Miracle-dz/UniFAR-FTUs huggingface dataset:
PopBERT. Detecting populism and its host ideologies in the German Bundestag

The rise of populism concerns many political scientists and practitioners, yet the detection of its underlying language remains fragmentary. This paper aims to provide a reliable, valid, and scalable approach to measure populist stances. For that purpose, we created an annotated dataset based on parliamentary speeches of the German Bundestag (2013 to 2021). Following the ideational definition of populism, we label moralizing references to the virtuous people or the corrupt elite as core dimensions of populist language. To identify, in addition, how the thin ideology of populism is thickened, we annotate how populist statements are attached to left-wing or right-wing host ideologies. We then train a transformer-based model (PopBERT) as a multilabel classifier to detect and quantify each dimension. A battery of validation checks reveals that the model has a strong predictive accuracy, provides high qualitative face validity, matches party rankings of expert surveys, and detects out-of-sample text snippets correctly. PopBERT enables dynamic analyses of how German-speaking politicians and parties use populist language as a strategic device. Furthermore, the annotator-level data may also be applied in cross-domain applications or to develop related classifiers.
Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation

Unsupervised video object segmentation (VOS) is a task that aims to detect the most salient object in a video without external guidance about the object. To leverage the property that salient objects usually have distinctive movements compared to the background, recent methods collaboratively use motion cues extracted from optical flow maps with appearance cues extracted from RGB images. However, as optical flow maps are usually very relevant to segmentation masks, the network is easy to be learned overly dependent on the motion cues during network training. As a result, such two-stream approaches are vulnerable to confusing motion cues, making their prediction unstable. To relieve this issue, we design a novel motion-as-option network by treating motion cues as optional. During network training, RGB images are randomly provided to the motion encoder instead of optical flow maps, to implicitly reduce motion dependency of the network. As the learned motion encoder can deal with both RGB images and optical flow maps, two different predictions can be generated depending on which source information is used as motion input. In order to fully exploit this property, we also propose an adaptive output selection algorithm to adopt optimal prediction result at test time. Our proposed approach affords state-of-the-art performance on all public benchmark datasets, even maintaining real-time inference speed.
Graph Neural Network Based Method for Path Planning Problem

Sampling-based path planning is a widely used method in robotics, particularly in high-dimensional state space. Among the whole process of the path planning, collision detection is the most time-consuming operation. In this paper, we propose a learning-based path planning method that aims to reduce the number of collision detection. We develop an efficient neural network model based on Graph Neural Networks (GNN) and use the environment map as input. The model outputs weights for each neighbor based on the input and current vertex information, which are used to guide the planner in avoiding obstacles. We evaluate the proposed method's efficiency through simulated random worlds and real-world experiments, respectively. The results demonstrate that the proposed method significantly reduces the number of collision detection and improves the path planning speed in high-dimensional environments.
TOI-199 b: A Well-characterized 100 day Transiting Warm Giant Planet with TTVs Seen from Antarctica

We present the spectroscopic confirmation and precise mass measurement of the warm giant planet TOI-199 b. This planet was first identified in TESS photometry and confirmed using ground-based photometry from ASTEP in Antarctica including a full 6.5 hr long transit, PEST, Hazelwood, and LCO; space photometry from NEOSSat; and radial velocities (RVs) from FEROS, HARPS, CORALIE, and CHIRON. Orbiting a late G-type star, TOI-199 b has a 104.854−0.002+0.001day period, a mass of 0.17 ± 0.02 M J, and a radius of 0.810 ± 0.005 R J. It is the first warm exo-Saturn with a precisely determined mass and radius. The TESS and ASTEP transits show strong transit timing variations (TTVs), pointing to the existence of a second planet in the system. The joint analysis of the RVs and TTVs provides a unique solution for the nontransiting companion TOI-199 c, which has a period of 273.69−0.22+0.26days and an estimated mass of 0.28−0.01+0.02MJ . This period places it within the conservative habitable zone.
Automating question generation from educational text

The use of question-based activities (QBAs) is wide-spread in education, traditionally forming an integral part of the learning and assessment process. In this paper, we design and evaluate an automated question generation tool for formative and summative assessment in schools. We present an expert survey of one hundred and four teachers, demonstrating the need for automated generation of QBAs, as a tool that can significantly reduce the workload of teachers and facilitate personalized learning experiences. Leveraging the recent advancements in generative AI, we then present a modular framework employing transformer based language models for automatic generation of multiple-choice questions (MCQs) from textual content. The presented solution, with distinct modules for question generation, correct answer prediction, and distractor formulation, enables us to evaluate different language models and generation techniques. Finally, we perform an extensive quantitative and qualitative evaluation, demonstrating trade-offs in the use of different techniques and models.
Sensitivity to Surface Forcing and Boundary Layer Mixing in a Global Ocean Model: Annual-Mean Climatology

Abstract The effects of more realistic bulk forcing boundary conditions, a more physical subgrid-scale vertical mixing parameterization, and more accurate bottom topography are investigated in a coarse-resolution, global oceanic general circulation model. In contrast to forcing with prescribed fluxes, the bulk forcing utilizes the evolving model sea surface temperatures and monthly atmospheric fields based on reanalyses by the National Centers for Environmental Prediction and on satellite data products. The vertical mixing in the oceanic boundary layer is governed by a nonlocal K-profile parameterization (KPP) and is matched to parameterizations of mixing in the interior. The KPP scheme is designed to represent well both convective and wind-driven entrainment. The near- equilibrium solutions are compared to a baseline experiment in which the surface tracers are strongly restored everywhere to climatology and the vertical mixing is conventional with constant coefficients, except where there is either conve.
Supersonic: Learning to Generate Source Code Optimizations in C/C++

Software optimization refines programs for resource efficiency while preserving functionality. Traditionally, it is a process done by developers and compilers. This paper introduces a third option, automated optimization at the source code level. We present Supersonic, a neural approach targeting minor source code modifications for optimization. Using a seq2seq model, Supersonic is trained on C/C++ program pairs ($x_{t}$, $x_{t+1}$), where $x_{t+1}$ is an optimized version of $x_{t}$, and outputs a diff. Supersonic's performance is benchmarked against OpenAI's GPT-3.5-Turbo and GPT-4 on competitive programming tasks. The experiments show that Supersonic not only outperforms both models on the code optimization task but also minimizes the extent of the change with a model more than 600x smaller than GPT-3.5-Turbo and 3700x smaller than GPT-4.
Higher-form symmetry and chiral transport in real-time lattice $U(1)$ gauge theory

We study classical lattice simulations of theories of electrodynamics coupled to charged matter at finite temperature, interpreting them using the higher-form symmetry formulation of magnetohydrodynamics (MHD). We compute transport coefficients using classical Kubo formulas on the lattice and show that the properties of the simulated plasma are in complete agreement with the predictions from effective field theories. In particular, the higher-form formulation allows us to understand from hydrodynamic considerations the relaxation rate of axial charge in the chiral plasma observed in previous simulations. A key point is that the resistivity of the plasma -- defined in terms of Kubo formulas for the electric field in the 1-form formulation of MHD -- remains a well-defined and predictive quantity at strong electromagnetic coupling. However, the Kubo formulas used to define the conventional conductivity vanish at low frequencies due to electrodynamic fluctuations, and thus the concept of the conductivity of a gauged electric current must be interpreted with care.
Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems

Recent academic research has extensively examined algorithmic collusion resulting from the utilization of artificial intelligence (AI)-based dynamic pricing algorithms. Nevertheless, e-commerce platforms employ recommendation algorithms to allocate exposure to various products, and this important aspect has been largely overlooked in previous studies on algorithmic collusion. Our study bridges this important gap in the literature and examines how recommendation algorithms can determine the competitive or collusive dynamics of AI-based pricing algorithms. Specifically, two commonly deployed recommendation algorithms are examined: (i) a recommender system that aims to maximize the sellers' total profit (profit-based recommender system) and (ii) a recommender system that aims to maximize the demand for products sold on the platform (demand-based recommender system). We construct a repeated game framework that incorporates both pricing algorithms adopted by sellers and the platform's recommender system. Subsequently, we conduct experiments to observe price dynamics and ascertain the final equilibrium. Experimental results reveal that a profit-based recommender system intensifies algorithmic collusion among sellers due to its congruence with sellers' profit-maximizing objectives. Conversely, a demand-based recommender system fosters price competition among sellers and results in a lower price, owing to its misalignment with sellers' goals. Extended analyses suggest the robustness of our findings in various market scenarios. Overall, we highlight the importance of platforms' recommender systems in delineating the competitive structure of the digital marketplace, providing important insights for market participants and corresponding policymakers.
Monitoring ice sheet behavior from space

Satellite remote sensing has revolutionized ice sheet research. A variety of instruments sensitive to different parts of the electromagnetic spectrum take what the human eye detects as a flat, white desert and provide data sets rich in scientific information. Image‐based maps of ice sheets are becoming commonplace and have become an integral component of field work. More than a pretty picture, the digital character of the satellite data from these instruments has become fundamental to the production of elevation, motion, accumulation, and reflectance data sets. Visible imagery shows the scientist a wealth of features that offer clues to the history and current behavior of the ice sheet. Radar and microwave imagery provide information from beneath the surface and have been used to estimate snow accumulation rates. Interferometry principles have recently been applied to measure surface topography and ice motion with unparalleled precision. Nonimaging instruments also keep a watchful eye, monitoring the ice sheet for indications of growth or shrinkage. Further expansion of the uses of satellite data is anticipated in the future.
Simultaneously Learning Speaker's Direction and Head Orientation from Binaural Recordings

Estimation of a speaker's direction and head orientation with binaural recordings can be a critical piece of information in many real-world applications with emerging `earable' devices, including smart headphones and AR/VR headsets. However, it requires predicting the mutual head orientations of both the speaker and the listener, which is challenging in practice. This paper presents a system for jointly predicting speaker-listener head orientations by leveraging inherent human voice directivity and listener's head-related transfer function (HRTF) as perceived by the ear-mounted microphones on the listener. We propose a convolution neural network model that, given binaural speech recording, can predict the orientation of both speaker and listener with respect to the line joining the two. The system builds on the core observation that the recordings from the left and right ears are differentially affected by the voice directivity as well as the HRTF. We also incorporate the fact that voice is more directional at higher frequencies compared to lower frequencies.
Interactively Learning Social Media Representations Improves News Source Factuality Detection

The rise of social media has enabled the widespread propagation of fake news, text that is published with an intent to spread misinformation and sway beliefs. Rapidly detecting fake news, especially as new events arise, is important to prevent misinformation. While prior works have tackled this problem using supervised learning systems, automatedly modeling the complexities of the social media landscape that enables the spread of fake news is challenging. On the contrary, having humans fact check all news is not scalable. Thus, in this paper, we propose to approach this problem interactively, where humans can interact to help an automated system learn a better social media representation quality. On real world events, our experiments show performance improvements in detecting factuality of news sources, even after few human interactions.
Progressive Text-to-3D Generation for Automatic 3D Prototyping

Text-to-3D generation is to craft a 3D object according to a natural language description. This can significantly reduce the workload for manually designing 3D models and provide a more natural way of interaction for users. However, this problem remains challenging in recovering the fine-grained details effectively and optimizing a large-size 3D output efficiently. Inspired by the success of progressive learning, we propose a Multi-Scale Triplane Network (MTN) and a new progressive learning strategy. As the name implies, the Multi-Scale Triplane Network consists of four triplanes transitioning from low to high resolution. The low-resolution triplane could serve as an initial shape for the high-resolution ones, easing the optimization difficulty. To further enable the fine-grained details, we also introduce the progressive learning strategy, which explicitly demands the network to shift its focus of attention from simple coarse-grained patterns to difficult fine-grained patterns. Our experiment verifies that the proposed method performs favorably against existing methods. For even the most challenging descriptions, where most existing methods struggle to produce a viable shape, our proposed method consistently delivers. We aspire for our work to pave the way for automatic 3D prototyping via natural language descriptions.
DifAttack: Query-Efficient Black-Box Attack via Disentangled Feature Space

This work investigates efficient score-based black-box adversarial attacks with high Attack Success Rate (ASR) and good generalizability. We design a novel attack method based on a Disentangled Feature space, called DifAttack, which differs significantly from the existing ones operating over the entire feature space. Specifically, DifAttack firstly disentangles an image's latent feature into an adversarial feature and a visual feature, where the former dominates the adversarial capability of an image, while the latter largely determines its visual appearance. We train an autoencoder for the disentanglement by using pairs of clean images and their Adversarial Examples (AEs) generated from available surrogate models via white-box attack methods. Eventually, DifAttack iteratively optimizes the adversarial feature according to the query feedback from the victim model until a successful AE is generated, while keeping the visual feature unaltered. In addition, due to the avoidance of using surrogate models' gradient information when optimizing AEs for black-box models, our proposed DifAttack inherently possesses better attack capability in the open-set scenario, where the training dataset of the victim model is unknown. Extensive experimental results demonstrate that our method achieves significant improvements in ASR and query efficiency simultaneously, especially in the targeted attack and open-set scenarios. The code is available The code is available at https://github.com/csjunjun/DifAttack.git.
Conditions for matchability in groups and field extensions II

We present sufficient conditions for the existence of matchings in abelian groups and their linear counterparts. These conditions lead to extensions of existing results in matching theory. Additionally, we classify subsets within abelian groups that cannot be matched. We introduce the concept of Chowla subspaces and formulate and conjecture a linear analogue of a result originally attributed to Y. O. Hamidoune [20] concerning Chowla sets. If proven true, this result would extend matchings in primitive subspaces. Throughout the paper, we emphasize the analogy between matchings in abelian groups and field extensions. We also pose numerous open questions for future research. Our approach relies on classical theorems in group theory, additive number theory and linear algebra. As the title of the paper suggests, this work is the second sequel to a previous paper [5] with a similar theme. This paper is self-contained and can be read independently.
Addressing Data Misalignment in Image-LiDAR Fusion on Point Cloud Segmentation

With the advent of advanced multi-sensor fusion models, there has been a notable enhancement in the performance of perception tasks within in terms of autonomous driving. Despite these advancements, the challenges persist, particularly in the fusion of data from cameras and LiDAR sensors. A critial concern is the accurate alignment of data from these disparate sensors. Our observations indicate that the projected positions of LiDAR points often misalign on the corresponding image. Furthermore, fusion models appear to struggle in accurately segmenting these misaligned points. In this paper, we would like to address this problem carefully, with a specific focus on the nuScenes dataset and the SOTA of fusion models 2DPASS, and providing the possible solutions or potential improvements.
K\"ahler-Ricci solitons on Fano threefolds with non-trivial moduli

We find Fano threefolds $X$ admitting K\"ahler-Ricci solitons (KRS) with non-trivial moduli, which are $\mathbb{T}$-varieties of complexity two. More precisely, we show that the weighted K-stability of $(X,\xi_0)$ (where $\xi_0$ is the soliton candidate) is equivalent to certain GIT-stability. In particular, this provides the first examples of strictly weighted K-semistable Fano varieties. On the other hand, we generalize Koiso's theorem to the log Fano setting. Indeed, we show that the K-stability of a log Fano pair $(V,\Delta_V)$ is equivalent to the weighted K-stability of a cone $(Y, \Delta_Y, \xi_0)$ over it. This also leads to new examples of KRS Fano varieties with non-trivial moduli and small automorphism groups. To achieve these, we establish the weighted Abban-Zhuang estimate generalizing the work of \cite{AZ22}, which gives a lower bound of the weighted stability threshold $\delta^g_{\mathbb{T}}(X,\Delta)$. This is an effective way to check the weighted K-semistablity of a log Fano triple $(X,\Delta,\xi_0)$. Surprisingly, such an estimate is also useful in testing (weighted) K-polystability based on the work of \cite{BLXZ23}.
A Public Key Infrastructure for 5G Service-Based Architecture

The 3GPP 5G Service-based Architecture (SBA) security specifications leave several details on how to setup an appropriate Public Key Infrastructure (PKI) for 5G SBA, unspecified. In this work, we propose 5G-SBA-PKI, a public key infrastructure for secure inter-NF communication in 5G SBA core networks, where NF refers to Network Functions. 5G-SBA-PKI is designed to include multiple certificate authorities (with different scopes of operation and capabilities) at different PLMN levels for certification operations and key exchange between communicating NFs, where PLMN refers to a Public Land Mobile Network. We conduct a formal analysis of 5G-SBA-PKI with respect to the desired security properties using TAMARIN prover. Finally, we evaluate 5G-SBA-PKI's performance with"pre-quantum"as well as quantum-safe cryptographic algorithms.
Corporate Credit Rating: A Survey

Corporate credit rating (CCR) plays a very important role in the process of contemporary economic and social development. How to use credit rating methods for enterprises has always been a problem worthy of discussion. Through reading and studying the relevant literature at home and abroad, this paper makes a systematic survey of CCR. This paper combs the context of the development of CCR methods from the three levels: statistical models, machine learning models and neural network models, summarizes the common databases of CCR, and deeply compares the advantages and disadvantages of the models. Finally, this paper summarizes the problems existing in the current research and prospects the future of CCR. Compared with the existing review of CCR, this paper expounds and analyzes the progress of neural network model in this field in recent years.
Carbon Containers: A System-level Facility for Managing Application-level Carbon Emissions

To reduce their environmental impact, cloud datacenters' are increasingly focused on optimizing applications' carbon-efficiency, or work done per mass of carbon emitted. To facilitate such optimizations, we present Carbon Containers, a simple system-level facility, which extends prior work on power containers, that automatically regulates applications' carbon emissions in response to variations in both their work-load's intensity and their energy's carbon-intensity. Specifically, Carbon Containers enable applications to specify a maximum carbon emissions rate (in g.CO2e/hr), and then transparently enforce this rate via a combination of vertical scaling, container migration, and suspend/resume while maximizing either energy-efficiency or performance. Carbon Containers are especially useful for applications that i) must continue running even during high-carbon periods, and ii) execute in regions with few variations in carbon-intensity. These low-variability regions also tend to have high average carbon-intensity, which increases the importance of regulating carbon emissions. We implement a Carbon Container prototype by extending Linux Containers to incorporate the mechanisms above and evaluate it using real workload traces and carbon-intensity data from multiple regions. We compare Carbon Containers with prior work that regulates carbon emissions by suspending/resuming applications during high/low carbon periods. We show that Carbon Containers are more carbon-efficient and improve performance while maintaining similar carbon emissions.