Miracle-dz/UniFAR-FTUs huggingface dataset:
Large language models can replicate cross-cultural differences in personality

We use a large-scale experiment (N=8000) to determine whether GPT-4 can replicate cross-cultural differences in the Big Five, measured using the Ten-Item Personality Inventory. We used the US and South Korea as the cultural pair, given that prior research suggests substantial personality differences between people from these two countries. We manipulated the target of the simulation (US vs. Korean), the language of the inventory (English vs. Korean), and the language model (GPT-4 vs. GPT-3.5). Our results show that GPT-4 replicated the cross-cultural differences for each factor. However, mean ratings had an upward bias and exhibited lower variation than in the human samples, as well as lower structural validity. Overall, we provide preliminary evidence that LLMs can aid cross-cultural psychological research.
Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting

As large language models (LLMs) are adopted as a fundamental component of language technologies, it is crucial to accurately characterize their performance. Because choices in prompt design can strongly influence model behavior, this design process is critical in effectively using any modern pre-trained generative language model. In this work, we focus on LLM sensitivity to a quintessential class of meaning-preserving design choices: prompt formatting. We find that several widely used open-source LLMs are extremely sensitive to subtle changes in prompt formatting in few-shot settings, with performance differences of up to 76 accuracy points when evaluated using LLaMA-2-13B. Sensitivity remains even when increasing model size, the number of few-shot examples, or performing instruction tuning. Our analysis suggests that work evaluating LLMs with prompting-based methods would benefit from reporting a range of performance across plausible prompt formats, instead of the currently-standard practice of reporting performance on a single format. We also show that format performance only weakly correlates between models, which puts into question the methodological validity of comparing models with an arbitrarily chosen, fixed prompt format. To facilitate systematic analysis we propose FormatSpread, an algorithm that rapidly evaluates a sampled set of plausible prompt formats for a given task, and reports the interval of expected performance without accessing model weights. Furthermore, we present a suite of analyses that characterize the nature of this sensitivity, including exploring the influence of particular atomic perturbations and the internal representation of particular formats.
Will the Prince Get True Love's Kiss? On the Model Sensitivity to Gender Perturbation over Fairytale Texts

Recent studies show that traditional fairytales are rife with harmful gender biases. To help mitigate these gender biases in fairytales, this work aims to assess learned biases of language models by evaluating their robustness against gender perturbations. Specifically, we focus on Question Answering (QA) tasks in fairytales. Using counterfactual data augmentation to the FairytaleQA dataset, we evaluate model robustness against swapped gender character information, and then mitigate learned biases by introducing counterfactual gender stereotypes during training time. We additionally introduce a novel approach that utilizes the massive vocabulary of language models to support text genres beyond fairytales. Our experimental results suggest that models are sensitive to gender perturbations, with significant performance drops compared to the original testing set. However, when first fine-tuned on a counterfactual training dataset, models are less sensitive to the later introduced anti-gender stereotyped text.
Is there a Trojan! : Literature survey and critical evaluation of the latest ML based modern intrusion detection systems in IoT environments

IoT as a domain has grown so much in the last few years that it rivals that of the mobile network environments in terms of data volumes as well as cybersecurity threats. The confidentiality and privacy of data within IoT environments have become very important areas of security research within the last few years. More and more security experts are interested in designing robust IDS systems to protect IoT environments as a supplement to the more traditional security methods. Given that IoT devices are resource-constrained and have a heterogeneous protocol stack, most traditional intrusion detection approaches don’t work well within these schematic boundaries. This has led security researchers to innovate at the intersection of Machine Learning and IDS to solve the shortcomings of non-learning based IDS systems in the IoT ecosystem. Despite various ML algorithms already having high accuracy with IoT datasets, we can see a lack of sufficient production grade models. This survey paper details a comprehensive summary of the latest learning-based approaches used in IoT intrusion detection systems, and conducts a through critical review of these systems, potential pitfalls in ML pipelines, challenges from an ML perspective and discusses future research scope, and recommendations.
Mid-Band 5G: A Measurement Study in Europe and US

Fifth Generation (5G) mobile networks mark a significant shift from previous generations of networks. By introducing a flexible design, 5G networks support highly diverse application requirements. Currently, the landscape of previous measurement studies does not shed light on 5G network configuration and the inherent implications to application performance. In this paper, we precisely fill this gap and report our in-depth multi-country measurement study on 5G deployed at mid-bands. This is the common playground for U.S. and European carriers. Our findings reveal key aspects on how carriers configure their network, including spectrum utilization, frame configuration, resource allocation and their implication on the application performance.
Bulk Universality for Complex non-Hermitian Matrices with Independent and Identically Distributed Entries

We consider N x N matrices with complex entries that are perturbed by a complex Gaussian matrix with small variance. We prove that if the unperturbed matrix satisfies certain local laws then the bulk correlation functions are universal in the large N limit. Assuming the entries are independent and identically distributed with a common distribution that has finite moments, the Gaussian component is removed by the four moment theorem of Tao and Vu.
Almost sure central limit theorem for the hyperbolic Anderson model with L\'evy white noise

In this paper, we present an almost sure central limit theorem (ASCLT) for the hyperbolic Anderson model (HAM) with a L\'evy white noise in a finite-variance setting. This work complements a recent work by the first and third authors on the (quantitative) central limit theorems for the solution to the HAM. We present two different proofs: one uses the Clark-Ocone formula and takes advantage of the martingale structure of the white-in-time noise, and the other is obtained by combining the second-order Gaussian Poincar\'e inequality with Ibragimov and Lifshits' method of characteristic functions. Both approaches are different from the one developed in the PhD thesis of C. Zheng (2011), allowing us to establish the ASCLT without using lengthy computations based on star contractions. Moreover, the second approach is expected to be useful for similar studies on SPDEs with Gaussian colored-in-time noise when the first approach, based on It\^o calculus, is not applicable.
Extracting Physical Causality from Measurements to Detect and Localize False Data Injection Attacks

False Data Injection Attack (FDIA) has become a growing concern in modern cyber-physical power systems. Most existing FDIA detection techniques project the raw measurement data into a high-dimensional latent space to separate normal and attacked samples. These approaches focus more on the statistical correlations of data values and are therefore susceptible to data distribution drifts induced by changes in system operating points or changes in FDIA types and strengths, especially for FDIA localization tasks. Causal inference, on the other hand, extracts the causality behind the coordinated fluctuations of different measurements. The causality patterns are determined by fundamental physical laws such as Ohm's Law and Kirchhoff's Law. They are sensitive to the violation of physical laws caused by FDIA, but tend to remain stable with the drift of system operating points. Leveraging this advantage, this paper proposes a joint FDIA detection and localization framework based on causal inference and the Graph Attention Network (GAT) to identify the attacked system nodes. The proposed framework consists of two levels. The lower level uses the X-learner algorithm to estimate the causality strength between measurements and generate Measurement Causality Graphs (MCGs). The upper level then applies a GAT to identify the anomaly patterns in the MCGs. Since the extracted causality patterns are intrinsically related to the measurements, it is easier for the upper level to figure out the attacked nodes than the existing FDIA localization approaches. The performance of the proposed framework is evaluated on the IEEE 39-bus system. Experimental results show that the causality-based FDIA detection and localization mechanism is highly interpretable and robust.
Deep learning segmentation of low-resolution images for prostate magnetic resonance-guided radiotherapy

The MR-Linac can enable real-time radiotherapy adaptation. However, real-time image acquisition is restricted to 2D to obtain sufficient spatial resolution, hindering accurate 3D segmentation. By reducing spatial resolution fast 3D imaging is feasible. Our study evaluates how much the spatial resolution of MR-images can be reduced without compromising a deep-learning segmentation performance. We also assess the effect of an auxiliary task of simultaneous restoration of high-resolution images. Artificially downsampled images were created from 163 3D MR-scans using a k-space truncation to spatial resolution levels of approximately 1.5x1. 5x2/1.5x3x3/1.5x6x6/2.5x9x9 mm3. Data was split into train/validation/test of 116/12/35 images. A U-Net was trained to obtain high-resolution segmentation of prostate, bladder, and rectum on each resolution level. Images acquired with low resolution were obtained by scanning 10 male healthy volunteers, 4 series per subject with a gradually decreasing spatial resolution. The networks trained from the artificially downsampled data were fine-tuned to segment these images. For the artificially downsampled images results in terms of DICE when including the auxiliary task, going from training on the highest resolution images to the lowest, where 0.87/0.86/0.86/0.85, 0.95/0.94/0.93/0.93 and 0.82/0.84/0.83/0.80 for CTV, bladder, and rectum, respectively while for the acquired low-resolution images 0.78/0.76/0.71/0.59, 0.82/0.85/0.81/0.74 and 0.81/0.82/0.69/0.58. The segmentation results when training including the auxiliary task were comparable for the artificial images and with a trend towards better for the acquired LR images. These results indicate that some speed-up in image acquisition can be obtained without significantly reducing the accuracy of a segmentation deep-learning network.
VaR\ and CVaR Estimation in a Markov Cost Process: Lower and Upper Bounds

We tackle the problem of estimating the Value-at-Risk (VaR) and the Conditional Value-at-Risk (CVaR) of the infinite-horizon discounted cost within a Markov cost process. First, we derive a minimax lower bound of $\Omega(1/\sqrt{n})$ that holds both in an expected and in a probabilistic sense. Then, using a finite-horizon truncation scheme, we derive an upper bound for the error in CVaR estimation, which matches our lower bound up to constant factors. Finally, we discuss an extension of our estimation scheme that covers more general risk measures satisfying a certain continuity criterion, e.g., spectral risk measures, utility-based shortfall risk. To the best of our knowledge, our work is the first to provide lower and upper bounds on the estimation error for any risk measure within Markovian settings. We remark that our lower bounds also extend to the infinite-horizon discounted costs' mean. Even in that case, our result $\Omega(1/\sqrt{n}) $ improves upon the existing result $\Omega(1/n)$[13].
Topological Expressivity of ReLU Neural Networks

We study the expressivity of ReLU neural networks in the setting of a binary classification problem from a topological perspective. Recently, empirical studies showed that neural networks operate by changing topology, transforming a topologically complicated data set into a topologically simpler one as it passes through the layers. This topological simplification has been measured by Betti numbers, which are algebraic invariants of a topological space. We use the same measure to establish lower and upper bounds on the topological simplification a ReLU neural network can achieve with a given architecture. We therefore contribute to a better understanding of the expressivity of ReLU neural networks in the context of binary classification problems by shedding light on their ability to capture the underlying topological structure of the data. In particular the results show that deep ReLU neural networks are exponentially more powerful than shallow ones in terms of topological simplification. This provides a mathematically rigorous explanation why deeper networks are better equipped to handle complex and topologically rich datasets.
Observational and Experimental Insights into Machine Learning-Based Defect Classification in Wafers

This survey paper offers a comprehensive review of methodologies utilizing machine learning (ML) classification techniques for identifying wafer defects in semiconductor manufacturing. Despite the growing body of research demonstrating the effectiveness of ML in wafer defect identification, there is a noticeable absence of comprehensive reviews on this subject. This survey attempts to fill this void by amalgamating available literature and providing an in-depth analysis of the advantages, limitations, and potential applications of various ML classification algorithms in the realm of wafer defect detection. An innovative taxonomy of methodologies that we present provides a detailed classification of algorithms into more refined categories and techniques. This taxonomy follows a three-tier structure, starting from broad methodology categories and ending with specific techniques. It aids researchers in comprehending the complex relationships between different algorithms and their techniques. We employ a rigorous Observational and experimental evaluation to rank these varying techniques. For the Observational evaluation, we assess techniques based on a set of four criteria. The experimental evaluation ranks the algorithms employing the same techniques, sub-categories, and categories. Also the paper illuminates the future prospects of ML classification techniques for wafer defect identification, underscoring potential advancements and opportunities for further research in this field
Basal force fluctuations and granular rheology: Linking macroscopic descriptions of granular flows to bed forces with implications for monitoring signals

Granular flows are ubiquitous in nature with single flows traversing a wide range of dynamic conditions from initiation to deposition. Many of these flows are responsible for significant hazards and have the ability to generate remotely detectable seismic signals. These signals provide a potential for real-time flow measurements from a safe distance. To fully realize the benefit of seismic measurements, basal granular forces must be linked to macroscopic internal flow dynamics across a wide range of flow conditions. We utilize discrete element simulations to observe dry and submerged granular flows under plane-shear and inclined flow configurations, relating bulk kinematics to basal force distributions. We find that force fluctuations scale with non-dimensional shear-rate ($I$), and this scaling tracks three flow regimes that can be described by $\mu(I)$ rheology, as well as a fourth regime that marks a `phase change' from a liquid-like to a gas-like state: (1) an unsteady particle rearrangement regime when $I<10^{-3}$, where basal forces are dominated by low frequencies; (2) an intermediate regime when $10^{-3}<I<10^{-2}$, where basal forces start to become noise-like, (3) a transitional regime at $10^{-2}<I<10^{-1}$, where the increase in basal tractions with increasing shear-rates stalls as the granular bed dilates, partially destroying the contact network and memory stored in the particle contact network, and (4) a fully collisional regime when $I>10^{-1}$, where the signal is nearly flat up to a cutoff frequency. This effort suggests that basal forces can be used to interpret complex granular processes in geophysical flows.
Thermodynamic cost of finite-time stochastic resetting

Recent experiments have implemented resetting by means of an external trap, whereby a system relaxes to the minimum of the trap and is reset in a finite time. In this work, we set up and analyze the thermodynamics of such a protocol. We present a general framework, even valid for non-Poissonian resetting, that captures the thermodynamic work required to maintain a resetting process up to a given observation time, and exactly calculate the moment generating function of this work. Our framework is valid for a wide range of systems, the only assumption being of relaxation to equilibrium in the resetting trap. Examples and extensions are considered, including the validity of fluctuation theorems. In the case of Brownian motion, we investigate optimal resetting schemes that minimize work and its fluctuations, the mean work for arbitrary switching protocols and comparisons to earlier-studied resetting schemes. Numerical simulations are performed to validate our findings.
Robust Trading in a Generalized Lattice Market

This paper introduces a novel robust trading paradigm, called \textit{multi-double linear policies}, situated within a \textit{generalized} lattice market. Distinctively, our framework departs from most existing robust trading strategies, which are predominantly limited to single or paired assets and typically embed asset correlation within the trading strategy itself, rather than as an inherent characteristic of the market. Our generalized lattice market model incorporates both serially correlated returns and asset correlation through a conditional probabilistic model. In the nominal case, where the parameters of the model are known, we demonstrate that the proposed policies ensure survivability and probabilistic positivity. We then derive an analytic expression for the worst-case expected gain-loss and prove sufficient conditions that the proposed policies can maintain a \textit{positive expected profits}, even within a seemingly nonprofitable symmetric lattice market. When the parameters are unknown and require estimation, we show that the parameter space of the lattice model forms a convex polyhedron, and we present an efficient estimation method using a constrained least-squares method. These theoretical findings are strengthened by extensive empirical studies using data from the top 30 companies within the S\&P 500 index, substantiating the efficacy of the generalized model and the robustness of the proposed policies in sustaining the positive expected profit and providing downside risk protection.
Gaussian processes based data augmentation and expected signature for time series classification

The signature is a fundamental object that describes paths (that is, continuous functions from an interval to a Euclidean space). Likewise, the expected signature provides a statistical description of the law of stochastic processes. We propose a feature extraction model for time series built upon the expected signature. This is computed through a Gaussian processes based data augmentation. One of the main features is that an optimal feature extraction is learnt through the supervised task that uses the model.
Voroprot: an interactive tool for the analysis and visualization of complex geometric features of protein structure

UNLABELLED We present Voroprot, an interactive cross-platform software tool that provides a unique set of capabilities for exploring geometric features of protein structure. Voroprot allows the construction and visualization of the Apollonius diagram (also known as the additively weighted Voronoi diagram), the Apollonius graph, protein alpha shapes, interatomic contact surfaces, solvent accessible surfaces, pockets and cavities inside protein structure. AVAILABILITY Voroprot is available for Windows, Linux and Mac OS X operating systems and can be downloaded from http://www.ibt. lt/bioinformatics/voroprot/.
Open-Structure: a Structural Benchmark Dataset for SLAM Algorithms

This paper introduces a new benchmark dataset, Open-Structure, for evaluating visual odometry and SLAM methods, which directly equips point and line measurements, correspondences, structural associations, and co-visibility factor graphs instead of providing raw images. Based on the proposed benchmark dataset, these 2D or 3D data can be directly input to different stages of SLAM pipelines to avoid the impact of the data preprocessing modules in ablation experiments. First, we propose a dataset generator for real-world and simulated scenarios. In real-world scenes, it maintains the same observations and occlusions as actual feature extraction results. Those generated simulation sequences enhance the dataset's diversity by introducing various carefully designed trajectories and observations. Second, a SLAM baseline is proposed using our dataset to evaluate widely used modules in camera pose tracking, parametrization, and optimization modules. By evaluating these state-of-the-art algorithms across different scenarios, we discern each module's strengths and weaknesses within the camera tracking and optimization process. Our dataset and baseline are available at \url{https://github.com/yanyan-li/Open-Structure}.
LDRD Final Report: Global Optimization for Engineering Science Problems

For a wide variety of scientific and engineering problems the desired solution corresponds to an optimal set of objective function parameters, where the objective function measures a solution's quality. The main goal of the LDRD ''Global Optimization for Engineering Science Problems'' was the development of new robust and efficient optimization algorithms that can be used to find globally optimal solutions to complex optimization problems. This SAND report summarizes the technical accomplishments of this LDRD, discusses lessons learned and describes open research issues.
LDRD Final Report: A Lightweight Operating System for Multi-core Capability Class Supercomputers

The two primary objectives of this LDRD project were to create a lightweight kernel (LWK) operating system(OS) designed to take maximum advantage of multi-core processors, and to leverage the virtualization capabilities in modern multi-core processors to create a more flexible and adaptable LWK environment. The most significant technical accomplishments of this project were the development of the Kitten lightweight kernel, the co-development of the SMARTMAP intra-node memory mapping technique, and the development and demonstration of a scalable virtualization environment for HPC. Each of these topics is presented in this report by the inclusion of a published or submitted research paper. The results of this project are being leveraged by several ongoing and new research projects.