Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Modeling of Data Storage Systems using Generative Models

High-precision modeling of systems is one of the main areas of industrial data analysis. Models of systems, their digital twins, are used to predict their behavior under various conditions. We have developed several models of a storage system using machine learning-based generative models. The system consists of several components: hard disk drive (HDD) and solid-state drive (SSD) storage pools with different RAID schemes and cache. Each storage component is represented by a probabilistic model that describes the probability distribution of the component performance in terms of IOPS and latency, depending on their configuration and external data load parameters. The results of the experiments demonstrate the errors of 4-10 % for IOPS and 3-16 % for latency predictions depending on the components and models of the system. The predictions show up to 0.99 Pearson correlation with Little's law, which can be used for unsupervised reliability checks of the models. In addition, we present novel data sets that can be used for benchmarking regression algorithms, conditional generative models, and uncertainty estimation methods in machine learning.
SpaceNLI: Evaluating the Consistency of Predicting Inferences In Space

While many natural language inference (NLI) datasets target certain semantic phenomena, e.g., negation, tense & aspect, monotonicity, and presupposition, to the best of our knowledge, there is no NLI dataset that involves diverse types of spatial expressions and reasoning. We fill this gap by semi-automatically creating an NLI dataset for spatial reasoning, called SpaceNLI. The data samples are automatically generated from a curated set of reasoning patterns (see Figure 1), where the patterns are annotated with inference labels by experts. We test several SOTA NLI systems on SpaceNLI to gauge the complexity of the dataset and the system’s capacity for spatial reasoning. Moreover, we introduce a Pattern Accuracy and argue that it is a more reliable and stricter measure than the accuracy for evaluating a system’s performance on pattern-based generated data samples. Based on the evaluation results we find that the systems obtain moderate results on the spatial NLI problems but lack consistency per inference pattern. The results also reveal that non-projective spatial inferences (especially due to the “between” preposition) are the most challenging ones.
Strictifying Operational Coherences and Weak Functor Classifiers in Low Dimensions

Weak structures abound in higher category theory, but are often suitably equivalent to stricter structures that are easier to understand. We extend strictification for tricategories and trihomomorphisms to trinatural transformations, trimodifications and perturbations. Along the way we distinguish between the operational coherences, which are possible to strictify, and the coherences on globular inputs, which remain weak. We introduce generalised path objects for $\mathbf{Gray}$-categories, which help reduce proofs in the three-dimensional setting to known results. Upon closing the resulting semi-strict trinatural transformations under composition, we state the hom-triequivalences of what we expect to be a `semi-strictification tetra-adjunction'.
Statistical properties and lensing effect on the repeating fast radio burst FRB 180916.J0158+65

FRB 180916.J0158+65 is a well-known repeating fast radio burst with a period ($16.35~\rm d$) and an active window ($5.0~\rm d$). We give out the statistical results of the dispersion measures and waiting times of bursts of FRB 180916.J0158+65. We find the dispersion measures at the different frequencies show a bimodal distribution. The peaking dispersion measures of the left mode of the bimodal distributions increase with frequency, but the right one is inverse. The waiting times also present the bimodal distribution, peaking at 0.05622 and 1612.91266 s. The peaking time is irrelevant to the properties of bursts, either for the preceding or subsequent burst. By comparing the statistical results with possible theoretical models, we suggest that FRB 180916.J0158+65 suffered from the plasma lensing effects in the propagation path. Moreover, this source may be originated from a highly magnetized neutron star in a high-mass X-ray binary.
Co-creating a Transdisciplinary Map of Technology-mediated Harms, Risks and Vulnerabilities: Challenges, Ambivalences and Opportunities

The phrase "online harms'' has emerged in recent years out of a growing political willingness to address the ethical and social issues associated with the use of the Internet and digital technology at large. The broad landscape that surrounds online harms gathers a multitude of disciplinary, sectoral and organizational efforts while raising myriad challenges and opportunities for the crossing entrenched boundaries. In this paper we draw lessons from a journey of co-creating a transdisciplinary knowledge infrastructure within a large research initiative animated by the online harms agenda. We begin with a reflection of the implications of mapping, taxonomizing and constructing knowledge infrastructures and a brief review of how online harm and adjacent themes have been theorized and classified in the literature to date. Grounded on our own experience of co-creating a map of online harms, we then argue that the map---and the process of mapping---perform three mutually constitutive functions, acting simultaneously as method, medium and provocation. We draw lessons from how an open-ended approach to mapping, despite not guaranteeing consensus, can foster productive debate and collaboration in ethically and politically fraught areas of research. We end with a call for CSCW research to surface and engage with the multiple temporalities, social lives and political sensibilities of knowledge infrastructures.
Power-up! What Can Generative Models Do for Human Computation Workflows?

We are amidst an explosion of artificial intelligence research, particularly around large language models (LLMs). These models have a range of applications across domains like medicine, finance, commonsense knowledge graphs, and crowdsourcing. Investigation into LLMs as part of crowdsourcing workflows remains an under-explored space. The crowdsourcing research community has produced a body of work investigating workflows and methods for managing complex tasks using hybrid human-AI methods. Within crowdsourcing, the role of LLMs can be envisioned as akin to a cog in a larger wheel of workflows. From an empirical standpoint, little is currently understood about how LLMs can improve the effectiveness of crowdsourcing workflows and how such workflows can be evaluated. In this work, we present a vision for exploring this gap from the perspectives of various stakeholders involved in the crowdsourcing paradigm -- the task requesters, crowd workers, platforms, and end-users. We identify junctures in typical crowdsourcing workflows at which the introduction of LLMs can play a beneficial role and propose means to augment existing design patterns for crowd work.
EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models

While the general machine learning (ML) community has benefited from public datasets, tasks, and models, the progress of ML in healthcare has been hampered by a lack of such shared assets. The success of foundation models creates new challenges for healthcare ML by requiring access to shared pretrained models to validate performance benefits. We help address these challenges through three contributions. First, we publish a new dataset, EHRSHOT, which contains deidentified structured data from the electronic health records (EHRs) of 6,739 patients from Stanford Medicine. Unlike MIMIC-III/IV and other popular EHR datasets, EHRSHOT is longitudinal and not restricted to ICU/ED patients. Second, we publish the weights of CLMBR-T-base, a 141M parameter clinical foundation model pretrained on the structured EHR data of 2.57M patients. We are one of the first to fully release such a model for coded EHR data; in contrast, most prior models released for clinical data (e.g. GatorTron, ClinicalBERT) only work with unstructured text and cannot process the rich, structured data within an EHR. We provide an end-to-end pipeline for the community to validate and build upon its performance. Third, we define 15 few-shot clinical prediction tasks, enabling evaluation of foundation models on benefits such as sample efficiency and task adaptation. Our model and dataset are available via a research data use agreement from the Stanford AIMI Center. Code to reproduce our results are available at our Github repo: https://github.com/som-shahlab/ehrshot-benchmark
An envy-free online UAV charging scheme with vehicle-mounted mobile wireless chargers

In commercial unmanned aerial vehicle (UAV) applications, one of the main restrictions is UAVs' limited battery endurance when executing persistent tasks. With the mature of wireless power transfer (WPT) technologies, by leveraging ground vehicles mounted with WPT facilities on their proofs, we propose a mobile and collaborative recharging scheme for UAVs in an on-demand manner. Specifically, we first present a novel air-ground cooperative UAV recharging framework, where ground vehicles cooperatively share their idle wireless chargers to UAVs and a swarm of UAVs in the task area compete to get recharging services. Considering the mobility dynamics and energy competitions, we formulate an energy scheduling problem for UAVs and vehicles under practical constraints. A fair online auction-based solution with low complexity is also devised to allocate and price idle wireless chargers on vehicular proofs in real time. We rigorously prove that the proposed scheme is strategy-proof, envy-free, and produces stable allocation outcomes. The first property enforces that truthful bidding is the dominant strategy for participants, the second ensures that no user is better off by exchanging his allocation with another user when the auction ends, while the third guarantees the matching stability between UAVs and UGVs. Extensive simulations validate that the proposed scheme outperforms benchmarks in terms of energy allocation efficiency and UAV's utility.
Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources

Traditionally, data selection has been studied in settings where all samples from prospective sources are fully revealed to a machine learning developer. However, in practical data exchange scenarios, data providers often reveal only a limited subset of samples before an acquisition decision is made. Recently, there have been efforts to fit scaling laws that predict model performance at any size and data source composition using the limited available samples. However, these scaling functions are black-box, computationally expensive to fit, highly susceptible to overfitting, or/and difficult to optimize for data selection. This paper proposes a framework called, which predicts model performance and supports data selection decisions based on partial samples of prospective data sources. Our approach distinguishes itself from existing work by introducing a novel *two-stage* performance inference process. In the first stage, we leverage the Optimal Transport distance to predict the model's performance for any data mixture ratio within the range of disclosed data sizes. In the second stage, we extrapolate the performance to larger undisclosed data sizes based on a novel parameter-free mapping technique inspired by neural scaling laws. We further derive an efficient gradient-based method to select data sources based on the projected model performance. Evaluation over a diverse range of applications demonstrates thatsignificantly improves existing performance scaling approaches in terms of both the accuracy of performance inference and the computation costs associated with constructing the performance predictor. Also,outperforms by a wide margin in data selection effectiveness compared to a range of other off-the-shelf solutions.
Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning

The availability of large amounts of informative data is crucial for successful machine learning. However, in domains with sensitive information, the release of high-utility data which protects the privacy of individuals has proven challenging. Despite progress in differential privacy and generative modeling for privacy-preserving data release in the literature, only a few approaches optimize for machine learning utility: most approaches only take into account statistical metrics on the data itself and fail to explicitly preserve the loss metrics of machine learning models that are to be subsequently trained on the generated data. In this paper, we introduce a data release framework, 3A (Approximate, Adapt, Anonymize), to maximize data utility for machine learning, while preserving differential privacy. We also describe a specific implementation of this framework that leverages mixture models to approximate, kernel-inducing points to adapt, and Gaussian differential privacy to anonymize a dataset, in order to ensure that the resulting data is both privacy-preserving and high utility. We present experimental evidence showing minimal discrepancy between performance metrics of models trained on real versus privatized datasets, when evaluated on held-out real data. We also compare our results with several privacy-preserving synthetic data generation models (such as differentially private generative adversarial networks), and report significant increases in classification performance metrics compared to state-of-the-art models. These favorable comparisons show that the presented framework is a promising direction of research, increasing the utility of low-risk synthetic data release for machine learning.
Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities

Although we have witnessed impressive progress in Semantic Role Labeling (SRL), most of the research in the area is carried out assuming that the majority of predicates are verbs. Conversely, predicates can also be expressed using other parts of speech, e.g., nouns and adjectives. However, non-verbal predicates appear in the benchmarks we commonly use to measure progress in SRL less frequently than in some real-world settings -- newspaper headlines, dialogues, and tweets, among others. In this paper, we put forward a new PropBank dataset which boasts wide coverage of multiple predicate types. Thanks to it, we demonstrate empirically that standard benchmarks do not provide an accurate picture of the current situation in SRL and that state-of-the-art systems are still incapable of transferring knowledge across different predicate types. Having observed these issues, we also present a novel, manually-annotated challenge set designed to give equal importance to verbal, nominal, and adjectival predicate-argument structures. We use such dataset to investigate whether we can leverage different linguistic resources to promote knowledge transfer. In conclusion, we claim that SRL is far from"solved", and its integration with other semantic tasks might enable significant improvements in the future, especially for the long tail of non-verbal predicates, thereby facilitating further research on SRL for non-verbal predicates.
With Trail to Follow: Measurements of Real-world Non-fungible Token Phishing Attacks on Ethereum

With the popularity of Non-Fungible Tokens (NFTs), NFTs have become a new target of phishing attacks, posing a significant threat to the NFT trading ecosystem. There has been growing anecdotal evidence that new means of NFT phishing attacks have emerged in Ethereum ecosystem. Most of the existing research focus on detecting phishing scam accounts for native cryptocurrency on the blockchain, but there is a lack of research in the area of phishing attacks of emerging NFTs. Although a few studies have recently started to focus on the analysis and detection of NFT phishing attacks, NFT phishing attack means are diverse and little has been done to understand these various types of NFT phishing attacks. To the best of our knowledge, we are the first to conduct case retrospective analysis and measurement study of real-world historical NFT phishing attacks on Ethereum. By manually analyzing the existing scams reported by Chainabuse, we classify NFT phishing attacks into four patterns. For each pattern, we further investigate the tricks and working principles of them. Based on 469 NFT phishing accounts collected up until October 2022 from multiple channels, we perform a measurement study of on-chain transaction data crawled from Etherscan to characterizing NFT phishing scams by analyzing the modus operandi and preferences of NFT phishing scammers, as well as economic impacts and whereabouts of stolen NFTs. We classify NFT phishing transactions into one of the four patterns by log parsing and transaction record parsing. We find these phishing accounts stole 19,514 NFTs for a total profit of 8,858.431 ETH (around 18.57 million dollars). We also observe that scammers remain highly active in the last two years and favor certain categories and series of NFTs, accompanied with signs of gang theft.
Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning

We consider the problem of learning models for risk-sensitive reinforcement learning. We theoretically demonstrate that proper value equivalence, a method of learning models which can be used to plan optimally in the risk-neutral setting, is not sufficient to plan optimally in the risk-sensitive setting. We leverage distributional reinforcement learning to introduce two new notions of model equivalence, one which is general and can be used to plan for any risk measure, but is intractable; and a practical variation which allows one to choose which risk measures they may plan optimally for. We demonstrate how our framework can be used to augment any model-free risk-sensitive algorithm, and provide both tabular and large-scale experiments to demonstrate its ability.
Grammatical parallelism effect in anaphora resolution: Using data from russian to choose between theoretical approaches

In the present study we explored structural parallelism, the preference of hearers to connect an unaccented pronoun to a referent occupying the same syntactic position. The traditional linguistic approach is based on the fact that referential preferences are associated with specific linguistic properties of potential antecedents of pronouns. Discourse-coherent approach is based on a hearer`s coherence-driven expectations about discourse continuation and supposes a structural parallelism effect to be a by-product of establishing relations, which provide the coherence of discourse. From this point of view, parallel reading is caused by information structure. In order to investigate the role of grammatical and information structures in the parallelism effect, and to choose between the theoretical approaches, we addressed a flexible word-order language, which has several ways of focusing, such as Russian. The two experiments demonstrated that the use of non-contrastive focusing strategy reveals parallelism bias to be equally strong for both subject-subject and object-object dependencies. We found that syntactic roles’ congruence is insufficient for the parallelism effect. Instead, parallel elements are required to occupy the topical position in the information structure of their clauses to provide the parallel reading. This evidence showed that structural parallelism effect is driven by information structure and is a by-product of establishing more general discourse relations, which provide its coherence.
DeepFlorist: Rethinking Deep Neural Networks and Ensemble Learning as A Meta-Classifier For Object Classification

In this paper, we propose a novel learning paradigm called"DeepFlorist"for flower classification using ensemble learning as a meta-classifier. DeepFlorist combines the power of deep learning with the robustness of ensemble methods to achieve accurate and reliable flower classification results. The proposed network architecture leverages a combination of dense convolutional and convolutional neural networks (DCNNs and CNNs) to extract high-level features from flower images, followed by a fully connected layer for classification. To enhance the performance and generalization of DeepFlorist, an ensemble learning approach is employed, incorporating multiple diverse models to improve the classification accuracy. Experimental results on benchmark flower datasets demonstrate the effectiveness of DeepFlorist, outperforming state-of-the-art methods in terms of accuracy and robustness. The proposed framework holds significant potential for automated flower recognition systems in real-world applications, enabling advancements in plant taxonomy, conservation efforts, and ecological studies.
Dissipation, quantum coherence, and asymmetry of finite-time cross-correlations

Recent studies have revealed a deep connection between the asymmetry of cross-correlations and thermodynamic quantities in the short-time limit. In this study, we address the finite-time domain of the asymmetry for both open classical and quantum systems. Focusing on Markovian dynamics, we show that the asymmetry observed in finite-time cross-correlations is upper bounded by dissipation. We prove that, for classical systems in a steady state with arbitrary operational durations, the asymmetry exhibits, at most, linear growth over time, with the growth speed determined by the rates of entropy production and dynamical activity. In the long-time regime, the asymmetry exhibits exponential decay, with the decay rate determined by the spectral gap of the transition matrix. Remarkably, for quantum cases, quantum coherence is equally important as dissipation in constraining the asymmetry of correlations. We demonstrate an example where only quantum coherence bounds the asymmetry while the entropy production rate vanishes. Furthermore, we generalize the short-time bounds on correlation asymmetry, as reported by Shiraishi [Phys. Rev. E 108, L042103 (2023)] and Ohga et al. [Phys. Rev. Lett. 131, 077101 (2023)], to encompass finite-time scenarios. These findings offer novel insights into the thermodynamic aspects of correlation asymmetry.
On the connections between the spatial Lambda-Fleming-Viot model and other processes for analysing geo-referenced genetic data

The introduction of the spatial Lambda-Fleming-Viot model (LV) in population genetics was mainly driven by the pioneering work of Alison Etheridge, in collaboration with Nick Barton and Amandine V\'eber about ten years ago (1,2). The LV model provides a sound mathematical framework for describing the evolution of a population of related individuals along a spatial continuum. It alleviates the"pain in the torus"issue with Wright and Mal\'ecot's isolation by distance model and is sampling consistent, making it a tool of choice for statistical inference. Yet, little is known about the potential connections between the LV and other stochastic processes generating trees and the spatial coordinates along the corresponding lineages. This work focuses on a version of the LV whereby lineages move infinitely rapidly over infinitely small distances. Using simulations, we show that the induced LV tree-generating process is well approximated by a birth-death model. Our results also indicate that Brownian motions modelling the movements of lineages along birth-death trees do not generally provide a good approximation of the LV due to habitat boundaries effects that play an increasingly important role in the long run. Finally, we describe efficient algorithms for fast simulation of the backward and forward in time versions of the LV model.