Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Machine Learning: Perspectives from Adversarial Risk Analysis

Adversarial Machine Learning (AML) is emerging as a major field aimed at the protection of automated ML systems against security threats. The majority of work in this area has built upon a game-theoretic framework by modelling a conflict between an attacker and a defender. After reviewing game-theoretic approaches to AML, we discuss the benefits that a Bayesian Adversarial Risk Analysis perspective brings when defending ML based systems. A research agenda is included.
Formal Definitions and Performance Comparison of Consistency Models for Parallel File Systems

The semantics of HPC storage systems are defined by the consistency models to which they abide. Storage consistency models have been less studied than their counterparts in memory systems, with the exception of the POSIX standard and its strict consistency model. The use of POSIX consistency imposes a performance penalty that becomes more significant as the scale of parallel file systems increases and the access time to storage devices, such as node-local solid storage devices, decreases. While some efforts have been made to adopt relaxed storage consistency models, these models are often defined informally and ambiguously as by-products of a particular implementation. In this work, we establish a connection between memory consistency models and storage consistency models and revisit the key design choices of storage consistency models from a high-level perspective. Further, we propose a formal and unified framework for defining storage consistency models and a layered implementation that can be used to easily evaluate their relative performance for different I/O workloads. Finally, we conduct a comprehensive performance comparison of two relaxed consistency models on a range of commonly-seen parallel I/O workloads, such as checkpoint/restart of scientific applications and random reads of deep learning applications. We demonstrate that for certain I/O scenarios, a weaker consistency model can significantly improve the I/O performance. For instance, in small random reads that typically found in deep learning applications, session consistency achieved an 5x improvement in I/O bandwidth compared to commit consistency, even at small scales.
Rare-earth doped yttrium silicate (Y2SiO5) thin films grown by chemical vapour deposition for quantum technologies

Yttrium orthosilicate (Y2SiO5 - YSO) is one of the most promising crystals to host rare-earth (RE) ions for quantum technologies applications. In this matrix, they indeed exhibit narrow optical and spin linewidths that can be exploited to develop quantum memories or quantum information processing capabilities. In this paper, we propose a new method to grow RE doped silicate thin films on silicon wafers based on direct liquid injection chemical vapour deposition (DLI-CVD). We optimize the deposition and annealing conditions to achieve formation of the high temperature X2-YSO phase. The phase purity and crystalline quality of the films are assessed by evaluating the optical properties of Eu3+ ions embedded in this oxide matrix. In view of the results, we discuss the possible phase formation mechanisms, and the potential of this new wafer-compatible form of YSO for quantum technologies applications.
Estimating Unknown Population Sizes Using the Hypergeometric Distribution

The multivariate hypergeometric distribution describes sampling without replacement from a discrete population of elements divided into multiple categories. Addressing a gap in the literature, we tackle the challenge of estimating discrete distributions when both the total population size and the sizes of its constituent categories are unknown. Here, we propose a novel solution using the hypergeometric likelihood to solve this estimation challenge, even in the presence of severe under-sampling. We develop our approach to account for a data generating process where the ground-truth is a mixture of distributions conditional on a continuous latent variable, such as with collaborative filtering, using the variational autoencoder framework. Empirical data simulation demonstrates that our method outperforms other likelihood functions used to model count data, both in terms of accuracy of population size estimate and in its ability to learn an informative latent space. We demonstrate our method's versatility through applications in NLP, by inferring and estimating the complexity of latent vocabularies in text excerpts, and in biology, by accurately recovering the true number of gene transcripts from sparse single-cell genomics data.
BUGFIX: towards a common language and framework for the AutomaticProgram Repair community

Techniques of Automatic Program Repair (APR) have the potential of thoroughly facilitating the task of producing quality software. After a promising start, however, progress in making APR practical has been hindered by the lack of a common framework to support the multiplicity of APR ideas and tools, and of target programming languages and environments. In this position paper we outline a general framework to enable the APR community to benefit from each other\'s advances, in particular through a standard language for describing bugs and their fixes. Such a common framework (which is also applicable to work on fault seeding) could be a tremendous benefit to researchers and developers of Interactive Development Environments (IDEs) who are working to make APR an effective part of the practical experience of software developers.
Dataset Artefacts are the Hidden Drivers of the Declining Disruptiveness in Science

Park et al. [1] reported a decline in the disruptiveness of scientific and technological knowledge over time. Their main finding is based on the computation of CD indices, a measure of disruption in citation networks [2], across almost 45 million papers and 3.9 million patents. Due to a factual plotting mistake, database entries with zero references were omitted in the CD index distributions, hiding a large number of outliers with a maximum CD index of one, while keeping them in the analysis [1]. Our reanalysis shows that the reported decline in disruptiveness can be attributed to a relative decline of these database entries with zero references. Notably, this was not caught by the robustness checks included in the manuscript. The regression adjustment fails to control for the hidden outliers as they correspond to a discontinuity in the CD index. Proper evaluation of the Monte-Carlo simulations reveals that, because of the preservation of the hidden outliers, even random citation behaviour replicates the observed decline in disruptiveness. Finally, while these papers and patents with supposedly zero references are the hidden drivers of the reported decline, their source documents predominantly do make references, exposing them as pure dataset artefacts.
"It Must Be Gesturing Towards Me": Gesture-Based Interaction between Autonomous Vehicles and Pedestrians

Interacting with pedestrians understandably and efficiently is one of the toughest challenges faced by autonomous vehicles (AVs) due to the limitations of current algorithms and external human-machine interfaces (eHMIs). In this paper, we design eHMIs based on gestures inspired by the most popular method of interaction between pedestrians and human drivers. Eight common gestures were selected to convey AVs' yielding or non-yielding intentions at uncontrolled crosswalks from previous literature. Through a VR experiment (N1 = 31) and a following online survey (N2 = 394), we discovered significant differences in the usability of gesture-based eHMIs compared to current eHMIs. Good gesture-based eHMIs increase the efficiency of pedestrian-AV interaction while ensuring safety. Poor gestures, however, cause misinterpretation. The underlying reasons were explored: ambiguity regarding the recipient of the signal and whether the gestures are precise, polite, and familiar to pedestrians. Based on this empirical evidence, we discuss potential opportunities and provide valuable insights into developing comprehensible gesture-based eHMIs in the future to support better interaction between AVs and other road users.
Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia

AI tools are increasingly deployed in community contexts. However, datasets used to evaluate AI are typically created by developers and annotators outside a given community, which can yield misleading conclusions about AI performance. How might we empower communities to drive the intentional design and curation of evaluation datasets for AI that impacts them? We investigate this question on Wikipedia, an online community with multiple AI-based content moderation tools deployed. We introduce Wikibench, a system that enables communities to collaboratively curate AI evaluation datasets, while navigating ambiguities and differences in perspective through discussion. A field study on Wikipedia shows that datasets curated using Wikibench can effectively capture community consensus, disagreement, and uncertainty. Furthermore, study participants used Wikibench to shape the overall data curation process, including refining label definitions, determining data inclusion criteria, and authoring data statements. Based on our findings, we propose future directions for systems that support community-driven data curation.
Amplified Amplitude Estimation: Exploiting Prior Knowledge to Improve Estimates of Expectation Values

We provide a method for estimating the expectation value of an operator that can utilize prior knowledge to accelerate the learning process on a quantum computer. Specifically, suppose we have an operator that can be expressed as a concise sum of projectors whose expectation values we know a priori to be $O(\epsilon)$. In that case, we can estimate the expectation value of the entire operator within error $\epsilon$ using a number of quantum operations that scales as $O(1/\sqrt{\epsilon})$. We then show how this can be used to reduce the cost of learning a potential energy surface in quantum chemistry applications by exploiting information gained from the energy at nearby points. Furthermore, we show, using Newton-Cotes methods, how these ideas can be exploited to learn the energy via integration of derivatives that we can estimate using a priori knowledge. This allows us to reduce the cost of energy estimation if the block-encodings of directional derivative operators have a smaller normalization constant than the Hamiltonian of the system.
BIRCO: A Benchmark of Information Retrieval Tasks with Complex Objectives

We present the Benchmark of Information Retrieval (IR) tasks with Complex Objectives (BIRCO). BIRCO evaluates the ability of IR systems to retrieve documents given multi-faceted user objectives. The benchmark's complexity and compact size make it suitable for evaluating large language model (LLM)-based information retrieval systems. We present a modular framework for investigating factors that may influence LLM performance on retrieval tasks, and identify a simple baseline model which matches or outperforms existing approaches and more complex alternatives. No approach achieves satisfactory performance on all benchmark tasks, suggesting that stronger models and new retrieval protocols are necessary to address complex user needs.
2D Matryoshka Sentence Embeddings

Common approaches rely on fixed-length embedding vectors from language models as sentence embeddings for downstream tasks such as semantic textual similarity (STS). Such methods are limited in their flexibility due to unknown computational constraints and budgets across various applications. Matryoshka Representation Learning (MRL) (Kusupati et al., 2022) encodes information at finer granularities, i.e., with lower embedding dimensions, to adaptively accommodate ad hoc tasks. Similar accuracy can be achieved with a smaller embedding size, leading to speedups in downstream tasks. Despite its improved efficiency, MRL still requires traversing all Transformer layers before obtaining the embedding, which remains the dominant factor in time and memory consumption. This prompts consideration of whether the fixed number of Transformer layers affects representation quality and whether using intermediate layers for sentence representation is feasible. In this paper, we introduce a novel sentence embedding model called Two-dimensional Matryoshka Sentence Embedding (2DMSE). It supports elastic settings for both embedding sizes and Transformer layers, offering greater flexibility and efficiency than MRL. We conduct extensive experiments on STS tasks and downstream applications. The experimental results demonstrate the effectiveness of our proposed model in dynamically supporting different embedding sizes and Transformer layers, allowing it to be highly adaptable to various scenarios.
Engineering and Revealing Dirac Strings in Spinor Condensates

Artificial monopoles have been engineered in various systems, yet there has been no systematic study on the singular vector potentials associated with the monopole field. We show that the Dirac string, the line singularity of the vector potential, can be engineered, manipulated, and made manifest in a spinor atomic condensate. We elucidate the connection among spin, orbital degrees of freedom, and the artificial gauge, and reveal that there exists a mapping between the vortex filament and the Dirac string. We also devise a proposal where preparing initial spin states with relevant symmetries can result in different vortex patterns, revealing an underlying correspondence between the internal spin states and the spherical vortex structures. Such a mapping also leads to a new way of constructing monopole harmonics. Our observation provides significant insights in quantum matter possessing internal symmetries in curved spaces.
Shuttling for Scalable Trapped-Ion Quantum Computers

Trapped-ion quantum computers exhibit promising potential to provide platforms for high-quality qubits and reliable quantum computation. The Quantum Charge Coupled Device (QCCD) architecture offers a modular solution to enable the realization of scalable quantum computers, paving the way for practical quantum algorithms with large qubit numbers. Within these devices, ions can be shuttled (moved) throughout the trap and through different dedicated zones, e.g., a memory zone for storage and a processing zone for the actual computation. However, due to the decoherence of the ions' quantum states, the qubits lose their quantum information over time. Thus, the required time steps of shuttling operations should be minimized. In this paper, we propose a heuristic approach to finding an efficient shuttling schedule, which orchestrates the movement operations within the device. Given a quantum algorithm and a device architecture, the proposed algorithm produces shuttling schedules with a close-to-minimal amount of time steps for small-size QCCD architectures. Furthermore, even for large-scale QCCD devices, the empirical evaluation shows promising results with respect to the quality of the solution as well as performance.
Investigations of optical aberration on quantum diamond microscopy toward high spatial resolution and sensitivity

Quantum diamond microscopy (QDM), which employs nitrogen-vacancy (NV) center ensembles, is a promising approach to quantitatively imaging magnetic fields with both high resolution that approaches the diffraction limit and a wide field of view. The commonly adopted setups of QDM capture the photoluminescence through transparent diamonds, which inevitably entail aberrations -- optical errors that degrade the optical resolution and contrast of the obtainable image. In this study, we delve into the impact of optical aberrations, focusing on their dependence on diamond thickness. We first introduce a rigorous model [Richards et al., Braat et al.] of diffraction that incorporates aberrations, producing the NV center optical image. We confirm that this model accurately reproduces the confocal images of single NV centers obtained at various depths in diamonds. Extending this model to a wide-field microscope, we find that the model also accurately reproduces the USAF 1951 resolution test chart obtained through diamonds of various thicknesses. Based on these investigations, we quantitatively assess the consequent resolution constraints and propose thinning the diamond as a viable solution. We present a robust method to quantitatively ascertain resolution in optical systems influenced by aberrations caused by ray transmission through diamonds. For instance, for a typical microscope with an objective lens of NA = 0.7, the diffraction limit is achievable through diamonds that are 30 {\mu}m thick, and a resolution of 1 {\mu}m is obtained through diamonds that are 100 {\mu}m thick. Those results opens up avenues for enhanced performance in QDM.
Zero-shot cross-lingual transfer in instruction tuning of large language model

Instruction tuning (IT) is widely used to teach pretrained large language models (LLMs) to follow arbitrary instructions, but is under-studied in multilingual settings. In this work, we conduct a systematic study of zero-shot cross-lingual transfer in IT, when an LLM is instruction-tuned on English-only data and then tested on user prompts in other languages. We investigate the influence of model configuration choices and devise a multi-facet evaluation strategy for multilingual instruction following. We find that cross-lingual transfer does happen successfully in IT even if all stages of model training are English-centric, but only if multiliguality is taken into account in hyperparameter tuning and with large enough IT data. English-trained LLMs are capable of generating correct-language, comprehensive and helpful responses in the other languages, but suffer from low factuality and may occasionally have fluency errors.
From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter

In the rapidly evolving landscape of social media, the introduction of new emojis in Unicode release versions presents a structured opportunity to explore digital language evolution. Analyzing a large dataset of sampled English tweets, we examine how newly released emojis gain traction and evolve in meaning. We find that community size of early adopters and emoji semantics are crucial in determining their popularity. Certain emojis experienced notable shifts in the meanings and sentiment associations during the diffusion process. Additionally, we propose a novel framework utilizing language models to extract words and pre-existing emojis with semantically similar contexts, which enhances interpretation of new emojis. The framework demonstrates its effectiveness in improving sentiment classification performance by substituting unknown new emojis with familiar ones. This study offers a new perspective in understanding how new language units are adopted, adapted, and integrated into the fabric of online communication.
Towards homological projective duality for $\mathrm{Gr}(2, 2n)$

Consider a Grassmannian $\mathrm{Gr}(2, V)$ for an even-dimensional vector space $V$. Its derived category of coherent sheaves has a Lefschetz exceptional collection with respect to the Pl\"ucker embedding. We consider a variety $X_1$ of pairs consisting of a degenerate $2$-form on $V$ and a line in its kernel. Note that $X_1$ is generically a $\mathbb{P}^1$-fibration over the Pfaffian variety of degenerate $2$-forms on $V$. We construct an exceptional collection of coherent sheaves on $X_1$ such that the subcategory of $D^b_{\mathrm{coh}}(X_1)$ generated by that collection is conjecturally equivalent to the homologically projectively dual category of the Grassmannian.
Observation of Surface State Suppression in Magnetic Weyl Semimetal NdAlSi

Understanding and mastering the control of surface states in topological materials are crucial steps for the development of future electronic devices and technologies that leverage the unique properties of these materials. Here, using angle-resolved photoemission spectroscopy, we provide a good case study of this by visualizing the electronic structure of a magnetic Weyl semimetal on both flat and uneven surfaces. Our observations reveal that the preparation of an uneven sample surface can effectively suppress all surface states in the Weyl semimetal NdAlSi, including topological surface Fermi arcs. This results in the observation of pure bulk states devoid of any Fermi energy shift. This discovery not only opens up a new avenue to directly study the pure bulk states of a Weyl semimetal using low photon energies in ARPES but also provides key insights into the control of the surface states in topological materials.
Spatially continuous mapping of daily global ozone distribution (2004–2014) with the Aura OMI sensor

Total ozone data from the Aura Ozone Monitoring Instrument (OMI) play an important role in the monitoring of the spatial distribution and temporal change of total ozone. However, since September 2005, and especially after mid‐2006, due to row anomalies in the OMI instrument, one third to one half of the OMI total ozone data has been missing. In this study, we generate a spatially continuous and daily global total ozone product (2004–2014) by quantitatively reconstructing the level 3 (gridded) total ozone data via a new two‐step method, which takes full advantage of the temporal and spatial correlation characteristics. First, a preliminary prediction is made based on an adaptive weighted temporal fitting method. Residual correction based on an anisotropic kriging method is then proposed to further improve the prediction accuracy. To assess the efficacy of the proposed method, a comparison of different gap filling algorithms through a series of simulated experiments was performed. On this basis, we further evaluated the proposed product with Brewer spectrophotometers' total ozone columns. The evaluation results suggest that the proposed method outperforms the other algorithms, and its product is better able to capture total ozone variation than the MERRA‐2 assimilated ozone product. The total ozone product produced in this study can be freely downloaded from http://sendimage.whu. edu.cn/send‐resource‐download/.
On the communication complexity of finding a king in a tournament

A tournament is a complete directed graph. A king in a tournament is a vertex v such that every other vertex is reachable from v via a path of length at most 2. It is well known that every tournament has at least one king, one of which is a maximum out-degree vertex. The tasks of finding a king, a maximum out-degree vertex and a source in a tournament has been relatively well studied in the context of query complexity. We study the communication complexity of these tasks, where the edges are partitioned between two players. The following are our main results for n-vertex tournaments: 1) The deterministic communication complexity of finding whether a source exists is tilde{Theta}(log^2 n). 2) The deterministic and randomized communication complexities of finding a king are Theta(n). The quantum communication complexity is tilde{Theta}(sqrt{n}). 3) The deterministic, randomized and quantum communication complexities of finding a maximum out-degree vertex are Theta(n log n), tilde{Theta}(n) and tilde{Theta}(sqrt{n}), respectively. Our upper bounds hold for all partitions of edges, and the lower bounds for a specific partition of the edges. To show the first bullet above, we show, perhaps surprisingly, that finding a source in a tournament is equivalent to the well-studied Clique vs. Independent Set (CIS) problem on undirected graphs. Our bounds for finding a source then follow from known bounds on the complexity of the CIS problem. In view of this equivalence, we can view the task of finding a king in a tournament to be a natural generalization of CIS. One of our lower bounds uses a fooling-set based argument, and all our other lower bounds follow from carefully-constructed reductions from Set-Disjointness.