Miracle-dz/UniFAR-FTUs huggingface dataset:
DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis

We present DiffuScene for indoor 3D scene synthesis based on a novel scene configuration denoising diffusion model. It generates 3D instance properties stored in an unordered object set and retrieves the most similar geometry for each object configuration, which is characterized as a concatenation of different attributes, including location, size, orientation, semantics, and geometry features. We introduce a diffusion network to synthesize a collection of 3D indoor objects by denoising a set of unordered object attributes. Unordered parametrization simplifies and eases the joint distribution approximation. The shape feature diffusion facilitates natural object placements, including symmetries. Our method enables many downstream applications, including scene completion, scene arrangement, and text-conditioned scene synthesis. Experiments on the 3D-FRONT dataset show that our method can synthesize more physically plausible and diverse indoor scenes than state-of-the-art methods. Extensive ablation studies verify the effectiveness of our design choice in scene diffusion models.
D$^2$-JSCC: Digital Deep Joint Source-channel Coding for Semantic Communications

Semantic communications (SemCom) have emerged as a new paradigm for supporting sixth-generation applications, where semantic features of data are transmitted using artificial intelligence algorithms to attain high communication efficiencies. Most existing SemCom techniques utilize deep neural networks (DNNs) to implement analog source-channel mappings, which are incompatible with existing digital communication architectures. To address this issue, this paper proposes a novel framework of digital deep joint source-channel coding (D$^2$-JSCC) targeting image transmission in SemCom. The framework features digital source and channel codings that are jointly optimized to reduce the end-to-end (E2E) distortion. First, deep source coding with an adaptive density model is designed to encode semantic features according to their distributions. Second, digital channel coding is employed to protect encoded features against channel distortion. To facilitate their joint design, the E2E distortion is characterized as a function of the source and channel rates via the analysis of the Bayesian model and Lipschitz assumption on the DNNs. Then to minimize the E2E distortion, a two-step algorithm is proposed to control the source-channel rates for a given channel signal-to-noise ratio. Simulation results reveal that the proposed framework outperforms classic deep JSCC and mitigates the cliff and leveling-off effects, which commonly exist for separation-based approaches.
TRMM 3B43 RAIN DATA INFORMATION IN DETERMINING LONG WET AND DRY PERIODS IN FARMING BUSINESS IN MOONSON AREA

Based on the observation towards climate station in several agriculture production centers, there is an increase temperature. Temperature rise due to greenhouse effect is very clearly visible, e.g., in Banjarbaru Indonesian Agency of Meteorological, Climatology, and Geophysics (BMKG), misal di stasiun BMKG Banjarbaru, Banjarmasin Sei Tabuk Special Farm Meteorology Station (SMPK), and South Kalimantan Kotabaru BMKG. This temperature rise impacted to plant’s respiration increase that led to the reduction of photosynthesis result. Adaptation strategy with plants water balance analysis to determine the correct planting time and surplus period and water deficit, in various growth phases, is one of water permanent strategic measures to know water suffiency in the field. Material used in this research is TRMM from 3B43 type in the form of grid with monthly temporal resolution and spatial resolution 0.25 x 0.25 with data from 1998 – 2015 period. Data format in the form of binary from ftp://disc2.nascom. nasa.gov/data/TRMM/Gridded/3B43_V7. Reanalysis surface temperature data is grid data from European Centre for Medium-Range Weather Forecasts (ECMWF) from 1998 -2015 period in netcdf format. Water balance analysis used Thornthwaite and Mather (1957) method. Water balance analysis shown groundwater in sufficient category (>60%) happens during November – July period, meanwhile water surplus happens during December – June period. Whereas during August – October period is in less period (<40%). This information can be used in the formulation of one season planting pattern both in wet or dry field.
Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model

More accurate extraction of invisible building footprints from very-high-resolution (VHR) aerial images relies on roof segmentation and roof-to-footprint offset extraction. Existing state-of-the-art methods based on instance segmentation suffer from poor generalization when extended to large-scale data production and fail to achieve low-cost human interactive annotation. The latest prompt paradigms inspire us to design a promptable framework for roof and offset extraction, which transforms end-to-end algorithms into promptable methods. Within this framework, we propose a novel Offset-Building Model (OBM). To rigorously evaluate the algorithm's capabilities, we introduce a prompt-based evaluation method, where our model reduces offset errors by 16.6% and improves roof Intersection over Union (IoU) by 10.8% compared to other models. Leveraging the common patterns in predicting offsets, we propose Distance-NMS (DNMS) algorithms, enabling the model to further reduce offset vector loss by 6.5%. To further validate the generalization of models, we tested them using a new dataset with over 7,000 manually annotated instance samples. Our algorithms and dataset are available at https://anonymous.4open. science/r/OBM-B3EC.
The Missing Piece in Model Editing: A Deep Dive into the Hidden Damage Brought By Model Editing

Large Language Models have revolutionized numerous tasks with their remarkable efficacy.However, the editing of these models, crucial for rectifying outdated or erroneous information, often leads to a complex issue known as the ripple effect in the hidden space. This effect, while difficult to detect, can significantly impede the efficacy of model editing tasks and deteriorate model performance.This paper addresses this scientific challenge by proposing a novel evaluation methodology, Graphical Outlier Relation based Assessment(GORA), which quantitatively evaluates the adaptations of the model and the subsequent impact of editing. Furthermore, we introduce the Selective Outlier Re-Editing Approach(SORA), a model editing method designed to mitigate this ripple effect. Our comprehensive evaluations reveal that the ripple effect in the hidden space is a significant issue in all current model editing methods. However, our proposed methods, GORA and SORA, effectively identify and alleviate this issue, respectively, contributing to the advancement of LLM editing techniques.
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge. Our method, named Branch-Train-MiX (BTX), starts from a seed model, which is branched to train experts in embarrassingly parallel fashion with high throughput and reduced communication cost. After individual experts are asynchronously trained, BTX brings together their feedforward parameters as experts in Mixture-of-Expert (MoE) layers and averages the remaining parameters, followed by an MoE-finetuning stage to learn token-level routing. BTX generalizes two special cases, the Branch-Train-Merge method, which does not have the MoE finetuning stage to learn routing, and sparse upcycling, which omits the stage of training experts asynchronously. Compared to alternative approaches, BTX achieves the best accuracy-efficiency tradeoff.
Frequency Decoupling for Motion Magnification via Multi-Level Isomorphic Architecture

Video Motion Magnification (VMM) aims to reveal subtle and imperceptible motion information of objects in the macroscopic world. Prior methods directly model the motion field from the Eulerian perspective by Representation Learning that separates shape and texture or Multi-domain Learning from phase fluctuations. Inspired by the frequency spectrum, we observe that the low-frequency components with stable energy always possess spatial structure and less noise, making them suitable for modeling the subtle motion field. To this end, we present FD4MM, a new paradigm of Frequency Decoupling for Motion Magnification with a Multi-level Isomorphic Architecture to capture multi-level high-frequency details and a stable low-frequency structure (motion field) in video space. Since high-frequency details and subtle motions are susceptible to information degradation due to their inherent subtlety and unavoidable external interference from noise, we carefully design Sparse High/Low-pass Filters to enhance the integrity of details and motion structures, and a Sparse Frequency Mixer to promote seamless recoupling. Besides, we innovatively design a contrastive regularization for this task to strengthen the model's ability to discriminate irrelevant features, reducing undesired motion magnification. Extensive experiments on both Real-world and Synthetic Datasets show that our FD4MM outperforms SOTA methods. Meanwhile, FD4MM reduces FLOPs by 1.63$\times$ and boosts inference speed by 1.68$\times$ than the latest method. Our code is available at https://github.com/Jiafei127/FD4MM.
Mathematical analysis and numerical comparison of energy-conservative schemes for the Zakharov equations

Furihata and Matsuo proposed in 2010 an energy-conserving scheme for the Zakharov equations, as an application of the discrete variational derivative method (DVDM). This scheme is distinguished from conventional methods (in particular the one devised by Glassey in 1992) in that the invariants are consistent with respect to time, but it has not been sufficiently studied both theoretically and numerically. In this study, we theoretically prove the solvability under the loosest possible assumptions. We also prove the convergence of this DVDM scheme by improving the argument by Glassey. Furthermore, we perform intensive numerical experiments for comparing the above two schemes. It is found that the DVDM scheme is superior in terms of accuracy, but since it is fully-implicit, the linearly-implicit Glassey scheme is better for practical efficiency. In addition, we proposed a way to choose a solution for the first step that would allow Glassey's scheme to work more efficiently.
DrPlanner: Diagnosis and Repair of Motion Planners Using Large Language Models

Motion planners are essential for the safe operation of automated vehicles across various scenarios. However, no motion planning algorithm has achieved perfection in the literature, and improving its performance is often time-consuming and labor-intensive. To tackle the aforementioned issues, we present DrPlanner, the first framework designed to automatically diagnose and repair motion planners using large language models. Initially, we generate a structured description of the planner and its planned trajectories from both natural and programming languages. Leveraging the profound capabilities of large language models in addressing reasoning challenges, our framework returns repaired planners with detailed diagnostic descriptions. Furthermore, the framework advances iteratively with continuous feedback from the evaluation of the repaired outcomes. Our approach is validated using search-based motion planners; experimental results highlight the need of demonstrations in the prompt and the ability of our framework in identifying and rectifying elusive issues effectively.
Time-Efficient and Identity-Consistent Virtual Try-On Using A Variant of Altered Diffusion Models

This study discusses the critical issues of Virtual Try-On in contemporary e-commerce and the prospective metaverse, emphasizing the challenges of preserving intricate texture details and distinctive features of the target person and the clothes in various scenarios, such as clothing texture and identity characteristics like tattoos or accessories. In addition to the fidelity of the synthesized images, the efficiency of the synthesis process presents a significant hurdle. Various existing approaches are explored, highlighting the limitations and unresolved aspects, e.g., identity information omission, uncontrollable artifacts, and low synthesis speed. It then proposes a novel diffusion-based solution that addresses garment texture preservation and user identity retention during virtual try-on. The proposed network comprises two primary modules - a warping module aligning clothing with individual features and a try-on module refining the attire and generating missing parts integrated with a mask-aware post-processing technique ensuring the integrity of the individual's identity. It demonstrates impressive results, surpassing the state-of-the-art in speed by nearly 20 times during inference, with superior fidelity in qualitative assessments. Quantitative evaluations confirm comparable performance with the recent SOTA method on the VITON-HD and Dresscode datasets.
Large fluctuations in the Sky

Renormalization of quantum loop effects generated from large fluctuations is a hugely debatable topic of research these days which rules out the Primordial Black Hole (PBH) formation within the framework of single-field inflation. In this article, we briefly discuss that the correct implementation of regularization, renormalization, and resummation techniques in a setup described by an ultra-slow-roll phase sandwiched between two slow-roll phases in the presence of smooth or sharp transitions can lead to a stringent constraint on the PBH mass (i.e. ${\cal O}(10^{2}{\rm gm}$)), which we advertise as a new No-go theorem. Finally, we will give some of the possible way-outs using which one can evade this proposed No-go theorem and produce solar/sub-solar mass PBHs.
Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning

Toddlers evolve from free exploration with sparse feedback to exploiting prior experiences for goal-directed learning with denser rewards. Drawing inspiration from this Toddler-Inspired Reward Transition, we set out to explore the implications of varying reward transitions when incorporated into Reinforcement Learning (RL) tasks. Central to our inquiry is the transition from sparse to potential-based dense rewards, which share optimal strategies regardless of reward changes. Through various experiments, including those in egocentric navigation and robotic arm manipulation tasks, we found that proper reward transitions significantly influence sample efficiency and success rates. Of particular note is the efficacy of the toddler-inspired Sparse-to-Dense (S2D) transition. Beyond these performance metrics, using Cross-Density Visualizer technique, we observed that transitions, especially the S2D, smooth the policy loss landscape, promoting wide minima that enhance generalization in RL models.
Gabor-guided transformer for single image deraining

Image deraining have have gained a great deal of attention in order to address the challenges posed by the effects of harsh weather conditions on visual tasks. While convolutional neural networks (CNNs) are popular, their limitations in capturing global information may result in ineffective rain removal. Transformer-based methods with self-attention mechanisms have improved, but they tend to distort high-frequency details that are crucial for image fidelity. To solve this problem, we propose the Gabor-guided tranformer (Gabformer) for single image deraining. The focus on local texture features is enhanced by incorporating the information processed by the Gabor filter into the query vector, which also improves the robustness of the model to noise due to the properties of the filter. Extensive experiments on the benchmarks demonstrate that our method outperforms state-of-the-art approaches.
Towards Full Automation of Geometry Extraction for Biomechanical Analysis of Abdominal Aortic Aneurysm; Neural Network-Based versus Classical Methodologies

In this study we investigated the impact of image segmentation methods on the results of stress computation in the wall of abdominal aortic aneurysms (AAAs). We compared wall stress distributions and magnitudes calculated from geometry models obtained from classical semi-automated segmentation versus automated neural network-based segmentation. Ten different AAA contrast-enhanced computed tomography (CT) images were semi-automatically segmented by an analyst, taking, depending on the quality of an image, between 15 and 40 minutes of human effort per patient. The same images were automatically segmented using PRAEVAorta 2, commercial software by NUREA (https://www.nurea-soft.com/), developed based on artificial intelligence (AI) algorithms, requiring only 1-2 minutes of computer time per patient. Aneurysm wall stress calculations performed using the BioPARR software (https://bioparr.mech.uwa.edu.au/) revealed that, compared to the classical semi-automated segmentation, the automatic neural network-based segmentation leads to equivalent stress distributions, and slightly higher peak and 99th percentile maximum principal stress values. This difference is due to consistently larger lumen surface areas in automatically segmented models as compared to classical semi-automated segmentations, resulting in greater total pressure load on the wall. Our findings are a steppingstone toward a fully automated pipeline for biomechanical analysis of AAAs, starting with CT scans and concluding with wall stress assessment, while at the same time highlighting the critical importance of the repeatable and accurate segmentation of the lumen, the difficult problem often underestimated by the literature.
Auxiliary CycleGAN-guidance for Task-Aware Domain Translation from Duplex to Monoplex IHC Images

Generative models enable the translation from a source image domain where readily trained models are available to a target domain unseen during training. While Cycle Generative Adversarial Networks (GANs) are well established, the associated cycle consistency constrain relies on that an invertible mapping exists between the two domains. This is, however, not the case for the translation between images stained with chromogenic monoplex and duplex immunohistochemistry (IHC) assays. Focusing on the translation from the latter to the first, we propose - through the introduction of a novel training design, an alternative constrain leveraging a set of immunofluorescence (IF) images as an auxiliary unpaired image domain. Quantitative and qualitative results on a downstream segmentation task show the benefit of the proposed method in comparison to baseline approaches.
Emerging Technologies for 6G Non-Terrestrial-Networks: From Academia to Industrial Applications

Terrestrial networks form the fundamental infrastructure of modern communication systems, serving more than 4 billion users globally. However, terrestrial networks are facing a wide range of challenges, from coverage and reliability to interference and congestion. As the demands of the 6G era are expected to be much higher, it is crucial to address these challenges to ensure a robust and efficient communication infrastructure for the future. To address these problems, Non-terrestrial Network (NTN) has emerged to be a promising solution. NTNs are communication networks that leverage airborne (e.g., unmanned aerial vehicles) and spaceborne vehicles (e.g., satellites) to facilitate ultra-reliable communications and connectivity with high data rates and low latency over expansive regions. This article aims to provide a comprehensive survey on the utilization of network slicing, Artificial Intelligence/Machine Learning (AI/ML), and Open Radio Access Network (ORAN) to address diverse challenges of NTNs from the perspectives of both academia and industry. Particularly, we first provide an in-depth tutorial on NTN and the key enabling technologies including network slicing, AI/ML, and ORAN. Then, we provide a comprehensive survey on how network slicing and AI/ML have been leveraged to overcome the challenges that NTNs are facing. Moreover, we present how ORAN can be utilized for NTNs. Finally, we highlight important challenges, open issues, and future research directions of NTN in the 6G era.
TCAM-SSD: A Framework for Search-Based Computing in Solid-State Drives

As the amount of data produced in society continues to grow at an exponential rate, modern applications are incurring significant performance and energy penalties due to high data movement between the CPU and memory/storage. While processing in main memory can alleviate these penalties, it is becoming increasingly difficult to keep large datasets entirely in main memory. This has led to a recent push for in-storage computation, where processing is performed inside the storage device. We propose TCAM-SSD, a new framework for search-based computation inside the NAND flash memory arrays of a conventional solid-state drive (SSD), which requires lightweight modifications to only the array periphery and firmware. TCAM-SSD introduces a search manager and link table, which can logically partition the NAND flash memory's contents into search-enabled regions and standard storage regions. Together, these light firmware changes enable TCAM-SSD to seamlessly handle block I/O operations, in addition to new search operations, thereby reducing end-to-end execution time and total data movement. We provide an NVMe-compatible interface that provides programmers with the ability to dynamically allocate data on and make use of TCAM-SSD, allowing the system to be leveraged by a wide variety of applications. We evaluate three example use cases of TCAM-SSD to demonstrate its benefits. For transactional databases, TCAM-SSD can mitigate the performance penalties for applications with large datasets, achieving a 60.9% speedup over a conventional system that retrieves data from the SSD and computes using the CPU. For database analytics, TCAM-SSD provides an average speedup of 17.7x over a conventional system for a collection of analytical queries. For graph analytics, we combine TCAM-SSD's associative search with a sparse data structure, speeding up graph computing for larger-than-memory datasets by 14.5%.
Inverse Garment and Pattern Modeling with a Differentiable Simulator

The capability to generate simulation-ready garment models from 3D shapes of clothed humans will significantly enhance the interpretability of captured geometry of real garments, as well as their faithful reproduction in the virtual world. This will have notable impact on fields like shape capture in social VR, and virtual try-on in the fashion industry. To align with the garment modeling process standardized by the fashion industry as well as cloth simulation softwares, it is required to recover 2D patterns. This involves an inverse garment design problem, which is the focus of our work here: Starting with an arbitrary target garment geometry, our system estimates an animatable garment model by automatically adjusting its corresponding 2D template pattern, along with the material parameters of the physics-based simulation (PBS). Built upon a differentiable cloth simulator, the optimization process is directed towards minimizing the deviation of the simulated garment shape from the target geometry. Moreover, our produced patterns meet manufacturing requirements such as left-to-right-symmetry, making them suited for reverse garment fabrication. We validate our approach on examples of different garment types, and show that our method faithfully reproduces both the draped garment shape and the sewing pattern.