Miracle-dz/UniFAR-FTUs huggingface dataset:
Derived Grassmannians and derived Schur functors

This paper develops two theories, the geometric theory of derived Grassmannians (and flag schemes) and the algebraic theory of derived Schur (and Weyl) functors, and establishes their connection, a derived generalization of the Borel-Weil-Bott theorem. More specifically: (1) The theory of derived Grassmannians and flag schemes is the natural extension of the theory of derived projectivizations [arXiv:2202.11636] and generalizes Grothendieck's theory of Grassmannians and flag schemes of sheaves to the case of complexes. We establish their fundamental properties and study various natural morphisms among them. (2) The theory of derived Schur and Weyl functors extends the classical theory of Schur and Weyl module functors studied in $\mathrm{GL}_n(\mathbb{Z})$-representation theory to the case of complexes. We show that these functors have excellent functorial properties and satisfy derived generalizations of classical formulae such as Cauchy's decomposition formula, direct-sum decomposition formula and Littlewood-Richardson rule. We also generalize various results from the case of derived symmetric powers to derived Schur functors, such as Illusie-Lurie's d\'ecalage isomorphism. (3) These two theories are connected by a derived version of the Borel-Weil-Bott theorem, which generalizes the classical Borel-Weil-Bott theorem and calculates the derived pushforwards of tautological perfect complexes on derived flag schemes in terms of derived Schur functors when the complexes have perfect-amplitude $\le 1$ and positive ranks.
PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in English

Privacy policies provide individuals with information about their rights and how their personal information is handled. Natural language understanding (NLU) technologies can support individuals and practitioners to understand better privacy practices described in lengthy and complex documents. However, existing efforts that use NLU technologies are limited by processing the language in a way exclusive to a single task focusing on certain privacy practices. To this end, we introduce the Privacy Policy Language Understanding Evaluation (PLUE) benchmark, a multi-task benchmark for evaluating the privacy policy language understanding across various tasks. We also collect a large corpus of privacy policies to enable privacy policy domain-specific language model pre-training. We evaluate several generic pre-trained language models and continue pre-training them on the collected corpus. We demonstrate that domain-specific continual pre-training offers performance improvements across all tasks. The code and models are released at https://github.com/JFChi/PLUE.
Lower bounds for self-adjoint Sturm-Liouville operators

In this note we provide estimates for the lower bound of the self-adjoint operator associated with the three-coeﬃcient Sturm–Liouville diﬀer- ential expression 1 r (cid:18) − d d xp d d x + q (cid:19) in the weighted L 2 -Hilbert space L 2 ( R ; rdx ).
Needs, Trends, and Advances in Scintillators for Radiographic Imaging and Tomography

Radiographic imaging and tomography (RadIT), which started with Röntgen's seminal X-ray work in 1895, now includes an increasing number of IT modalities. In addition to the original absorption-based X-ray radiography, others include phase contrast X-ray imaging, coherent X-ray diffractive imaging, MeV X- and $\gamma $ -ray radiography, X-ray computed tomography, proton IT, neutron IT, positron emission tomography (PET), high-energy electron radiography, and cosmic-ray muon tomography. Scintillators are widely used in RadIT as the detector frontend that converts ionizing radiation into signals and data. We give an overview of the status and needs of scintillator applications in RadIT. More than 160 kinds of scintillators were presented during the SCINT22 conference and offered ample options for novel RadIT applications. New trends in scintillators for RadIT applications include inorganic and organic scintillator composites or heterostructures, liquid-phase synthesized perovskites and single-crystal micrometer-thick films, use of multiphysics models and data science to guide scintillator and RadIT optimization, structural innovations, such as photonic crystals, nanoscintillators enhanced by the Purcell effect, heterostructural scintillating fibers, and multilayer configurations. RadIT has also been recognized as a powerful tool for scintillator discovery and development.
An extremal problem concerning finite dimensional subspaces of C[a, b] pertinent in signal theory

Abstract Increase in dimensionality of the signal space for a fixed bandwidth leads to exponential growth in the number of different signals which must be encoded. In this paper we determine the best subspace of orthogonal functions which can be used to minimise the worst ratio of peak power to RMS power. A mathematical formulation of this problem has been made and it has been found that the Fourier basis satisfies the required constraints of optimality in terms of form factor (peak/RMS ratio).
Search and Characterization of Remnant Radio Galaxies in the XMM-LSS Deep Field

The remnant phase of a radio galaxy is characterized by the cessation of active galactic nuclei activity, resulting in the jets ceasing to feed plasma to the radio lobes. In this paper, we present a search for and the characterization of remnant candidates in a 12.5 deg2 area of the XMM-Newton Large-Scale Structure field, using deep radio observations at 325 MHz from the Giant Meterwave Radio Telescope, at 150 MHz from the LOw Frequency ARray, at 1.4 GHz from the Jansky Very Large Array (VLA), and at 3 GHz from the VLA Sky Survey. By using both morphological criteria, i.e., undetected radio core as well as spectral criteria, i.e., high spectral curvature and ultra-steep spectrum, we identify 21 remnant candidates that are found to reside mostly in non-cluster environments, and exhibit diverse properties in terms of morphology, spectral index ( α1501400 in the range of −1.71 to −0.75 with a median of −1.10), and linear radio size (ranging from 242 kpc–1.3 Mpc with a median of 469 kpc). Our study attempts to identify remnant candidates down to the flux density limit of 6.0 mJy at 325 MHz, and yields an upper limit on the remnant fraction (f rem) to be around 5%. The observed f rem seems consistent with the predictions of an evolutionary model assuming power-law distributions of the duration of the active phase and jet kinetic power with an index of −0.8 to −1.2.
Compound Poisson‐gamma vs. delta‐gamma to handle zero‐inflated continuous data under a variable sampling volume

Ecological data such as biomasses often present a high proportion of zeros with possible skewed positive values. The Delta‐Gamma (DG) approach, which models separately the presence–absence and the positive biomass, is commonly used in ecology. A less commonly known alternative is the compound Poisson‐gamma (CPG) approach, which essentially mimics the process of capturing clusters of biomass during a sampling event. Regardless of the approach, the effort involved in obtaining a sample (henceforth called the sampling volume, but could also include swept areas, sampling durations, etc.), which can potentially be quite variable between samples, needs to be taken into account when modelling the resulting sample biomass. This is achieved empirically for the DG approach (using a generalized linear model with sampling volume as a covariate), and theoretically for the CPG approach (by scaling a parameter of the model). In this study, the consequences of this disparity between approaches were explored first using theoretical arguments, then using simulations and finally by applying the approaches to catch data from a commercial groundfish trawl fishery. The simulation study results point out that the DG approach can lead to poor estimates when far from standard idealized sampling assumptions. On the contrary, the CPG approach is much more robust to variable sampling conditions, confirming theoretical predictions. These results were confirmed by the case study for which model performances were weaker for the DG. Given the results, care must be taken when choosing an approach for dealing with zero‐inflated continuous data. The DG approach, which is easily implemented using standard statistical softwares, works well when the sampling volume variability is small. However, better results were obtained with the CPG model when dealing with variable sampling volumes.
Rumour detection using graph neural network and oversampling in benchmark Twitter dataset

Recently, online social media has become a primary source for new information and misinformation or rumours. In the absence of an automatic rumour detection system the propagation of rumours has increased manifold leading to serious societal damages. In this work, we propose a novel method for building automatic rumour detection system by focusing on oversampling to alleviating the fundamental challenges of class imbalance in rumour detection task. Our oversampling method relies on contextualised data augmentation to generate synthetic samples for underrepresented classes in the dataset. The key idea exploits selection of tweets in a thread for augmentation which can be achieved by introducing a non-random selection criteria to focus the augmentation process on relevant tweets. Furthermore, we propose two graph neural networks(GNN) to model non-linear conversations on a thread. To enhance the tweet representations in our method we employed a custom feature selection technique based on state-of-the-art BERTweet model. Experiments of three publicly available datasets confirm that 1) our GNN models outperform the the current state-of-the-art classifiers by more than 20%(F1-score); 2) our oversampling technique increases the model performance by more than 9%;(F1-score) 3) focusing on relevant tweets for data augmentation via non-random selection criteria can further improve the results; and 4) our method has superior capabilities to detect rumours at very early stage.
Personalized PageRank on Evolving Graphs with an Incremental Index-Update Scheme

\em Personalized PageRank (PPR) stands as a fundamental proximity measure in graph mining. Given an input graph G with the probability of decay α, a source node s and a target node t, the PPR score π(s,t) of target t with respect to source s is the probability that an α-decay random walk starting from s stops at t. A \em single-source PPR (SSPPR) query takes an input graph G with decay probability α and a source s, and then returns the PPR π(s,v) for each node v ∈ V. Since computing an exact SSPPR query answer is prohibitive, most existing solutions turn to approximate queries with guarantees. The state-of-the-art solutions for approximate SSPPR queries are index-based and mainly focus on static graphs, while real-world graphs are usually dynamically changing. However, existing index-update schemes can not achieve a sub-linear update time. Motivated by this, we present an efficient indexing scheme for single-source PPR queries on evolving graphs. Our proposed solution is based on a classic framework that combines the forward-push technique with a random walk index for approximate PPR queries. Thus, our indexing scheme is similar to existing solutions in the sense that we store pre-sampled random walks for efficient query processing. One of our main contributions is an incremental updating scheme to maintain indexed random walks in expected O(1) time after each graph update. To achieve O(1) update cost, we need to maintain auxiliary data structures for both vertices and edges. To reduce the space consumption, we further revisit the sampling methods and propose a new sampling scheme to remove the auxiliary data structure for vertices while still supporting O(1) index update cost on evolving graphs. Extensive experiments show that our update scheme achieves orders of magnitude speed-up on update performance over existing index-based dynamic schemes without sacrificing the query efficiency.
Obtaining gluon propagator in a new generalized gauge

Gauge theories play a fundamental role in particle physics, nuclear physics and cosmology. The basic idea of these theories is that the Lagrangian density should be invariant under some transformations. Lagrangian invariance implies a certain freedom in defining gauge fields. In this study, the standard path integral quantization formalism is used. Gauge degrees of freedom manifest themselves in the difficulties in obtaining gauge field propagators. For a consistent quantization, it is necessary to eliminate non-physical gauge degrees of freedom. The standard procedure is to break the gauge symmetry by applying a gauge condition. In this work, we introduced a new generalized gauge condition
SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization

Data scarcity has been a long standing issue in the field of open-domain social dialogue. To quench this thirst, we present SODA: the first publicly available, million-scale high-quality social dialogue dataset. By contextualizing social commonsense knowledge from a knowledge graph, we are able to distill an exceptionally broad spectrum of social interactions from a large language model. Human evaluation shows that conversations in SODA are more consistent, specific, and (surprisingly) natural than those in prior human-authored datasets. Using SODA, we train COSMO: a generalizable conversation model that is significantly more natural and consistent on unseen datasets than best-performing conversation models (e.g., GODEL, BlenderBot-1, Koala, Vicuna). Experiments reveal COSMO is sometimes even preferred to the original human-written gold responses. Additionally, our results shed light on the distinction between knowledge-enriched conversations and natural social chitchats. We plan to make our data, model, and code public.
HYRR: Hybrid Infused Reranking for Passage Retrieval

We present Hybrid Infused Reranking for Passages Retrieval (HYRR), a framework for training rerankers based on a hybrid of BM25 and neural retrieval models. Retrievers based on hybrid models have been shown to outperform both BM25 and neural models alone. Our approach exploits this improved performance when training a reranker, leading to a robust reranking model. The reranker, a cross-attention neural model, is shown to be robust to different first-stage retrieval systems, achieving better performance than rerankers simply trained upon the first-stage retrievers in the multi-stage systems. We present evaluations on a supervised passage retrieval task using MS MARCO and zero-shot retrieval tasks using BEIR. The empirical results show strong performance on both evaluations.
Raman backscatter in high temperature, inhomogeneous plasmas

The behavior of Raman backscatter of intense laser light in a high temperature, inhomogeneous plasma is examined in theory and simulations. Plasma conditions thought to be characteristic of recent long pulse length experiments are considered. Significant levels of reflected light are found near ω0/2, a frequency which is not commonly monitored in experiments. It is shown that the plasma temperature leads to a sizable shift of the spectrum to frequencies below ω0/2. Conditions for significant Raman backscatter at densities much less than one‐fourth the critical density are also briefly considered.
Quantifying and Visualizing Vascular Branching Geometry with Micro-CT: Normalization of Intra- and Inter-Specimen Variations

Micro-CT images of the renal arteries of intact rat kidneys, which had their vasculature injected with the contrast agent polymer Microfil, were characterized. Measurement of inter-branch segment properties and the hierarchical structure of the vessel trees were computed by an automated algorithmic approach. The perfusion territories of the different kidneys, as well as the local diameters of the segmented vasculature were mapped onto the representative structures and visually explored. Various parameters were compared in order to outline key geometrical properties, properties which were shown to not have a wide range of inter-specimen variation. It is shown that the fractal scaling in non-symmetric branching reveals itself differently, than in symmetric branching (e.g., in the lung the mean bronchial diameters at each generation are closely related). Also, perfused tissue is shown to have very little inter-specimen variation and therefore could be used in future studies related to characterizing various disease states of tissues and organs based on vascular branching geometry.
Validating argument-based opinion dynamics with survey experiments

The empirical validation of models remains one of the most important challenges in opinion dynamics. In this contribution, we report on recent developments on combining data from survey experiments with computational models of opinion formation. We extend previous work on the empirical assessment of an argument-based model for opinion dynamics in which biased processing is the principle mechanism. While previous work (Banisch&Shamon, in press) has focused on calibrating the micro mechanism with experimental data on argument-induced opinion change, this paper concentrates on the macro level using the empirical data gathered in the survey experiment. For this purpose, the argument model is extended by an external source of balanced information which allows to control for the impact of peer influence processes relative to other noisy processes. We show that surveyed opinion distributions are matched with a high level of accuracy in a specific region in the parameter space, indicating an equal impact of social influence and external noise. More importantly, the estimated strength of biased processing given the macro data is compatible with those values that achieve high likelihood at the micro level. The main contribution of the paper is hence to show that the extended argument-based model provides a solid bridge from the micro processes of argument-induced attitude change to macro level opinion distributions. Beyond that, we review the development of argument-based models and present a new method for the automated classification of model outcomes.
CSMPQ: Class Separability Based Mixed-Precision Quantization

Mixed-precision quantization has received increasing attention for its capability of reducing the computational burden and speeding up the inference time. Existing methods usually focus on the sensitivity of different network layers, which requires a time-consuming search or training process. To this end, a novel mixed-precision quantization method, termed CSMPQ, is proposed. Specifically, the TF-IDF metric that is widely used in natural language processing (NLP) is introduced to measure the class separability of layer-wise feature maps. Furthermore, a linear programming problem is designed to derive the optimal bit configuration for each layer. Without any iterative process, the proposed CSMPQ achieves better compression trade-offs than the state-of-the-art quantization methods. Specifically, CSMPQ achieves 73.03$\%$ Top-1 acc on ResNet-18 with only 59G BOPs for QAT, and 71.30$\%$ top-1 acc with only 1.5Mb on MobileNetV2 for PTQ.
Generalizing Multimodal Variational Methods to Sets

Making sense of multiple modalities can yield a more comprehensive description of real-world phenomena. However, learning the co-representation of diverse modalities is still a long-standing endeavor in emerging machine learning applications and research. Previous generative approaches for multimodal input approximate a joint-modality posterior by uni-modality posteriors as product-of-experts (PoE) or mixture-of-experts (MoE). We argue that these approximations lead to a defective bound for the optimization process and loss of semantic connection among modalities. This paper presents a novel variational method on sets called the Set Multimodal VAE (SMVAE) for learning a multimodal latent space while handling the missing modality problem. By modeling the joint-modality posterior distribution directly, the proposed SMVAE learns to exchange information between multiple modalities and compensate for the drawbacks caused by factorization. In public datasets of various domains, the experimental results demonstrate that the proposed method is applicable to order-agnostic cross-modal generation while achieving outstanding performance compared to the state-of-the-art multimodal methods. The source code for our method is available online https://anonymous.4open. science/r/SMVAE-9B3C/.
A World Full of Privacy and Security (Mis)conceptions? Findings of a Representative Survey in 12 Countries

Misconceptions about digital security and privacy topics in the general public frequently lead to insecure behavior. However, little is known about the prevalence and extent of such misconceptions in a global context. In this work, we present the results of the first large-scale survey of a global population on misconceptions: We conducted an online survey with n = 12, 351 participants in 12 countries on four continents. By investigating influencing factors of misconceptions around eight common security and privacy topics (including E2EE, Wi-Fi, VPN, and malware), we find the country of residence to be the strongest estimate for holding misconceptions. We also identify differences between non-Western and Western countries, demonstrating the need for region-specific research on user security knowledge, perceptions, and behavior. While we did not observe many outright misconceptions, we did identify a lack of understanding and uncertainty about several fundamental privacy and security topics.
Dissipativity of nonlinear ODE model of distribution voltage profile

In this paper, we consider a power distribution system consisting of a straight feeder line. A nonlinear ordinary differential equation (ODE) model is used to describe the voltage distribution profile over the feeder line. At first, we show the dissipativity of the subsystems corresponding to active and reactive powers. We also show that the dissipation rates of these subsystem coincide with the distribution loss given by a square of current amplitudes. Moreover, the entire distribution system is decomposed into two subsystems corresponding to voltage amplitude and phase. As a main result, we prove the dissipativity of these subsystems based on the decomposition. As a physical interpretation of these results, we clarify that the phenomena related to the gradients of the voltage amplitude and phase are induced in a typical power distribution system from the dissipation equalities. Finally, we discuss a reduction of distribution losses by injecting a linear combination of the active and reactive powers as a control input based on the dissipation rate of the subsystem corresponding to voltage amplitude.
An infrared polarization switch consisting of silicon and liquid crystal

An infrared polarization switch was fabricated by sandwiching liquid crystal between two silicon prisms. The liquid crystal was so aligned as to exhibit the ordinary index no for p polarization and the extraordinary index ne for s polarization. Consequently, a p polarized beam was reflected at the boundary, while an s polarized beam passed through the liquid crystal layer. When an electric voltage (15 V) was applied between the prisms, the liquid crystal was reoriented in the direction perpendicular to the prism surface. Accordingly, an s polarized beam was reflected and a p polarized beam passed through the liquid crystal layer.