Miracle-dz/UniFAR-FTUs huggingface dataset:
Centrality of the Fingerprint Core Location

Fingerprints have long been recognized as a unique and reliable means of personal identification. Central to the analysis and enhancement of fingerprints is the concept of the fingerprint core. Although the location of the core is used in many applications, to the best of our knowledge, this study is the first to investigate the empirical distribution of the core over a large, combined dataset of rolled, as well as plain fingerprint recordings. We identify and investigate the extent of incomplete rolling during the rolled fingerprint acquisition and investigate the centrality of the core. After correcting for the incomplete rolling, we find that the core deviates from the fingerprint center by 5.7% $\pm$ 5.2% to 7.6% $\pm$ 6.9%, depending on the finger. Additionally, we find that the assumption of normal distribution of the core position of plain fingerprint recordings cannot be rejected, but for rolled ones it can. Therefore, we use a multi-step process to find the distribution of the rolled fingerprint recordings. The process consists of an Anderson-Darling normality test, the Bayesian Information Criterion to reduce the number of possible candidate distributions and finally a Generalized Monte Carlo goodness-of-fit procedure to find the best fitting distribution. We find the non-central Fischer distribution best describes the cores' horizontal positions. Finally, we investigate the correlation between mean core position offset and the NFIQ 2 score and find that the NFIQ 2 prefers rolled fingerprint recordings where the core sits slightly below the fingerprint center.
Exponential Quantum Communication Advantage in Distributed Learning

Training and inference with large machine learning models that far exceed the memory capacity of individual devices necessitates the design of distributed architectures, forcing one to contend with communication constraints. We present a framework for distributed computation over a quantum network in which data is encoded into specialized quantum states. We prove that for certain models within this framework, inference and training using gradient descent can be performed with exponentially less communication compared to their classical analogs, and with relatively modest time and space complexity overheads relative to standard gradient-based methods. To our knowledge, this is the first example of exponential quantum advantage for a generic class of machine learning problems with dense classical data that holds regardless of the data encoding cost. Moreover, we show that models in this class can encode highly nonlinear features of their inputs, and their expressivity increases exponentially with model depth. We also find that, interestingly, the communication advantage nearly vanishes for simpler linear classifiers. These results can be combined with natural privacy advantages in the communicated quantum states that limit the amount of information that can be extracted from them about the data and model parameters. Taken as a whole, these findings form a promising foundation for distributed machine learning over quantum networks.
ObliuSky: Oblivious User-Defined Skyline Query Processing in the Cloud

The proliferation of cloud computing has greatly spurred the popularity of outsourced database storage and management, in which the cloud holding outsourced databases can process database queries on demand. Among others, skyline queries play an important role in the database field due to its prominent usefulness in multi-criteria decision support systems. To accommodate the tailored needs of users, user-defined skyline query has recently emerged as an intriguing advanced type of skyline query, which allows users to define custom preferences in their skyline queries (including the target attributes, preferred dominance relations, and range constraints on the target attributes). However, user-defined skyline query services, if deployed in the cloud, may raise critical privacy concerns as the outsourced databases and skyline queries may contain proprietary/privacy-sensitive information, and the cloud might even suffer from data breaches. In light of the above, this paper presents ObliuSky, a new system framework enabling oblivious user-defined skyline query processing in the cloud. ObliuSky departs from the state-of-the-art prior work by not only providing confidentiality protection for the content of the outsourced database, the user-defined skyline query, and the query results, but also making the cloud oblivious to the data patterns (e.g., user-defined dominance relations among database points and search access patterns) which may indirectly cause data leakages. We formally analyze the security guarantees and conduct extensive performance evaluations. The results show that while achieving much stronger security guarantees than the state-of-the-art prior work, ObliuSky is superior in database and query encryption efficiency, with practically affordable query latency.
Structure of the Earth's Exosphere

The main components of the earth's exosphere are neutral oxygen, neutral hydrogen atoms, ionized oxygen, and ionized hydrogen. The position of the base of the exosphere is established at an altitude of 530 km, from an analysis of density data obtained from satellite drag observations. The relative distribution of both neutral oxygen and neutral hydrogen is derived from a theory of the exosphere. The normalization of oxygen is accomplished by means of satellite drag data. The normalization of hydrogen is accomplished by means of the Lyman-α technique of Johnson, Friedman, and Tousey. The distribution of ionized oxygen can be established jointly from the rocket measurements of Berning and from an analysis of radiation belt data obtained from satellites. They give a consistent picture of ionized oxygen becoming the most important constituent of the atmosphere at about 1500 km. The distribution of ionized hydrogen is only imperfectly known.
Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation

Target-oriented dialogue systems, designed to proactively steer conversations toward predefined targets or accomplish specific system-side goals, are an exciting area in conversational AI. In this work, by formulating apair as the conversation target, we explore a novel problem of personalized target-oriented dialogue by considering personalization during the target accomplishment process. However, there remains an emergent need for high-quality datasets, and building one from scratch requires tremendous human effort. To address this, we propose an automatic dataset curation framework using a role-playing approach. Based on this framework, we construct a large-scale personalized target-oriented dialogue dataset, TopDial, which comprises about 18K multi-turn dialogues. The experimental results show that this dataset is of high quality and could contribute to exploring personalized target-oriented dialogue.
for text summary

—in today's rapidly growing information age, text summary has become a critical and important instrument for help understanding text information. it is really hard for human beings to physically summarize huge textual documents also there is an abundance of text content available online. text summarization is an active research field that works on compressing large pieces of text into smaller texts that preserve relevant information. text summary classified as extractive or abstractive. methods of extractive summarization working by deciding important text sentences and choosing them as a summary. that method based only on sentences from the source text. methods of abstractive summarization aim to paraphrase important information in a new form like that of humans. text summary can be achieved using different deep learning techniques, such as: fuzzy logic, Convolutional Neural (CNN), transformers, neural network, reinforcement learning, etc. in the past three years, the research trend in text summarization has also undergone a slight change, where new trends have appeared that are trends that lead to enhancement, how to improve the efficiency of text summarization to obtain high accuracy. we have made several attempts in this paper to discuss the various techniques used on the basis of deep learning for text summary in these years and observe the new trends in the field of deep learning
PlatoSim: An end-to-end PLATO camera simulator for modelling high-precision space-based photometry

PLAnetary Transits and Oscillations of stars (PLATO) is the ESA M3 space mission dedicated to detect and characterise transiting exoplanets including information from the asteroseismic properties of their stellar hosts. The uninterrupted and high-precision photometry provided by space-borne instruments such as PLATO require long preparatory phases. An exhaustive list of tests are paramount to design a mission that meets the performance requirements, and as such, simulations are an indispensable tool in the mission preparation. To accommodate PLATO's need of versatile simulations prior to mission launch - that at the same time describe accurately the innovative but complex multi-telescope design - we here present the end-to-end PLATO simulator specifically developed for the purpose, namely PlatoSim. We show step-by-step the algorithms embedded into the software architecture of PlatoSim that allow the user to simulate photometric time series of CCD images and light curves in accordance to the expected observations of PLATO. In the context of the PLATO payload, a general formalism of modelling, end-to-end, incoming photons from the sky to the final measurement in digital units is discussed. We show the strong predictive power of PlatoSim through its diverse applicability and contribution to numerous working groups within the PLATO Mission Consortium. This involves the on-going mechanical integration and alignment, performance studies of the payload, the pipeline development and assessments of the scientific goals. PlatoSim is a state-of-the-art simulator that is able to produce the expected photometric observations of PLATO to a high level of accuracy. We demonstrate that PlatoSim is a key software tool for the PLATO mission in the preparatory phases until mission launch and prospectively beyond.
Prospective Side Information for Latent MDPs

In many interactive decision-making settings, there is latent and unobserved information that remains fixed. Consider, for example, a dialogue system, where complete information about a user, such as the user's preferences, is not given. In such an environment, the latent information remains fixed throughout each episode, since the identity of the user does not change during an interaction. This type of environment can be modeled as a Latent Markov Decision Process (LMDP), a special instance of Partially Observed Markov Decision Processes (POMDPs). Previous work established exponential lower bounds in the number of latent contexts for the LMDP class. This puts forward a question: under which natural assumptions a near-optimal policy of an LMDP can be efficiently learned? In this work, we study the class of LMDPs with {\em prospective side information}, when an agent receives additional, weakly revealing, information on the latent context at the beginning of each episode. We show that, surprisingly, this problem is not captured by contemporary settings and algorithms designed for partially observed environments. We then establish that any sample efficient algorithm must suffer at least $\Omega(K^{2/3})$-regret, as opposed to standard $\Omega(\sqrt{K})$ lower bounds, and design an algorithm with a matching upper bound.
Computing approximate roots of monotone functions

Given a function f: [a,b] ->R, if f(a)<0 and f(b)>0 and f is continuous, the Intermediate Value Theorem implies that f has a root in [a,b]. Moreover, given a value-oracle for f, an approximate root of f can be computed using the bisection method, and the number of required evaluations is polynomial in the number of accuracy digits. The goal of this note is to identify conditions under which this polynomiality result extends to a multi-dimensional function that satisfies the conditions of Miranda's theorem -- the natural multi-dimensional extension of the Intermediate Value Theorem. In general, finding an approximate root might require an exponential number of evaluations even for a two-dimensional function. We show that, if f is two-dimensional and satisfies a single monotonicity condition, then the number of required evaluations is polynomial in the accuracy. For any fixed dimension d, if f is a d-dimensional function that satisfies all d^2-d ``ex-diagonal'' monotonicity conditions (that is, component i of f is monotonically decreasing with respect to variable j for all i! =j), then the number of required evaluations is polynomial in the accuracy. But if f satisfies only d^2-d-2 ex-diagonal conditions, then the number of required evaluations may be exponential in the accuracy. The case of d^2-d-1 ex-diagonal conditions remains unsolved. As an example application, we show that computing approximate roots of monotone functions can be used for approximate envy-free cake-cutting.
Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization

Generalization poses a significant challenge in Multi-agent Reinforcement Learning (MARL). The extent to which an agent is influenced by unseen co-players depends on the agent's policy and the specific scenario. A quantitative examination of this relationship sheds light on effectively training agents for diverse scenarios. In this study, we present the Level of Influence (LoI), a metric quantifying the interaction intensity among agents within a given scenario and environment. We observe that, generally, a more diverse set of co-play agents during training enhances the generalization performance of the ego agent; however, this improvement varies across distinct scenarios and environments. LoI proves effective in predicting these improvement disparities within specific scenarios. Furthermore, we introduce a LoI-guided resource allocation method tailored to train a set of policies for diverse scenarios under a constrained budget. Our results demonstrate that strategic resource allocation based on LoI can achieve higher performance than uniform allocation under the same computation budget.
Global Minima, Recoverability Thresholds, and Higher-Order Structure in GNNS

We analyze the performance of graph neural network (GNN) architectures from the perspective of random graph theory. Our approach promises to complement existing lenses on GNN analysis, such as combinatorial expressive power and worst-case adversarial analysis, by connecting the performance of GNNs to typical-case properties of the training data. First, we theoretically characterize the nodewise accuracy of one- and two-layer GCNs relative to the contextual stochastic block model (cSBM) and related models. We additionally prove that GCNs cannot beat linear models under certain circumstances. Second, we numerically map the recoverability thresholds, in terms of accuracy, of four diverse GNN architectures (GCN, GAT, SAGE, and Graph Transformer) under a variety of assumptions about the data. Sample results of this second analysis include: heavy-tailed degree distributions enhance GNN performance, GNNs can work well on strongly heterophilous graphs, and SAGE and Graph Transformer can perform well on arbitrarily noisy edge data, but no architecture handled sufficiently noisy feature data well. Finally, we show how both specific higher-order structures in synthetic data and the mix of empirical structures in real data have dramatic effects (usually negative) on GNN performance.
Simplifying the simulation of local Hamiltonian dynamics

Local Hamiltonians, $H_k$, describe non-trivial $k$-body interactions in quantum many-body systems. Here, we address the dynamical simulatability of a $k$-local Hamiltonian by a simpler one, $H_{k'}$, with $k'<k$, under the realistic constraint that both Hamiltonians act on the same Hilbert space. When it comes to exact simulation, we build upon known methods to derive examples of $H_k$ and $H_{k'}$ that simulate the same physics. We also address the most realistic case of approximate simulation. There, we upper-bound the error up to which a Hamiltonian can simulate another one, regardless of their internal structure, and prove, by means of an example, that the accuracy of a $(k'=2)$-local Hamiltonian to simulate $H_{k}$ with $k>2$ increases with $k$. Finally, we propose a method to search for the $k'$-local Hamiltonian that simulates, with the highest possible precision, the short time dynamics of a given $H_k$ Hamiltonian.
Distributed Transfer Learning with 4th Gen Intel Xeon Processors

In this paper, we explore how transfer learning, coupled with Intel Xeon, specifically 4th Gen Intel Xeon scalable processor, defies the conventional belief that training is primarily GPU-dependent. We present a case study where we achieved near state-of-the-art accuracy for image classification on a publicly available Image Classification TensorFlow dataset using Intel Advanced Matrix Extensions(AMX) and distributed training with Horovod.
Rotating Vector Model and Radius-to-frequency Mapping in the Presence of a Multipole Magnetic Field

The rotating vector model and radius-to-frequency mapping in the presence of a multipole magnetic field in pulsars and magnetars are considered. An axisymmetric potential field is assumed, and the following is found: (1) The radiation beam in the case of a multipole field is wider than the dipole case. This may account for the increasing pulse width at the higher frequency of pulsars (anti-radius-to-frequency mapping); (2) The expression for the polarization position angle is unchanged. Only the inclination angle α and phase constant ϕ 0 will change. The angle between the rotational axis and line of sight and the position angle constant ψ 0 will not change. When fitting the varying position angle of magnetars, these constraints should be considered. The appearance and disappearance of a multipole field may account for the changing slope of the position angle in the radio-emitting magnetar Swift J1818.0–1607. A similar but more active process in magnetar magnetospheres may account for the diverse position angle in fast radio bursts.
GRaMuFeN: Graph-based Multi-modal Fake News Detection in Social Media

The proliferation of social media platforms such as Twitter, Instagram, and Weibo has significantly enhanced the dissemination of false information. This phenomenon grants both individuals and governmental entities the ability to shape public opinions, highlighting the need for deploying effective detection methods. In this paper, we propose GraMuFeN, a model designed to detect fake content by analyzing both the textual and image content of news. GraMuFeN comprises two primary components: a text encoder and an image encoder. For textual analysis, GraMuFeN treats each text as a graph and employs a Graph Convolutional Neural Network (GCN) as the text encoder. Additionally, the pre-trained ResNet-152, as a Convolutional Neural Network (CNN), has been utilized as the image encoder. By integrating the outputs from these two encoders and implementing a contrastive similarity loss function, GraMuFeN achieves remarkable results. Extensive evaluations conducted on two publicly available benchmark datasets for social media news indicate a 10 % increase in micro F1-Score, signifying improvement over existing state-of-the-art models. These findings underscore the effectiveness of combining GCN and CNN models for detecting fake news in multi-modal data, all while minimizing the additional computational burden imposed by model parameters.
Benchmarking Deep Learning Fuzzers

In this work, we set out to conduct the first ground-truth empirical evaluation of state-of-the-art DL fuzzers. Specifically, we first manually created an extensive DL bug benchmark dataset, which includes 627 real-world DL bugs from TensorFlow and PyTorch libraries reported by users between 2020 and 2022. Then we run three state-of-the-art DL fuzzers, i.e., FreeFuzz, DeepRel, and DocTer, on the benchmark by following their instructions. We find that these fuzzers are unable to detect many real bugs collected in our benchmark dataset. Specifically, most (235) of the 257 applicable bugs cannot be detected by any fuzzer. Our systematic analysis further identifies four major, broad, and common factors that affect these fuzzers' ability to detect real bugs. These findings present opportunities to improve the performance of the fuzzers in future work. As a proof of concept, we propose a lightweight corner case generator as an extension to the three DL fuzzers, which simply covers several boundary values as well as DL-specific data types. It helps FreeFuzz, DeepRel, and DocTer detect 12, 12, and 14 more bugs, respectively, that were overlooked by the original fuzzers. Overall, this work complements prior studies on DL fuzzers with an extensive performance evaluation and provides a benchmark for future DL library fuzzing studies. Also, our proposed corner case generator proves that the fuzzers can be extended to detect more bugs by extending their internal fuzzing logic based on the insights provided in root cause analysis.
Prosody Analysis of Audiobooks

Recent advances in text-to-speech have made it possible to generate natural-sounding audio from text. However, audiobook narrations involve dramatic vocalizations and intonations by the reader, with greater reliance on emotions, dialogues, and descriptions in the narrative. Using our dataset of 93 aligned book-audiobook pairs, we present improved models for prosody prediction properties (pitch, volume, and rate of speech) from narrative text using language modeling. Our predicted prosody attributes correlate much better with human audiobook readings than results from a state-of-the-art commercial TTS system: our predicted pitch shows a higher correlation with human reading for 22 out of the 24 books, while our predicted volume attribute proves more similar to human reading for 23 out of the 24 books. Finally, we present a human evaluation study to quantify the extent that people prefer prosody-enhanced audiobook readings over commercial text-to-speech systems.
On Penrose's Analogy between Curved Spacetime Regions and Optical Lenses

We present a detailed analysis of Penrose's gravito-optical analogy between the focusing effects of particular families of Ricci- and Weyl-curved spacetime regions on the one hand, and anastigmatic and astigmatic optical lenses on the other. We put the analogy in its historical context, investigate its underlying assumptions, its range of validity, its proof of concept, and its application in Penrose's study of the notion of energy flux in general relativity. Finally, we examine the analogy within the framework of Norton's material theory of induction.
AI/ML-based Load Prediction in IEEE 802.11 Enterprise Networks

Enterprise Wi-Fi networks can greatly benefit from Artificial Intelligence and Machine Learning (AI/ML) thanks to their well-developed management and operation capabilities. At the same time, AI/ML-based traffic/load prediction is one of the most appealing data-driven solutions to improve the Wi-Fi experience, either through the enablement of autonomous operation or by boosting troubleshooting with forecasted network utilization. In this paper, we study the suitability and feasibility of adopting AI/ML-based load prediction in practical enterprise Wi-Fi networks. While leveraging AI/ML solutions can potentially contribute to optimizing Wi-Fi networks in terms of energy efficiency, performance, and reliability, their effective adoption is constrained to aspects like data availability and quality, computational capabilities, and energy consumption. Our results show that hardware-constrained AI/ML models can potentially predict network load with less than 20% average error and 3% 85th-percentile error, which constitutes a suitable input for proactively driving Wi-Fi network optimization.