Miracle-dz/UniFAR-FTUs huggingface dataset:
On closed queueing networks with mixed preemptive resume priority servers

Herlinde Leemans Katholieke Universiteit Leuven Faculty of Economics and Applied Economics Naamsestraat 69 B-3000 Leuven Belgium Fax: +32 -1632.67. 32 E-mail: Herlinde.Leemans@econ.kuleuven. ac.be Guido Dedene Katholieke Universiteit Leuven Faculty of Economics and Applied Economics Naamsestraat 69 B-3000 Leuven Belgium Fax: +32 -1632.67. 32 E-mail: Guido.Dedene@econ.kuleuven. ac.be This paper discusses a typical closed queueing network model in which multiple preemptive resume servers are present with different priority structures at each priority node. An algorithm is developed that is applicable for the three-node two-class model and results are compared to point estimates obtained from simulation. The algorithm is partly based on the Delay/MV A algorithm developed by Bondi and Chuang, because of the accuracy with which instant arrival queue lengths at fcfs servers are calculated. Results are also compared with results obtained from the Shadow Approximation. On closed queueing networks with mixed preemptive resume priority servers
Application of recently developed computer algorithm for automatic classification of unstructured radiology reports: validation study.

PURPOSE To validate the accuracy of Lexicon Mediated Entropy Reduction (LEXIMER), a new information theory-based computer algorithm developed by the authors for independent analysis and classification of unstructured radiology reports based on the presence of clinically important findings (F(T), where (T) represents "true") and recommendations for subsequent action (R(T)). MATERIALS AND METHODS The study was approved by the Human Research Committee of the institutional review board. Consecutive de-identified radiology reports (n = 1059) comprising results of barium studies (n = 99), computed tomography (n = 107), mammography (n = 90), magnetic resonance imaging (n = 108), nuclear medicine (n = 99), positron emission tomography (n = 106), radiography (n = 212), ultrasonography (n = 131), and vascular procedures (n = 107) were independently analyzed by two radiologists and then with LEXIMER to categorize the reports into F(T) and F(T)0 (containing or not containing clinically important findings) categories and R(T) and R(T)0 (containing or not containing recommendations for subsequent action) categories. Accuracy, sensitivity, specificity, and positive and negative predictive values of LEXIMER for placing reports into F(T) and F(T)0 and R(T) and R(T)0 categories were assessed by using appropriate statistical tests. RESULTS There was strong interobserver concordance between the two radiologists for placing radiology reports into F(T) and R(T) categories (kappa = 0.9, P < .01). For the LEXIMER program, accuracy, sensitivity, specificity, and positive and negative predictive values, respectively, were 97.5% (95% confidence interval [CI]: 96.6%, 98.5%), 98.9% (95% CI: 97.9%, 99.6%), 94.9% (95% CI: 93.1%, 96.0%), 97.5% (95% CI: 96.6%, 98.0%), and 97.7% (95% CI: 95.8%, 98.8%) for placing radiology reports into F(T) and F(T)0 categories and 99.6% (95% CI: 99.2%, 99.9%), 98.2% (95% CI: 95.0%, 99.6%), 99.9% (95% CI: 99.4%, 99.99%), 99.4% (95% CI: 96.3%, 99.9%), and 99.7% (95% CI: 98.9%, 99.9%) for placing reports into R(T) and R(T)0 categories. CONCLUSION LEXIMER is an accurate automated engine for evaluating the percentage positivity of clinically important findings and rates of recommendation for subsequent action in unstructured radiology reports.
1 INTRODUCTION TO BIOMETRICS

Biometrics deals with identification of individuals based on their biological or behavioral characteristics. Biometrics has lately been receiving attention in popular media. it is widely believed that biometrics will become a significant component of the identification technology as (i) the prices of biometrics sensors continue to fall, (ii) the underlying technology becomes more mature, and (iii) the public becomes aware of the strengths and limitations of biometrics. This chapter provides an overview of the biometrics technology and its applications and introduces the research issues underlying the biometrics.
Crafting Knowledge: Exploring the Creative Mechanisms of Chat-Based Search Engines

In the domain of digital information dissemination, search engines act as pivotal conduits linking information seekers with providers. The advent of chat-based search engines utilizing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG), exemplified by Bing Chat, marks an evolutionary leap in the search ecosystem. They demonstrate metacognitive abilities in interpreting web information and crafting responses with human-like understanding and creativity. Nonetheless, the intricate nature of LLMs renders their"cognitive"processes opaque, challenging even their designers' understanding. This research aims to dissect the mechanisms through which an LLM-powered chat-based search engine, specifically Bing Chat, selects information sources for its responses. To this end, an extensive dataset has been compiled through engagements with New Bing, documenting the websites it cites alongside those listed by the conventional search engine. Employing natural language processing (NLP) techniques, the research reveals that Bing Chat exhibits a preference for content that is not only readable and formally structured, but also demonstrates lower perplexity levels, indicating a unique inclination towards text that is predictable by the underlying LLM. Further enriching our analysis, we procure an additional dataset through interactions with the GPT-4 based knowledge retrieval API, unveiling a congruent text preference between the RAG API and Bing Chat. This consensus suggests that these text preferences intrinsically emerge from the underlying language models, rather than being explicitly crafted by Bing Chat's developers. Moreover, our investigation documents a greater similarity among websites cited by RAG technologies compared to those ranked highest by conventional search engines.
One model to use them all: Training a segmentation model with complementary datasets

Understanding a surgical scene is crucial for computer-assisted surgery systems to provide any intelligent assistance functionality. One way of achieving this scene understanding is via scene segmentation, where every pixel of a frame is classified and therefore identifies the visible structures and tissues. Progress on fully segmenting surgical scenes has been made using machine learning. However, such models require large amounts of annotated training data, containing examples of all relevant object classes. Such fully annotated datasets are hard to create, as every pixel in a frame needs to be annotated by medical experts and, therefore, are rarely available. In this work, we propose a method to combine multiple partially annotated datasets, which provide complementary annotations, into one model, enabling better scene segmentation and the use of multiple readily available datasets. Our method aims to combine available data with complementary labels by leveraging mutual exclusive properties to maximize information. Specifically, we propose to use positive annotations of other classes as negative samples and to exclude background pixels of binary annotations, as we cannot tell if they contain a class not annotated but predicted by the model. We evaluate our method by training a DeepLabV3 on the publicly available Dresden Surgical Anatomy Dataset, which provides multiple subsets of binary segmented anatomical structures. Our approach successfully combines 6 classes into one model, increasing the overall Dice Score by 4.4% compared to an ensemble of models trained on the classes individually. By including information on multiple classes, we were able to reduce confusion between stomach and colon by 24%. Our results demonstrate the feasibility of training a model on multiple datasets. This paves the way for future work further alleviating the need for one large, fully segmented datasets.
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers

The quality of the data and annotation upper-bounds the quality of a downstream model. While there exist large text corpora and image-text pairs, high-quality video-text data is much harder to collect. First of all, manual labeling is more time-consuming, as it requires an annotator to watch an entire video. Second, videos have a temporal dimension, consisting of several scenes stacked together, and showing multiple actions. Accordingly, to establish a video dataset with high-quality captions, we propose an automatic approach leveraging multimodal inputs, such as textual video description, subtitles, and individual video frames. Specifically, we curate 3.8M high-resolution videos from the publicly available HD-VILA-100M dataset. We then split them into semantically consistent video clips, and apply multiple cross-modality teacher models to obtain captions for each video. Next, we finetune a retrieval model on a small subset where the best caption of each video is manually selected and then employ the model in the whole dataset to select the best caption as the annotation. In this way, we get 70M videos paired with high-quality text captions. We dub the dataset as Panda-70M. We show the value of the proposed dataset on three downstream tasks: video captioning, video and text retrieval, and text-driven video generation. The models trained on the proposed data score substantially better on the majority of metrics across all the tasks.
Probing the Information Encoded in Neural-based Acoustic Models of Automatic Speech Recognition Systems

Deep learning architectures have made significant progress in terms of performance in many research areas. The automatic speech recognition (ASR) field has thus benefited from these scientific and technological advances, particularly for acoustic modeling, now integrating deep neural network architectures. However, these performance gains have translated into increased complexity regarding the information learned and conveyed through these black-box architectures. Following many researches in neural networks interpretability, we propose in this article a protocol that aims to determine which and where information is located in an ASR acoustic model (AM). To do so, we propose to evaluate AM performance on a determined set of tasks using intermediate representations (here, at different layer levels). Regarding the performance variation and targeted tasks, we can emit hypothesis about which information is enhanced or perturbed at different architecture steps. Experiments are performed on both speaker verification, acoustic environment classification, gender classification, tempo-distortion detection systems and speech sentiment/emotion identification. Analysis showed that neural-based AMs hold heterogeneous information that seems surprisingly uncorrelated with phoneme recognition, such as emotion, sentiment or speaker identity. The low-level hidden layers globally appears useful for the structuring of information while the upper ones would tend to delete useless information for phoneme recognition.
Ab initio elasticity at finite temperature and stress in ferroelectrics

Computing the temperature and stress dependence of the full elastic constant tensor from first-principles in non-cubic materials remains a challenging problem. Here we circumvent the aforementioned challenge via the generalized quasiharmonic approximation in conjunction with the irreducible derivative approach for computing strain dependent phonons using finite difference, explicitly including dipole-quadrupole contributions. We showcase this approach in ferroelectric PbTiO$_3$ using density functional theory, computing all independent elastic constants and piezoelectric strain coefficients at finite temperature and stress. There is good agreement between the quasiharmonic approximation and the experimental lattice parameters close to 0 K. However, the quasiharmonic approximation overestimates the temperature dependence of the lattice parameters and elastic constant tensor, demonstrating that a higher level of strain dependent anharmonic vibrational theory is needed.
Testing gravitational waveforms in full General Relativity

We perform a comprehensive analysis of state-of-the-art waveform models, focusing on their predictions concerning kick velocity and inferred gravitational wave memory. In our investigation we assess the accuracy of waveform models using energy-momentum balance laws, which were derived in the framework of full, non-linear General Relativity. The numerical accuracy assessment is performed for precessing as well as non-precessing scenarios for models belonging to the \textit{EOB}, \textit{Phenom}, and \textit{Surrogate} families. We analyze the deviations of these models from each other and from Numerical Relativity waveforms. Our analysis reveals statistically significant deviations, which we trace back to inaccuracies in modelling subdominant modes and inherent systematic errors in the chosen models. We corroborate our findings through analytical considerations regarding the mixing of harmonic modes in the computed kick velocities and inferred memories.
Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models

Adam has been shown to outperform gradient descent in optimizing large language transformers empirically, and by a larger margin than on other tasks, but it is unclear why this happens. We show that the heavy-tailed class imbalance found in language modeling tasks leads to difficulties in the optimization dynamics. When training with gradient descent, the loss associated with infrequent words decreases slower than the loss associated with frequent ones. As most samples come from relatively infrequent words, the average loss decreases slowly with gradient descent. On the other hand, Adam and sign-based methods do not suffer from this problem and improve predictions on all classes. To establish that this behavior is indeed caused by class imbalance, we show empirically that it persist through different architectures and data types, on language transformers, vision CNNs, and linear models. We further study this phenomenon on a linear classification with cross-entropy loss, showing that heavy-tailed class imbalance leads to ill-conditioning, and that the normalization used by Adam can counteract it.
RoadRunner - Learning Traversability Estimation for Autonomous Off-road Driving

Autonomous navigation at high speeds in off-road environments necessitates robots to comprehensively understand their surroundings using onboard sensing only. The extreme conditions posed by the off-road setting can cause degraded camera image quality due to poor lighting and motion blur, as well as limited sparse geometric information available from LiDAR sensing when driving at high speeds. In this work, we present RoadRunner, a novel framework capable of predicting terrain traversability and an elevation map directly from camera and LiDAR sensor inputs. RoadRunner enables reliable autonomous navigation, by fusing sensory information, handling of uncertainty, and generation of contextually informed predictions about the geometry and traversability of the terrain while operating at low latency. In contrast to existing methods relying on classifying handcrafted semantic classes and using heuristics to predict traversability costs, our method is trained end-to-end in a self-supervised fashion. The RoadRunner network architecture builds upon popular sensor fusion network architectures from the autonomous driving domain, which embed LiDAR and camera information into a common Bird's Eye View perspective. Training is enabled by utilizing an existing traversability estimation stack to generate training data in hindsight in a scalable manner from real-world off-road driving datasets. Furthermore, RoadRunner improves the system latency by a factor of roughly 4, from 500 ms to 140 ms, while improving the accuracy for traversability costs and elevation map predictions. We demonstrate the effectiveness of RoadRunner in enabling safe and reliable off-road navigation at high speeds in multiple real-world driving scenarios through unstructured desert environments.
Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance

While Neural Networks (NNs) have surpassed human accuracy in image classification on ImageNet, they often lack robustness against image corruption, i.e., corruption robustness. Yet such robustness is seemingly effortless for human perception. In this paper, we propose visually-continuous corruption robustness (VCR) -- an extension of corruption robustness to allow assessing it over the wide and continuous range of changes that correspond to the human perceptive quality (i.e., from the original image to the full distortion of all perceived visual information), along with two novel human-aware metrics for NN evaluation. To compare VCR of NNs with human perception, we conducted extensive experiments on 14 commonly used image corruptions with 7,718 human participants and state-of-the-art robust NN models with different training objectives (e.g., standard, adversarial, corruption robustness), different architectures (e.g., convolution NNs, vision transformers), and different amounts of training data augmentation. Our study showed that: 1) assessing robustness against continuous corruption can reveal insufficient robustness undetected by existing benchmarks; as a result, 2) the gap between NN and human robustness is larger than previously known; and finally, 3) some image corruptions have a similar impact on human perception, offering opportunities for more cost-effective robustness assessments. Our validation set with 14 image corruptions, human robustness data, and the evaluation code is provided as a toolbox and a benchmark.
Neutrino zeromodes on electroweak strings in light of topological insulators

We examine neutrino zeromode solutions on the electroweak $Z$-string and their effect on the stability of the string in the standard model and its extensions. We propose using topological invariants constructed from the momentum (and real) space topology of Green's functions, often used for investigating edge modes in condensed matter physics. We analyze the standard model and then examine type-I and type-II extensions of the neutrino sector as well as their hybrid. Based on this analysis, we also comment on proposals in the literature to stabilize the $Z$-string.
First-principles electron-phonon interactions and electronic transport in large-angle twisted bilayer graphene

Twisted bilayer graphene (tBLG) has emerged as an exciting platform for novel condensed matter physics. However, electron-phonon ($e$-ph) interactions in tBLG and their effects on electronic transport are not completely understood. Here we show first-principles calculations of $e$-ph interactions and resistivity in commensurate tBLG with large twist angles of 13.2 and 21.8 degrees. These calculations overcome key technical barriers, including large unit cells of up to 76 atoms, Brillouin-zone folding of the $e$-ph interactions, and unstable lattice vibrations due to the AA-stacked domains. We show that $e$-ph interactions due to layer-breathing (LB) phonons enhance intervalley scattering in large-angle tBLG. This interaction effectively couples the two layers, which are otherwise electronically decoupled at such large twist angles. As a result, the phonon-limited resistivity in tBLG deviates from the temperature-linear trend characteristic of monolayer graphene and tBLG near the magic angle. Taken together, our work quantifies $e$-ph interactions and scattering mechanisms in tBLG, revealing subtle interlayer coupling effects at large twist angles.
Magnon spectrum of altermagnets: Time-dependent matrix product states vs. linearized Holstein-Primakoff calculations unravelling spontaneous magnon decay

The energy-momentum dispersion of magnons, viewed as noninteracting and infinitely long-lived quasiparticles describing collective low-energy excitations of magnetic materials, is often presented as sharp bands obtained from the effective quantum spin Hamiltonian, after being simplified via linearized Holstein-Primakoff (HP) transformations. However, magnons are prone to many-body interactions with other quasiparticles which can lead to their spontaneous decay. The magnon-magnon interactions could affect newly classified altermagnets. On the other hand, sharp bands of noninteracting chiral magnons in RuO2, as the canonical example of altermagnets, have been very recently predicted. Here, we employ nonperturbative numerically (quasi)exact quantum many-body calculations, via time-dependent matrix product states (TDMPS), to obtain magnon spectral function of RuO2. These calculations produce a broadened magnon dispersion, which overlaps with linearized HP theory sharp bands only at edges/center of the Brillouin zone. Substantially deviating otherwise. Artificially making exchange interaction within two sublattices of RuO2 closer in value forces these two spectra to overlap, thereby explaining the origin of the failure of linearized HP theory. Such features translate into the difference between their respective density of states, which we also compute and which could be tested by Raman scattering experiments. Finally, we employ popular Landau-Lifshitz-Gilbert (LLG) equation-based classical atomistic spin dynamics (ASD) simulations to obtain dynamical structure factor and extract magnon spectrum from it at finite temperature. Despite including magnon-magnon interactions via nonlinearity of LLG equation, ASD simulations cannot fully match the TDMPS-computed magnon spectrum due to nonclassical effects harbored by altermagnets.
Retrieval-Augmented Generation for AI-Generated Content: A Survey

The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by advancements in model algorithms, the increasing scale of foundation models, and the availability of ample high-quality datasets. While AIGC has achieved remarkable performance, it still faces several challenges, such as the difficulty of maintaining up-to-date and long-tail knowledge, the risk of data leakage, and the high costs associated with training and inference. Retrieval-Augmented Generation(RAG) has recently emerged as a paradigm to address such challenges. In particular, RAG introduces the information retrieval process, which enhances the generation process by retrieving relevant objects from available data stores, leading to higher accuracy and better robustness. In this paper, we comprehensively review existing efforts that integrate RAG technique into AIGC scenarios. We first classify RAG foundations according to how the retriever augments the generator, distilling the fundamental abstractions of the augmentation methodologies for various retrievers and generators. This unified perspective encompasses all RAG scenarios, illuminating advancements and pivotal technologies that help with potential future progress. We also summarize additional enhancements methods for RAG, facilitating effective engineering and implementation of RAG systems. Then from another view, we survey on practical applications of RAG across different modalities and tasks, offering valuable references for researchers and practitioners. Furthermore, we introduce the benchmarks for RAG, discuss the limitations of current RAG systems, and suggest potential directions for future research.Project Repo: https://github.com/hymie122/RAG-Survey.