Miracle-dz/UniFAR-FTUs huggingface dataset:
V2X Sidelink Positioning in FR1: Scenarios, Algorithms, and Performance Evaluation

In this paper, we investigate sub-6 GHz V2X sidelink positioning scenarios in 5G vehicular networks through a comprehensive end-to-end methodology encompassing ray-tracing-based channel modeling, novel theoretical performance bounds, high-resolution channel parameter estimation, and geometric positioning using a round-trip-time (RTT) protocol. We first derive a novel, approximate Cram\'er-Rao bound (CRB) on the connected road user (CRU) position, explicitly taking into account multipath interference, path merging, and the RTT protocol. Capitalizing on tensor decomposition and ESPRIT methods, we propose high-resolution channel parameter estimation algorithms specifically tailored to dense multipath V2X sidelink environments, designed to detect multipath components (MPCs) and extract line-of-sight (LoS) parameters. Finally, using realistic ray-tracing data and antenna patterns, comprehensive simulations are conducted to evaluate channel estimation and positioning performance, indicating that sub-meter accuracy can be achieved in sub-6 GHz V2X with the proposed algorithms.
Data Augmentation: a Combined Inductive-Deductive Approach featuring Answer Set Programming

Although the availability of a large amount of data is usually given for granted, there are relevant scenarios where this is not the case; for instance, in the biomedical/healthcare domain, some applications require to build huge datasets of proper images, but the acquisition of such images is often hard for different reasons (e.g., accessibility, costs, pathology-related variability), thus causing limited and usually imbalanced datasets. Hence, the need for synthesizing photo-realistic images via advanced Data Augmentation techniques is crucial. In this paper we propose a hybrid inductive-deductive approach to the problem; in particular, starting from a limited set of real labeled images, the proposed framework makes use of logic programs for declaratively specifying the structure of new images, that is guaranteed to comply with both a set of constraints coming from the domain knowledge and some specific desiderata. The resulting labeled images undergo a dedicated process based on Deep Learning in charge of creating photo-realistic images that comply with the generated label.
MSFormer: A Skeleton-multiview Fusion Method For Tooth Instance Segmentation

Recently, deep learning-based tooth segmentation methods have been limited by the expensive and time-consuming processes of data collection and labeling. Achieving high-precision segmentation with limited datasets is critical. A viable solution to this entails fine-tuning pre-trained multiview-based models, thereby enhancing performance with limited data. However, relying solely on two-dimensional (2D) images for three-dimensional (3D) tooth segmentation can produce suboptimal outcomes because of occlusion and deformation, i.e., incomplete and distorted shape perception. To improve this fine-tuning-based solution, this paper advocates 2D-3D joint perception. The fundamental challenge in employing 2D-3D joint perception with limited data is that the 3D-related inputs and modules must follow a lightweight policy instead of using huge 3D data and parameter-rich modules that require extensive training data. Following this lightweight policy, this paper selects skeletons as the 3D inputs and introduces MSFormer, a novel method for tooth segmentation. MSFormer incorporates two lightweight modules into existing multiview-based models: a 3D-skeleton perception module to extract 3D perception from skeletons and a skeleton-image contrastive learning module to obtain the 2D-3D joint perception by fusing both multiview and skeleton perceptions. The experimental results reveal that MSFormer paired with large pre-trained multiview models achieves state-of-the-art performance, requiring only 100 training meshes. Furthermore, the segmentation accuracy is improved by 2.4%-5.5% with the increasing volume of training data.
Dimension Reduction for Positively Curved Steady Solitons

We consider noncollapsed steady gradient Ricci solitons with nonnegative sectional curvature. We show that such solitons always dimension reduce at infinity. This generalizes an earlier result in [CDM22] to higher dimensions. In dimension four, we classify possible reductions at infinity, which lays foundation for possible classifications of steady solitons. Moreover, we show that any tangent flow at infinity of a general noncollapsed steady soliton must split off a line. This generalizes an earlier result in [BCDMZ21] to higher dimensions. While this article is under preparation, we realized that part of our main results are proved independently in a recent post [ZZ23] under different assumptions.
Distractor-Aware Event-Based Tracking

Event cameras, or dynamic vision sensors, have recently achieved success from fundamental vision tasks to high-level vision researches. Due to its ability to asynchronously capture light intensity changes, event camera has an inherent advantage to capture moving objects in challenging scenarios including objects under low light, high dynamic range, or fast moving objects. Thus event camera are natural for visual object tracking. However, the current event-based trackers derived from RGB trackers simply modify the input images to event frames and still follow conventional tracking pipeline that mainly focus on object texture for target distinction. As a result, the trackers may not be robust dealing with challenging scenarios such as moving cameras and cluttered foreground. In this paper, we propose a distractor-aware event-based tracker that introduces transformer modules into Siamese network architecture (named DANet). Specifically, our model is mainly composed of a motion-aware network and a target-aware network, which simultaneously exploits both motion cues and object contours from event data, so as to discover motion objects and identify the target object by removing dynamic distractors. Our DANet can be trained in an end-to-end manner without any post-processing and can run at over 80 FPS on a single V100. We conduct comprehensive experiments on two large event tracking datasets to validate the proposed model. We demonstrate that our tracker has superior performance against the state-of-the-art trackers in terms of both accuracy and efficiency.
Research on Secular Variation of Solar Radiation over Northwest China from 1961 to 2007

The surface radiation data from China Meteorological Administration (CMA) including the global radiation from 1961 to 2007,the direct and diffuse radiation from 1961 to 1992 at 16 first-degree stations were used to analyze the long term variation of the surface solar radiation over Northwest China. The results showed that the long term variation of global radiation was characterized as four phases:"retaining","dimming","brightening" ,and "declining". The "dimming" phase proceeded from the 1970s to the mid 1980s,then "brightening" took place and lasted to the mid 1990s. Afterward,the global radiation began fluctuating and "declining". Furthermore,both "dimming" and "brightening" were obvious during all seasons. While the temporal spans of the above two phases were the longest and shortest respectively in winter than in other seasons. From 1961 to 1992,the direct radiation varied from "dimming" to "brightening" in general,while the diffuse radiation varied from "brightening" to "dimming". The variations of the global radiation in different phases were closely related to the corresponding trend and magnitude of the direct and diffuse radiation. The decreasing percentages of direct radiation and sunshine duration from 1961 to 1984 were distinct in different cities and different seasons. Direct radiation and sunshine duration decreased most significantly in the provincial capitals during winter months,which indicated that the secular variation of solar radiation over Northwest China is perhaps related to anthropogenic emission of aerosol.
API-Assisted Code Generation for Question Answering on Varied Table Structures

A persistent challenge to table question answering (TableQA) by generating executable programs has been adapting to varied table structures, typically requiring domain-specific logical forms. In response, this paper introduces a unified TableQA framework that: (1) provides a unified representation for structured tables as multi-index Pandas data frames, (2) uses Python as a powerful querying language, and (3) uses few-shot prompting to translate NL questions into Python programs, which are executable on Pandas data frames. Furthermore, to answer complex relational questions with extended program functionality and external knowledge, our framework allows customized APIs that Python programs can call. We experiment with four TableQA datasets that involve tables of different structures -- relational, multi-table, and hierarchical matrix shapes -- and achieve prominent improvements over past state-of-the-art systems. In ablation studies, we (1) show benefits from our multi-index representation and APIs over baselines that use only an LLM, and (2) demonstrate that our approach is modular and can incorporate additional APIs.
PaRaDe: Passage Ranking using Demonstrations with Large Language Models

Recent studies show that large language models (LLMs) can be instructed to effectively perform zero-shot passage re-ranking, in which the results of a first stage retrieval method, such as BM25, are rated and reordered to improve relevance. In this work, we improve LLM-based re-ranking by algorithmically selecting few-shot demonstrations to include in the prompt. Our analysis investigates the conditions where demonstrations are most helpful, and shows that adding even one demonstration is significantly beneficial. We propose a novel demonstration selection strategy based on difficulty rather than the commonly used semantic similarity. Furthermore, we find that demonstrations helpful for ranking are also effective at question generation. We hope our work will spur more principled research into question generation and passage ranking.
Non-null and Force-Free Electromagnetic Configurations in Kerr Geometry

Force-Free Electrodynamics (FFE) is known to describe the highly magnetized plasma around pulsars and astrophysical black holes. The equations describing FFE are highly non-linear and the task of finding a well-defined analytical solution has been unyielding. However, FFE can also be understood in terms of 2-dimensional foliations of the ambient 4-dimensional spacetime. The study of the foliations can provide significant insights into the structure of the force-free fields and such foliations can be exploited to generate new null and non-null solutions. In this paper, we present several nonnull solutions to the FFE equations in the Kerr spacetime obtained by applying the aforementioned methods of non-null foliations.
Updates to the stonefly fauna of Illinois and Indiana

In a first phase for reconstructing the stonefly fauna of the Midwest (Ohio, Indiana, Illinois, southern Ontario, Michigan, and Wisconsin) we present updates for the Illinois and Indiana stonefly fauna. Data presented here represent over 11,000 records from the Illinois Natural History Survey and several regional museums. We present three additions to the Illinois fauna, 13 to the Indiana fauna, remove Amphinemura nigrita (Provancher) from both state lists, and provide records for many other species. Illinois supports or supported at least 79 species, while the number for Indiana is 87. The two states share 70 species or 84% of the fauna between them. While Illinois has lost 20 species (25.3%), Indiana has lost 10 (11.5%), mostly from the Wabash and White River drainages. Till plain areas in northern Indiana have fared better than those in Illinois because surficial deposits are sand that promote high groundwater recharge. Areas of high diversity remaining in the two states are concentrated in the unglaciated southern landscapes.
New insights on the seismogenic potential of the Eastern Betic Shear Zone (SE Iberia): Quaternary activity and paleoseismicity of the SW segment of the Carrascoy Fault Zone

The Carrascoy Fault (CAF) is one of the main active faults that form part of the Eastern Betic Shear Zone, a 450 km fault system that accommodates most of the convergence between the Eurasian (Iberia) and Nubian plates in the Betic Cordillera, south Spain. Although the CAF represents a major earthquake threat to the nearby City of Murcia, studies on its Quaternary tectonics and seismogenic potential are scarce to date. We present evidence that supports the division of the CAF into two overlapping segments with contrasting tectonic structure, Quaternary activity, and landform control: a SW segment, characterized by a broad fold‐and‐thrust zone similar to the forebergs defined in the Gobi‐Altai region, and a NE segment, characterized by a sharp mountain front controlled by strike‐slip tectonics. We attribute the differentiation into these two segments to the stresses associated with topography, which in turn is a consequence of the shortening component, at the middle Pleistocene, after circa 217.4 ka. For the SW segment we infer the occurrence of 9 to 11, Mw 6.7 paleoearthquakes in the last 30.2 kyr, and a slip rate of 0.37 ± 0.08 m/kyr. We date the occurrence of the last surface rupture event after 2750 B.P., and we estimate an average recurrence period of major events of 3.3 ± 0.7 kyr.
Early Planet Formation in Embedded Disks (eDisk). XII. Accretion Streamers, Protoplanetary Disk, and Outflow in the Class I Source Oph IRS 63

We present Atacama Large Millimeter/submillimeter Array (ALMA) observations of the Class I source Oph IRS 63 in the context of the Early Planet Formation in Embedded Disks large program. Our ALMA observations of Oph IRS 63 show a myriad of protostellar features, such as a shell-like bipolar outflow (in 12CO), an extended rotating envelope structure (in 13CO), a streamer connecting the envelope to the disk (in C18O), and several small-scale spiral structures seen toward the edge of the dust continuum (in SO). By analyzing the velocity pattern of 13CO and C18O, we measure a protostellar mass of M ⋆ = 0.5 ± 0.2 M ⊙ and confirm the presence of a disk rotating at almost Keplerian velocity that extends up to ∼260 au. These calculations also show that the gaseous disk is about four times larger than the dust disk, which could indicate dust evolution and radial drift. Furthermore, we model the C18O streamer and SO spiral structures as features originating from an infalling rotating structure that continuously feeds the young protostellar disk. We compute an envelope-to-disk mass infall rate of ∼10−6 M ⊙ yr−1 and compare it to the disk-to-star mass accretion rate of ∼10−8 M ⊙ yr−1, from which we infer that the protostellar disk is in a mass buildup phase. At the current mass infall rate, we speculate that soon the disk will become too massive to be gravitationally stable.
On the Relationship Between Relevance and Conflict in Online Social Link Recommendations

In an online social network, link recommendations are a way for users to discover relevant links to people they may know, thereby potentially increasing their engagement on the platform. However, the addition of links to a social network can also have an effect on the level of conflict in the network -- expressed in terms of polarization and disagreement. To this date, however, we have very little understanding of how these two implications of link formation relate to each other: are the goals of high relevance and conflict reduction aligned, or are the links that users are most likely to accept fundamentally different from the ones with the greatest potential for reducing conflict? Here we provide the first analysis of this question, using the recently popular Friedkin-Johnsen model of opinion dynamics. We first present a surprising result on how link additions shift the level of opinion conflict, followed by explanation work that relates the amount of shift to structural features of the added links. We then characterize the gap in conflict reduction between the set of links achieving the largest reduction and the set of links achieving the highest relevance. The gap is measured on real-world data, based on instantiations of relevance defined by 13 link recommendation algorithms. We find that some, but not all, of the more accurate algorithms actually lead to better reduction of conflict. Our work suggests that social links recommended for increasing user engagement may not be as conflict-provoking as people might have thought.
Physics-informed Neural Network Modelling and Predictive Control of District Heating Systems

This paper addresses the data-based modelling and optimal control of District Heating Systems (DHSs). Physical models of such large-scale networked systems are governed by complex nonlinear equations that require a large amount of parameters, leading to potential computational issues in optimizing their operation. A novel methodology is hence proposed, exploiting operational data and available physical knowledge to attain accurate and computationally efficient DHSs dynamic models. The proposed idea consists in leveraging multiple Recurrent Neural Networks (RNNs) and in embedding the physical topology of the DHS network in their interconnections. With respect to standard RNN approaches, the resulting modelling methodology, denoted as Physics-Informed RNN (PI-RNN), enables to achieve faster training procedures and higher modelling accuracy, even when reduced-dimension models are exploited. The developed PI-RNN modelling technique paves the way for the design of a Nonlinear Model Predictive Control (NMPC) regulation strategy, enabling, with limited computational time, to minimize production costs, to increase system efficiency and to respect operative constraints over the whole DHS network. The proposed methods are tested in simulation on a DHS benchmark referenced in the literature, showing promising results from the modelling and control perspective.
Improved Techniques for Training Consistency Models

Consistency models are a nascent family of generative models that can sample high quality data in one step without the need for adversarial training. Current consistency models achieve optimal sample quality by distilling from pre-trained diffusion models and employing learned metrics such as LPIPS. However, distillation limits the quality of consistency models to that of the pre-trained diffusion model, and LPIPS causes undesirable bias in evaluation. To tackle these challenges, we present improved techniques for consistency training, where consistency models learn directly from data without distillation. We delve into the theory behind consistency training and identify a previously overlooked flaw, which we address by eliminating Exponential Moving Average from the teacher consistency model. To replace learned metrics like LPIPS, we adopt Pseudo-Huber losses from robust statistics. Additionally, we introduce a lognormal noise schedule for the consistency training objective, and propose to double total discretization steps every set number of training iterations. Combined with better hyperparameter tuning, these modifications enable consistency models to achieve FID scores of 2.51 and 3.25 on CIFAR-10 and ImageNet $64\times 64$ respectively in a single sampling step. These scores mark a 3.5$\times$ and 4$\times$ improvement compared to prior consistency training approaches. Through two-step sampling, we further reduce FID scores to 2.24 and 2.77 on these two datasets, surpassing those obtained via distillation in both one-step and two-step settings, while narrowing the gap between consistency models and other state-of-the-art generative models.
Elastic flow of curves with partial free boundary

We consider a curve with boundary points free to move on a line in $\R^2$, which evolves by the $L^2$--gradient flow of the elastic energy, that is a linear combination of the Willmore and the length functional. For such planar evolution problem we study the short and long--time existence. Once we establish under which boundary conditions the PDE's system is well--posed (in our case the Navier boundary conditions), employing the Solonnikov theory for linear parabolic systems in H\"older space, we show that there exists a unique flow in a maximal time interval $[0,T)$. Then, using energy methods we prove that the maximal time is actually $T= + \infty$.
Boosting Unsupervised Machine Translation with Pseudo-Parallel Data

Even with the latest developments in deep learning and large-scale language modeling, the task of machine translation (MT) of low-resource languages remains a challenge. Neural MT systems can be trained in an unsupervised way without any translation resources but the quality lags behind, especially in truly low-resource conditions. We propose a training strategy that relies on pseudo-parallel sentence pairs mined from monolingual corpora in addition to synthetic sentence pairs back-translated from monolingual corpora. We experiment with different training schedules and reach an improvement of up to 14.5 BLEU points (English to Ukrainian) over a baseline trained on back-translated data only.
Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions

A natural way to design a negotiation dialogue system is via self-play RL: train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data. Although this procedure has been adopted in prior work, we find that it results in a fundamentally flawed system that fails to learn the value of compromise in a negotiation, which can often lead to no agreements (i.e., the partner walking away without a deal), ultimately hurting the model's overall performance. We investigate this observation in the context of the DealOrNoDeal task, a multi-issue negotiation over books, hats, and balls. Grounded in negotiation theory from Economics, we modify the training procedure in two novel ways to design agents with diverse personalities and analyze their performance with human partners. We find that although both techniques show promise, a selfish agent, which maximizes its own performance while also avoiding walkaways, performs superior to other variants by implicitly learning to generate value for both itself and the negotiation partner. We discuss the implications of our findings for what it means to be a successful negotiation dialogue system and how these systems should be designed in the future.
Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in NMS

Non-maximum suppression (NMS) is an essential post-processing module used in many 3D object detection frameworks to remove overlapping candidate bounding boxes. However, an overreliance on classification scores and difficulties in determining appropriate thresholds can affect the resulting accuracy directly. To address these issues, we introduce fuzzy learning into NMS and propose a novel generalized Fuzzy-NMS module to achieve finer candidate bounding box filtering. The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process. Adequate validation experiments are conducted using the mainstream KITTI and large-scale Waymo 3D object detection benchmarks. The results of these tests demonstrate the proposed Fuzzy-NMS module can improve the accuracy of numerous recently NMS-based detectors significantly, including PointPillars, PV-RCNN, and IA-SSD, etc. This effect is particularly evident for small objects such as pedestrians and bicycles. As a plug-and-play module, Fuzzy-NMS does not need to be retrained and produces no obvious increases in inference time.