind of stoppage and the timeframe considered, hence different maintenance strategies are suggested. Originality/value This paper demonstrates that data mining (DM) tools, like association rules (AR), can provide a valuable support to maintenance processes. In particular, the described policy can be generalized and applied both to other refineries and to other continuous production systems.
DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning Systems

While notable progress has been made in specifying and learning objectives for general cyber-physical systems, applying these methods to distributed multi-agent systems still pose significant challenges. Among these are the need to (a) craft specification primitives that allow expression and interplay of both local and global objectives, (b) tame explosion in the state and action spaces to enable effective learning, and (c) minimize coordination frequency and the set of engaged participants for global objectives. To address these challenges, we propose a novel specification framework that allows natural composition of local and global objectives used to guide training of a multi-agent system. Our technique enables learning expressive policies that allow agents to operate in a coordination-free manner for local objectives, while using a decentralized communication protocol for enforcing global ones. Experimental results support our claim that sophisticated multi-agent distributed planning problems can be effectively realized using specification-guided learning.
Fundamental, technical and external factors induced positive and negative extreme events in stock market

Sporadic large ﬂuctuations in stock price are seen in the stock market. Such large ﬂuctuations occur due to three important factors: (a) the signiﬁcant change in the fundamental parameters like excellent or bad result; (b) the formation of special technical setup in the stock price like double-bottom and (c) external factors like war. These factors may lead to occasional and rare upsurge or crash of signiﬁcant height in the stock price, and such upsurge or crash is termed as positive or negative extreme event (EE), respectively. In this work, we have identiﬁed EE in the stock price of selected companies from global stock markets due to the above factors in minute, daily, weekly time-scales. Augmented Dickey-Fuller and degree of nonstationarity tests show that the stock price time-series is nonstationary. Subsequently, we applied Hilbert-Huang transformation to identify EE. The analysis shows that the instantaneous energy ( IE ) concentration in the stock price is very high during an EE with IE > E µ + 4 σ, where E µ and σ are the mean energy and standard deviation of energy, respectively. The analysis shows that investor can gain or lose a signiﬁcant amount of their capital due to these events. Hence, identiﬁcation of the EEs in stock market are important as it helps the investor and trader to take rational decisions during such crisis. The investors should carefully monitor the factors that lead to EEs for entry or exit strategy in the market.
How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection

Model stealing attacks present a dilemma for public machine learning APIs. To protect financial investments, companies may be forced to withhold important information about their models that could facilitate theft, including uncertainty estimates and prediction explanations. This compromise is harmful not only to users but also to external transparency. Model stealing defenses seek to resolve this dilemma by making models harder to steal while preserving utility for benign users. However, existing defenses have poor performance in practice, either requiring enormous computational overheads or severe utility trade-offs. To meet these challenges, we present a new approach to model stealing defenses called gradient redirection. At the core of our approach is a provably optimal, efficient algorithm for steering an adversary's training updates in a targeted manner. Combined with improvements to surrogate networks and a novel coordinated defense strategy, our gradient redirection defense, called GRAD${}^2$, achieves small utility trade-offs and low computational overhead, outperforming the best prior defenses. Moreover, we demonstrate how gradient redirection enables reprogramming the adversary with arbitrary behavior, which we hope will foster work on new avenues of defense.
Deployment of ML Models using Kubeflow on Different Cloud Providers

This project aims to explore the process of deploying Machine learning models on Kubernetes using an open-source tool called Kubeﬂow [1] - an end-to-end ML Stack orchestration toolkit. We create end-to-end Machine Learning models on Kubeﬂow in the form of pipelines and analyze various points including the ease of setup, deployment models, performance, limitations and features of the tool. We hope that our project acts almost like a seminar/introductory report that can help vanilla cloud/Kubernetes users with zero knowledge on Kubeﬂow use Kubeﬂow to deploy ML models. From setup on diﬀerent clouds to serving our trained model over the internet - we give details and metrics detailing the performance of Kubeﬂow.
Terahertz photodetection in scalable single-layer-graphene and hexagonal boron nitride heterostructures

The unique optoelectronic properties of single layer graphene (SLG) are ideal for the development of photonic devices across a broad range of frequencies from x rays to microwaves. In the terahertz (THz) frequency range (0.1–10 THz), this has led to the development of optical modulators, nonlinear sources, and photodetectors with state-of-the-art performances. A key challenge is the integration of SLG-based active elements with pre-existing technological platforms in a scalable way, while maintaining performance level unperturbed. Here, we report room temperature THz detectors made of large-area SLG, grown by chemical vapor deposition (CVD) and integrated in antenna-coupled field effect transistors. We selectively activate the photo-thermoelectric detection dynamics, and we employ different dielectric configurations of SLG on Al2O3 with and without large-area CVD hexagonal boron nitride capping to investigate their effect on SLG thermoelectric properties underpinning photodetection. With these scalable architectures, response times ∼5 ns and noise equivalent powers (NEPs) ∼1 nW Hz−1/2 are achieved under zero-bias operation. This shows the feasibility of scalable, large-area, layered material heterostructures for THz detection.
Competing for Attention - The Effect of Talk Radio on Elections and Political Polarization in the US

This paper studies the effects of talk radio, specifically the Rush Limbaugh Show, on electoral outcomes and attitude polarization in the U.S. We propose a novel identification strategy that considers the radio space in each county as a market where multiple stations are competing for listeners' attention. Our measure of competition is a spatial Herfindahl-Hirschman Index (HHI) in radio frequencies. To address endogeneity concerns, we exploit the variation in competition based on accidental frequency overlaps in a county, conditional on the overall level of radio frequency competition. We find that counties with higher exposure to the Rush Limbaugh Show have a systematically higher vote share for Donald Trump in the 2016 and 2020 U.S. presidential elections. Combining our county-level Rush Limbaugh Show exposure measure with individual survey data reveals that self-identifying Republicans in counties with higher exposure to the Show express more conservative political views, while self-identifying Democrats in these same counties express more moderate political views. Taken together, these findings provide some of the first insights on the effects of contemporary talk radio on political outcomes, both at the aggregate and individual level.
Integral equation study of effective attraction between like-charged particles mediated by cations: Comparison between IPY2 and HNC closures

Effective interactions between like-charged particles immersed in an electrolyte solution were calculated using two integral equation theories, hypernetted-chain (HNC)-Ornstein-Zernike (OZ) and ionic Percus-Yevick 2 (IPY2)-OZ. When the HNC-OZ theory was adopted, the electrolyte concentration dependence of the effective interaction showed a reentrant behavior. By contrast, the IPY2-OZ theory did not indicate the behavior. Monte Carlo simulations were performed for one of the model systems, and the results agreed qualitatively with those calculated using the HNC-OZ theory.
The Banach-Tarski Paradox

This thesis presents the strong and weak forms of the Banach-Tarski paradox based on the Hausdorff paradox. It provides modernized proofs of the paradoxes and necessary properties of equidecomposable and paradoxical sets. The historical significance of the paradox for measure theory is covered, along with its incorrect attribution to Banach and Tarski. Finally, the necessity of the axiom of choice is discussed and contrasted with other axiomatic and topological assumptions that enable the paradoxes.
Evaluating Understanding on Conceptual Abstraction Benchmarks

A long-held objective in AI is to build systems that understand concepts in a humanlike way. Setting aside the difficulty of building such a system, even trying to evaluate one is a challenge, due to present-day AI's relative opacity and its proclivity for finding shortcut solutions. This is exacerbated by humans' tendency to anthropomorphize, assuming that a system that can recognize one instance of a concept must also understand other instances, as a human would. In this paper, we argue that understanding a concept requires the ability to use it in varied contexts. Accordingly, we propose systematic evaluations centered around concepts, by probing a system's ability to use a given concept in many different instantiations. We present case studies of such an evaluations on two domains -- RAVEN (inspired by Raven's Progressive Matrices) and the Abstraction and Reasoning Corpus (ARC) -- that have been used to develop and assess abstraction abilities in AI systems. Our concept-based approach to evaluation reveals information about AI systems that conventional test sets would have left hidden.
Analysis and Verification of Cost-Effective Design Modifications to Commercially Available Fixed-Wing Unmanned Aerial Vehicle to Improve Performance, Stability and Control Characteristics, and Structural Integrity

In this work, several cost-effective design modifications were made to a commercially available fixed-wing unmanned aerial vehicle in order to improve overall performance and reliability. These modifications included the addition of 3D-printed winglets to improve aerodynamic performance, applying fiberglass reinforcement to areas of structural concern, the addition of rudder control surfaces for improved control authority, increasing the length of the empennage booms for both stability and control improvements, and the addition of dorsal fins to improve vertical fin stall characteristics. Design methodology and manufacturing processes were outlined, a cost breakdown was presented, and results from dynamic analysis were validated through extensive flight testing of the modified aircraft.