Abstract

This study employed the P-MiSTIC method to analyze decadal temperature patterns in contiguous Peninsular India. A gridded temperature dataset from IMD spanning 1990-2020 was utilized, generating three decade-wise datasets. P-MiSTIC identified similar zones in decadal and overall datasets, revealing localized data characteristics. The 30-year trend for diurnal temperature range (DTR) showed a significant increase of 0.002oC/year for all zones, while the Western Himalayas and Karakoram range exhibited higher increasing trends of 0.019oC/year and 0.028oC/year, respectively. The DTR lapse rate over 30 years was quantified as -0.133oC/km, with a sharp decline observed from the first two decades (0.012 & 0.028 oC/km) to the third decade (-0.533 oC/km). Differential elevation-dependent temperature changes were observed, with the Karakoram region displaying the most prominent increase, suggesting the emergence of Elevation Dependent Warming in contiguous Peninsular India.

Abstract

Researchers have extensively studied the field of vision and language, discovering that both visual and textual content is crucial for understanding scenes effectively. Particularly, comprehending text in videos holds great significance, requiring both scene text understanding and temporal reasoning. This paper focuses on exploring two recently introduced datasets, NewsVideoQA and M4-ViteVQA, which aim to address video question answering based on textual content. The NewsVideoQA dataset contains question-answer pairs related to the text in news videos, while M4-ViteVQA comprises question-answer pairs from diverse categories like vlogging, traveling, and shopping. We provide an analysis of the formulation of these datasets on various levels, exploring the degree of visual understanding and multi-frame comprehension required for answering the questions. Additionally, the study includes experimentation with BERT-QA, a text-only model, which demonstrates comparable performance to the original methods on both datasets, indicating the shortcomings in the formulation of these datasets. Furthermore, we also look into the domain adaptation aspect by examining the effectiveness of training on M4-ViteVQA and evaluating on NewsVideoQA and vice-versa, thereby shedding light on the challenges and potential benefits of out-of-domain training.

Abstract

We propose CLIP-Head, a novel approach towards text-driven neu- ral parametric 3D head model generation. Our method takes simple text prompts in natural language, describing the appearance & facial expressions, and generates 3D neural head avatars with ac- curate geometry and high-quality texture maps. Unlike existing approaches, which use conventional parametric head models with limited control and expressiveness, we leverage Neural Parametric Head Models (NPHM), offering disjoint latent codes for the dis- entangled encoding of identities and expressions. To facilitate the text-driven generation, we propose two weakly-supervised map- ping networks to map the CLIP’s encoding of input text prompt to NPHM’s disjoint identity and expression vector. The predicted latent codes are then fed to a pre-trained NPHM network to gener- ate 3D head geometry. Since NPHM mesh doesn’t support textures, we propose a novel aligned parametrization technique, followed by text-driven generation of texture maps by leveraging a recently pro- posed controllable diffusion model for the task of text-to-image syn- thesis. Our method is capable of generating 3D head meshes with arbitrary appearances and a variety of facial expressions, along with photoreal texture details. We show superior performance with ex- isting state-of-the-art methods, both qualitatively & quantitatively, and demonstrate potentially useful applications of our method.

Abstract

The discovery of potential therapeutic agents for life-threatening diseases has become a significant problem. There is a requirement for fast and accurate methods to identify drug-like molecules that can be used as potential candidates for novel targets. Existing techniques like high-throughput screening and virtual screening are time-consuming and inefficient. Traditional molecule generation pipelines are more efficient than virtual screening but use time-consuming docking software. Such docking functions can be emulated using Machine Learning models with comparable accuracy and faster execution times. However, we find that when pre-trained machine learning models are employed in generative pipelines as oracles, they suffer from model degradation in areas where data is scarce. In this study, we propose an active learning-based model that can be added as a supplement to enhanced molecule generation architectures. The proposed method uses uncertainty sampling on the molecules created by the generator model and dynamically learns as the generator samples molecules from different regions of the chemical space. The proposed framework can generate molecules with high binding affinity with ∼ a 70% improvement in runtime compared to the baseline model by labeling only ∼ 30% of molecules compared to the baseline oracle.

Abstract

Robust spectrum sensing is crucial for facilitating opportunistic spectrum utilization for secondary users (SU) in the absense of primary users (PU). However, propagation environment factors such as multi-path fading, shadowing, and lack of line of sight (LoS) often adversely affect detection performance. To deal with these issues, this paper focuses on utilizing reconfig- urable intelligent surfaces (RIS) to improve spectrum sensing in the scenario wherein both the multi-path fading and noise are correlated. In particular, to leverage the spatially correlated fading, we propose to use maximum eigenvalue detection (MED) for spectrum sensing. We first derive exact distributions of test statistics, i.e., the largest eigenvalue of the sample covariance matrix, observed under the null and signal present hypothesis. Next, utilizing these results, we present the exact closed-form expressions for the false alarm and detection probabilities. In addition, we also optimally configure the phase shift matrix of RIS such that the mean of the test statistics is maximized, thus improving the detection performance. Our numerical analysis demonstrates that the MED’s receiving operating characteristic (ROC) curve improves with increased RIS elements, SNR, and the utilization of statistically optimal configured RIS. Index Terms—Reconfigurable Intelligent Surfaces, Spectrum Sensing, Maximum Eigenvalue Detector, Correlated Fading, e

Abstract

This paper presents a star-of-star topology for internet-of-things (IoT) networks using mega low-Earth-orbit constellations. The proposed topology enables IoT users to broadcast their sensed data to multiple satellites simultaneously over a shared channel, which is then relayed to the ground station (GS) using amplify-and-forward relaying. The GS coherently combines the signals from multiple satellites using maximal ratio combining. To analyze the performance of the proposed topology in the presence of interference, a comprehensive outage probabil- ity (OP) analysis is performed, assuming imperfect channel state information at the GS. The paper employs stochastic geometry to model the random locations of satellites, making the analysis gen- eral and independent of any specific constellation. Furthermore, the paper examines successive interference cancellation (SIC) and capture model (CM)-based decoding schemes at the GS to mitigate interference. The average OP for the CM-based scheme and the OP of the best user for the SIC scheme are derived analytically. The paper also presents simplified expressions for the OP under a high signal-to-noise ratio (SNR) assumption, which are utilized to optimize the system parameters for achieving a tar- get OP. The simulation results are consistent with the analytical expressions and provide insights into the impact of various system parameters, such as mask angle, altitude, number of satellites, and decoding order. The findings of this study demonstrate that the proposed topology can effectively leverage the benefits of multiple satellites to achieve the desired OP and enable burst transmissions without coordination among IoT users, making it an attractive choice for satellite-based IoT networks. Index Terms—Amplify-and-forward, LEO satellites, outage probability, satellite-based IoT, stochastic geometry

Abstract

In the Structural Health Monitoring (SHM) framework, model updating procedure is an iterative or cyclical process which gives numerical model of structure having modal behaviour close to the dynamic behaviour of real structure. Normally for structures with or without any expansion joints typical model updating procedure is used. But the typical procedure may not be applicable for long uniform buildings having expansion joints since the individual blocks in structure connected with expansion joints tend to vibrate in coherence. This behaviour is confirmed by observing the identicalness of modal frequencies of individual blocks in long uniform buildings. This phenomenon was observed in three selected long uniform buildings. When structures connected with expansion joints vibrate in coherence or when the modal frequency of the connected structures are identical, the numerical models of these structures cannot be updated using typical procedure for the following reasons: (1) Modal parameters from Modal identification of structure doesn’t represent the actual parameters of the individual block since the ambient vibration is not independent and is influenced by interaction between blocks. (2) Modal analysis of analytical model of individual structural blocks gives dynamic parameters which are true to the model. And these two sets of parameters can’t be compared for convergence in model updating procedure. Therefore, to update a numerical model of long uniform buildings with expansion joints having the coherence phenomenon among its individual blocks, a different is needed. Hence, a new approach is proposed where instead of using modal parameters from analytical analysis, the parameters obtained for the structure from modelled floor responses to weak earthquake using Frequency Domain Decomposition (FDD) algorithm are used for checking convergence in model updating procedure. To demonstrate the proposed procedure, two long uniform buildings in which the coherence phenomenon was observed were updated by applying the proposed procedure and results are discussed. Here for the selected buildings, only modal frequencies were used for checking convergence, since mode shapes obtained were not reliable as floor vibration data of intermediate floors were not available.

Abstract

Large-scale deployment of fully autonomous vehicles re- quires a very high degree of robustness to unstructured traf- fic, weather conditions, and should prevent unsafe mispre- dictions. While there are several datasets and benchmarks focusing on segmentation for drive scenes, they are not specifically focused on safety and robustness issues. We introduce the IDD-AW dataset, which provides 5000 pairs of high-quality images with pixel-level annotations, cap- tured under rain, fog, low light, and snow in unstructured driving conditions. As compared to other adverse weather datasets, we provide i.) more annotated images, ii.) paired Near-Infrared (NIR) image for each frame, iii.) larger label set with a 4-level label hierarchy to capture unstructured traffic conditions. We benchmark state-of-the-art models for semantic segmentation in IDD-AW. We also propose a new metric called “Safe mean Intersection over Union (Safe mIoU)” for hierarchical datasets which penalizes danger- ous mispredictions that are not captured in the traditional definition of mean Intersection over Union (mIoU). The re- sults show that IDD-AW is one of the most challenging datasets to date for these tasks. The dataset and code will be available here: http://iddaw.github.io

Abstract

Unconstrained Asian roads often involve poor infrastructure, affecting overall road safety. Missing traffic signs are a regular part of such roads. Missing or non-existing object detection has been studied for locating missing curbs and estimating reasonable regions for pedestrians on road scene images. Such methods involve analyzing task-specific single object cues. In this paper, we present the first and most challenging video dataset for missing objects, with multiple types of traffic signs for which the cues are visible without the signs in the scenes. We refer to it as the Missing Traffic Signs Video Dataset (MTSVD). MTSVD is challenging compared to the previous works in two aspects i) The traffic signs are generally not present in the vicinity of their cues, ii) The traffic signs’ cues are diverse and unique. Also, MTSVD is the first publicly available missing object dataset. To train the models for identifying missing signs, we complement our dataset with 10K traffic sign tracks, with 40% of the traffic signs having cues visible in the scenes. For identifying missing signs, we propose the Cue-driven Contextual Attention units (CueCAn), which we incorporate in our model’s encoder. We first train the encoder to classify the presence of traffic sign cues and then train the entire segmentation model end-to-end to localize missing traffic signs. Quantitative and qualitative analysis shows that CueCAn significantly improves the performance of base models.

Abstract

Counter-intuitive to classical notions, quantum conditional entropy can be negative, playing a pivotal role in information-processing tasks. This article delves deeply into quantum channels, em- phasizing negative conditional entropy breaking channels (NCEB) and introducing negative condi- tional entropy annihilating channels (NCEA). We characterize these channels from both topological and information-theoretic perspectives, examining their properties when combined serially and in parallel. Our exploration extends to complimentary channels associated with NCEB, leading to the introduction of information-leaking channels. Utilizing the parameters of the standard depolarizing channel, we provide tangible examples and further characterization. We demonstrate the relation- ship of NCEB and NCEA with newly introduced channels like coherent information breaking (CIB) and mutual information breaking (MIB), along with standard channels like zero capacity channels. Preservation of quantum resources is an integral constituent of quantum information theory. Rec- ognizing this, we lay prescriptions to detect channels that do not break the negativity of conditional entropy, ensuring the conservation of this quantum resource