Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditionally Affinely Invariant Rerandomization and its Admissibility

Rerandomization utilizes modern computing ability to search for covariate balance improved experimental design while adhering to the randomization principle originally advocated by RA Fisher. Conditionally affinely invariant rerandomization has the ``Equal Percent Variance Reducing'' property on subsets of conditionally ellipsoidally symmetric covariates. It is suitable to deal with covariates of varying importance or mixed types and usually produces multiple balance scores. ``Unified'' and `` intersection'' methods are common ways of deciding on multiple scores. In general, `` intersection'' methods are computationally more efficient but asymptotically inadmissible. As computational cost is not a major concern in experimental design, we recommend ``unified'' methods to build admissible criteria for rerandomization
Ironies of Generative AI: Understanding and mitigating productivity loss in human-AI interactions

Generative AI (GenAI) systems offer opportunities to increase user productivity in many tasks, such as programming and writing. However, while they boost productivity in some studies, many others show that users are working ineffectively with GenAI systems and losing productivity. Despite the apparent novelty of these usability challenges, these 'ironies of automation' have been observed for over three decades in Human Factors research on the introduction of automation in domains such as aviation, automated driving, and intelligence. We draw on this extensive research alongside recent GenAI user studies to outline four key reasons for productivity loss with GenAI systems: a shift in users' roles from production to evaluation, unhelpful restructuring of workflows, interruptions, and a tendency for automation to make easy tasks easier and hard tasks harder. We then suggest how Human Factors research can also inform GenAI system design to mitigate productivity loss by using approaches such as continuous feedback, system personalization, ecological interface design, task stabilization, and clear task allocation. Thus, we ground developments in GenAI system usability in decades of Human Factors research, ensuring that the design of human-AI interactions in this rapidly moving field learns from history instead of repeating it.
Challenging the Black Box: A Comprehensive Evaluation of Attribution Maps of CNN Applications in Agriculture and Forestry

In this study, we explore the explainability of neural networks in agriculture and forestry, specifically in fertilizer treatment classification and wood identification. The opaque nature of these models, often considered 'black boxes', is addressed through an extensive evaluation of state-of-the-art Attribution Maps (AMs), also known as class activation maps (CAMs) or saliency maps. Our comprehensive qualitative and quantitative analysis of these AMs uncovers critical practical limitations. Findings reveal that AMs frequently fail to consistently highlight crucial features and often misalign with the features considered important by domain experts. These discrepancies raise substantial questions about the utility of AMs in understanding the decision-making process of neural networks. Our study provides critical insights into the trustworthiness and practicality of AMs within the agriculture and forestry sectors, thus facilitating a better understanding of neural networks in these application areas.
Buffered Streaming Edge Partitioning

Addressing the challenges of processing massive graphs, which are prevalent in diverse fields such as social, biological, and technical networks, we introduce HeiStreamE and FreightE, two innovative (buffered) streaming algorithms designed for efficient edge partitioning of large-scale graphs. HeiStreamE utilizes an adapted Split-and-Connect graph model and a Fennel-based multilevel partitioning scheme, while FreightE partitions a hypergraph representation of the input graph. Besides ensuring superior solution quality, these approaches also overcome the limitations of existing algorithms by maintaining linear dependency on the graph size in both time and memory complexity with no dependence on the number of blocks of partition. Our comprehensive experimental analysis demonstrates that HeiStreamE outperforms current streaming algorithms and the re-streaming algorithm 2PS in partitioning quality (replication factor), and is more memory-efficient for real-world networks where the number of edges is far greater than the number of vertices. Further, FreightE is shown to produce fast and efficient partitions, particularly for higher numbers of partition blocks.
Using rule engine in self-healing systems and MAPE model

Software malfunction presents a significant hurdle within the computing domain, carrying substantial risks for systems, enterprises, and users universally. To produce software with high reliability and quality, effective debugging is essential. Program debugging is an activity to reduce software maintenance costs. In this study, a failure repair method that uses a rule engine is presented. The simulation on mRUBIS showed that the proposed method could be efficient in the operational environment. Through a thorough grasp of software failure and the adoption of efficient mitigation strategies, stakeholders can bolster the dependability, security, and adaptability of software systems. This, in turn, reduces the repercussions of failures and cultivates increased confidence in digital technologies.
Nonlinear harmonic spectra in bilayer van der Waals antiferromagnets CrX$_{3}$

Bilayer antiferromagnets CrX$_{3}$ (X $=$ Cl, Br, and I) are promising materials for spintronics and optoelectronics that are rooted in their peculiar electronic structures. However, their bands are often hybridized from the interlayer antiferromagnetic ordering, which are difficult to disentangle by traditional methods. In this work, we theoretically show that nonlinear harmonic spectra can differentiate subtle differences in their electronic states. In contrast to prior nonlinear optical studies which often use one or two photon energies, we systematically study the wavelength-dependent nonlinear harmonic spectra realized by hundreds of individual dynamical simulations under changed photon energies. Through turning on and off some excitation channels, we can pinpoint every dipole-allowed transition that largely contributes to the second and third harmonics. With the help of momentum matrix elements, highly entangled resonance peaks at a higher energy above the band edge can be assigned to specific transitions between the valence bands and three separate regions of conduction bands. Our findings demonstrate a feasible means to detect very complex electronic structures in an important family of two-dimensional antiferromagnets.
Stability of quasiperiodic superconductors

We study the effects of quasiperiodicity on the stability of conventional and unconventional superconductors. Quasiperiodicity is modelled using the three-dimensional Aubry-Andre model, a system in which electrons are coupled to a translation-symmetry-breaking potential that is incommensurate with the underlying lattice. Upon increasing the strength of the quasiperiodic potential, the single-particle eigenstates undergo a transition from a ballistic to a diffusive character. Here, we study the instability of the model towards superconductivity. We find that in the ballistic regime, the system is unstable towards both $s$-wave and $p$-wave superconductivity. In contrast, only the conventional $s$-wave instability survives in the intermediate diffusive regime. Our results suggest a version of Anderson's theorem for quasiperiodic systems, relating the normal state dynamics to the stability of conventional and unconventional superconductivity. These findings are relevant vis-a-vis recent studies of superconductivity in quasiperiodic moire structures.
Projective Holder-Minkowski Colors: A Generalized Set of Commutative & Associative Operations with Inverse Elements for Representing and Manipulating Colors

One of the key problems in dealing with color in rendering, shading, compositing, or image manipulation is that we do not have algebraic structures that support operations over colors. In this paper, we present an all-encompassing framework that can support a set of algebraic structures with associativity, commutativity, and inverse properties. To provide these three properties, we build our algebraic structures on an extension of projective space by allowing for negative and complex numbers. These properties are important for (1) manipulating colors as periodic functions, (2) solving inverse problems dealing with colors, and (3) being consistent with the wave representation of the color. Allowance of negative and complex numbers is not a problem for practical applications, since we can always convert the results into desired range for display purposes as we do in High Dynamic Range imaging. This set of algebraic structures can be considered as a generalization of the Minkowski norm Lp in projective space. These structures also provide a new version of the generalized Holder average with associativity property. Our structures provide inverses of any operation by allowing for negative and complex numbers. These structures provide all properties of the generalized Holder average by providing a continuous bridge between the classical weighted average, harmonic mean, maximum, and minimum operations using a single parameter p.
Exploring City Branding as a Tool to Conserve Urban Green Infrastructure in Developing Countries

Cities are aggressively pushing themselves to become global destinations for economic activities resulting in various environmental stresses. The situation in developing countries such as India is not too different either. Cities are reinventing themselves to emerge as a global destination to attract talent, investment and tourism. City Branding is one such marketing strategy wherein key aspect(s) of a city (cultural, environmental, infrastructure, etc.) is used to project it as a brand, thus garnering competitive advantages and co benefits. This article, however, examines the city branding tool in a different perspective. By reviewing branding theories, concepts and case studies, this article explores the possible use of city branding strategy in conserving and promoting green infrastructure. Theoretical assessments undertaken in this article indicate that city branding has a potential to contribute positively towards cities’ developmental aspirations and improving the quality of life of its citizens, leading to an environmentally sustainable urban development in India.
Optimal Pseudorandom Generators for Low-Degree Polynomials Over Moderately Large Fields

We construct explicit pseudorandom generators that fool $n$-variate polynomials of degree at most $d$ over a finite field $\mathbb{F}_q$. The seed length of our generators is $O(d \log n + \log q)$, over fields of size exponential in $d$ and characteristic at least $d(d-1)+1$. Previous constructions such as Bogdanov's (STOC 2005) and Derksen and Viola's (FOCS 2022) had either suboptimal seed length or required the field size to depend on $n$. Our approach follows Bogdanov's paradigm while incorporating techniques from Lecerf's factorization algorithm (J. Symb. Comput. 2007) and insights from the construction of Derksen and Viola regarding the role of indecomposability of polynomials.
Multi-View Conformal Learning for Heterogeneous Sensor Fusion

Being able to assess the confidence of individual predictions in machine learning models is crucial for decision making scenarios. Specially, in critical applications such as medical diagnosis, security, and unmanned vehicles, to name a few. In the last years, complex predictive models have had great success in solving hard tasks and new methods are being proposed every day. While the majority of new developments in machine learning models focus on improving the overall performance, less effort is put on assessing the trustworthiness of individual predictions, and even to a lesser extent, in the context of sensor fusion. To this end, we build and test multi-view and single-view conformal models for heterogeneous sensor fusion. Our models provide theoretical marginal confidence guarantees since they are based on the conformal prediction framework. We also propose a multi-view semi-conformal model based on sets intersection. Through comprehensive experimentation, we show that multi-view models perform better than single-view models not only in terms of accuracy-based performance metrics (as it has already been shown in several previous works) but also in conformal measures that provide uncertainty estimation. Our results also showed that multi-view models generate prediction sets with less uncertainty compared to single-view models.
Is Open-Source There Yet? A Comparative Study on Commercial and Open-Source LLMs in Their Ability to Label Chest X-Ray Reports

Introduction: With the rapid advances in large language models (LLMs), there have been numerous new open source as well as commercial models. While recent publications have explored GPT-4 in its application to extracting information of interest from radiology reports, there has not been a real-world comparison of GPT-4 to different leading open-source models. Materials and Methods: Two different and independent datasets were used. The first dataset consists of 540 chest x-ray reports that were created at the Massachusetts General Hospital between July 2019 and July 2021. The second dataset consists of 500 chest x-ray reports from the ImaGenome dataset. We then compared the commercial models GPT-3.5 Turbo and GPT-4 from OpenAI to the open-source models Mistral-7B, Mixtral-8x7B, Llama2-13B, Llama2-70B, QWEN1.5-72B and CheXbert and CheXpert-labeler in their ability to accurately label the presence of multiple findings in x-ray text reports using different prompting techniques. Results: On the ImaGenome dataset, the best performing open-source model was Llama2-70B with micro F1-scores of 0.972 and 0.970 for zero- and few-shot prompts, respectively. GPT-4 achieved micro F1-scores of 0.975 and 0.984, respectively. On the institutional dataset, the best performing open-source model was QWEN1.5-72B with micro F1-scores of 0.952 and 0.965 for zero- and few-shot prompting, respectively. GPT-4 achieved micro F1-scores of 0.975 and 0.973, respectively. Conclusion: In this paper, we show that while GPT-4 is superior to open-source models in zero-shot report labeling, the implementation of few-shot prompting can bring open-source models on par with GPT-4. This shows that open-source models could be a performant and privacy preserving alternative to GPT-4 for the task of radiology report classification.
Easy as ABCs: Unifying Boltzmann Q-Learning and Counterfactual Regret Minimization

We propose ABCs (Adaptive Branching through Child stationarity), a best-of-both-worlds algorithm combining Boltzmann Q-learning (BQL), a classic reinforcement learning algorithm for single-agent domains, and counterfactual regret minimization (CFR), a central algorithm for learning in multi-agent domains. ABCs adaptively chooses what fraction of the environment to explore each iteration by measuring the stationarity of the environment's reward and transition dynamics. In Markov decision processes, ABCs converges to the optimal policy with at most an O(A) factor slowdown compared to BQL, where A is the number of actions in the environment. In two-player zero-sum games, ABCs is guaranteed to converge to a Nash equilibrium (assuming access to a perfect oracle for detecting stationarity), while BQL has no such guarantees. Empirically, ABCs demonstrates strong performance when benchmarked across environments drawn from the OpenSpiel game library and OpenAI Gym and exceeds all prior methods in environments which are neither fully stationary nor fully nonstationary.
Measuring and Controlling Persona Drift in Language Model Dialogs

Prompting is a standard tool for customizing language-model chatbots, enabling them to take on a specific"persona". An implicit assumption in the use of prompts is that they will be stable, so the chatbot will continue to generate text according to the stipulated persona for the duration of a conversation. We propose a quantitative benchmark to test this assumption, evaluating persona stability via self-chats between two personalized chatbots. Testing popular models like LLaMA2-chat-70B, we reveal a significant persona drift within eight rounds of conversations. An empirical and theoretical analysis of this phenomenon suggests the transformer attention mechanism plays a role, due to attention decay over long exchanges. To combat attention decay and persona drift, we propose a lightweight method called split-softmax, which compares favorably against two strong baselines.
Real-time 3D Semantic Scene Perception for Egocentric Robots with Binocular Vision

Perceiving a three-dimensional (3D) scene with multiple objects while moving indoors is essential for vision-based mobile cobots, especially for enhancing their manipulation tasks. In this work, we present an end-to-end pipeline with instance segmentation, feature matching, and point-set registration for egocentric robots with binocular vision, and demonstrate the robot's grasping capability through the proposed pipeline. First, we design an RGB image-based segmentation approach for single-view 3D semantic scene segmentation, leveraging common object classes in 2D datasets to encapsulate 3D points into point clouds of object instances through corresponding depth maps. Next, 3D correspondences of two consecutive segmented point clouds are extracted based on matched keypoints between objects of interest in RGB images from the prior step. In addition, to be aware of spatial changes in 3D feature distribution, we also weigh each 3D point pair based on the estimated distribution using kernel density estimation (KDE), which subsequently gives robustness with less central correspondences while solving for rigid transformations between point clouds. Finally, we test our proposed pipeline on the 7-DOF dual-arm Baxter robot with a mounted Intel RealSense D435i RGB-D camera. The result shows that our robot can segment objects of interest, register multiple views while moving, and grasp the target object. The source code is available at https://github.com/mkhangg/semantic_scene_perception.
Local and Global Expansivity in Water

The supra-molecular structure of a liquid is strongly connected to its dynamics which in turn controls macroscopic properties such as viscosity. Consequently, detailed knowledge about how this structure changes with temperature is essential to understand the thermal evolution of the dynamics ranging from the liquid to the glass. Here we combine infrared spectroscopy (IR) measurements of the hydrogen (H) bond stretching vibration of water with molecular dynamics simulations and employ a quantitative analysis to extract the inter-molecular H-bond length in a wide temperature range of the liquid. The extracted expansivity of this H-bond differs strongly from that of the average nearest neighbor distance of oxygen atoms obtained through a common conversion of mass density. However, both properties can be connected through a simple model based on a random loose packing of spheres with a variable coordination number which demonstrates the relevance of supra-molecular arrangement. Further, the exclusion of the expansivity of the inter-molecular H-bonds reveals that the most compact molecular arrangement is formed in the range $\sim316-331\,\text{K}$ (i.e. above the density maximum) close to the temperature of several pressure-related anomalies which indicates a characteristic point in the supra-molecular arrangement. These results confirm our earlier approach to deduce inter-molecular H-bond lengths via IR in polyalcohols [Gabriel et al. 2021] quantitatively and open a new alley to investigate the role of inter-molecular expansion as a precursor of molecular fluctuations on a bond-specific level.
Analysis of Levenshtein Transformer's Decoder and Its Variants

Levenshtein transformer (LevT) is a non-autoregressive machine translation model with high decoding efficiency and comparable translation quality in terms of bleu score, due to its parallel decoding and iterative refinement procedure. Are there any deficiencies of its translations and what improvements could be made? In this report, we focus on LevT's decoder and analyse the decoding results length, subword generation, and deletion module's capability. We hope to identify weaknesses of the decoder for future improvements. We also compare translations of the original LevT, knowledge-distilled LevT, LevT with translation memory, and the KD-LevT with translation memory to see how KD and translation memory can help.
Bayesian Active Learning for Censored Regression

Bayesian active learning is based on information theoretical approaches that focus on maximising the information that new observations provide to the model parameters. This is commonly done by maximising the Bayesian Active Learning by Disagreement (BALD) acquisitions function. However, we highlight that it is challenging to estimate BALD when the new data points are subject to censorship, where only clipped values of the targets are observed. To address this, we derive the entropy and the mutual information for censored distributions and derive the BALD objective for active learning in censored regression ($\mathcal{C}$-BALD). We propose a novel modelling approach to estimate the $\mathcal{C}$-BALD objective and use it for active learning in the censored setting. Across a wide range of datasets and models, we demonstrate that $\mathcal{C}$-BALD outperforms other Bayesian active learning methods in censored regression.
A critical analysis of cognitive load measurement methods for evaluating the usability of different types of interfaces: guidelines and framework for Human-Computer Interaction

Usability testing is an essential part of product design, particularly for user interfaces. To enhance the reliability of usability evaluations, employing cognitive load measurement methods can be highly effective in assessing the mental effort required to complete tasks during user testing. This review aims to provide an overview of the most suitable cognitive load measurement methods for evaluating various types of user interfaces, serving as a valuable resource for guiding usability assessments. To bridge the existing gap in the literature, a systematic review was conducted, analyzing 76 articles with experimental study designs that met the eligibility criteria. The review encompasses different methods of measuring cognitive load applicable to assessing the usability of diverse user interfaces, including computer software, information systems, video games, web and mobile applications, robotics, and virtual reality applications. The results highlight the most widely utilized cognitive load measurement methods in software usability, their respective usage percentages, and their application in evaluating the usability of each user interface type. Additionally, the advantages and disadvantages of each method are discussed. Furthermore, the review proposes a framework to assist usability testers in selecting an appropriate cognitive load measurement method for conducting accurate usability evaluations.