Miracle-dz/UniFAR-FTUs huggingface dataset:
IIITD-20K: Dense captioning for Text-Image ReID

Text-to-Image (T2I) ReID has attracted a lot of attention in the recent past. CUHK-PEDES, RSTPReid and ICFG-PEDES are the three available benchmarks to evaluate T2I ReID methods. RSTPReid and ICFG-PEDES comprise of identities from MSMT17 but due to limited number of unique persons, the diversity is limited. On the other hand, CUHK-PEDES comprises of 13,003 identities but has relatively shorter text description on average. Further, these datasets are captured in a restricted environment with limited number of cameras. In order to further diversify the identities and provide dense captions, we propose a novel dataset called IIITD-20K. IIITD-20K comprises of 20,000 unique identities captured in the wild and provides a rich dataset for text-to-image ReID. With a minimum of 26 words for a description, each image is densely captioned. We further synthetically generate images and fine-grained captions using Stable-diffusion and BLIP models trained on our dataset. We perform elaborate experiments using state-of-art text-to-image ReID models and vision-language pre-trained models and present a comprehensive analysis of the dataset. Our experiments also reveal that synthetically generated data leads to a substantial performance improvement in both same dataset as well as cross dataset settings. Our dataset is available at https://bit.ly/3pkA3Rj.
Averaged Lomb Periodograms for Nonuniform Sampling

In spite of the fact that a fast Lomb transform [5] has been developed, the evaluation of the Lomb spectrum still consumes considerably more computational cycles than the combined operations of uniform resampling and nonparametric spectral estimation based on the fast Fourier transform (FFT). Like the periodogram, the Lomb periodogram is not a statistically consistent estimator. To obtain a spectral estimate that strikes a reasonable tradeoff between bias and variance, one may want to consider power averaging transforms of several, possibly overlapped, windowed segments of the observed signal. This is similar to the approaches used in averaged Bartlett (adjacent segments) and Welch (overlapped segments) periodograms. In order to be able to average Lomb periodograms, two problems need to be resolved 1. The Lomb transform, as described in the literature, is variance normalized. Since each record is individually normalized, averaging across records is not meaningful. A denormalized transform is needed. 2. Since the data are unevenly sampled, how should the long data record be split for averaging purpose?
Science and Technology Ontology: A Taxonomy of Emerging Topics

Ontologies play a critical role in Semantic Web technologies by providing a structured and standardized way to represent knowledge and enabling machines to understand the meaning of data. Several taxonomies and ontologies have been generated, but individuals target one domain, and only some of those have been found expensive in time and manual effort. Also, they need more coverage of unconventional topics representing a more holistic and comprehensive view of the knowledge landscape and interdisciplinary collaborations. Thus, there needs to be an ontology covering Science and Technology and facilitate multidisciplinary research by connecting topics from different fields and domains that may be related or have commonalities. To address these issues, we present an automatic Science and Technology Ontology (S&TO) that covers unconventional topics in different science and technology domains. The proposed S&TO can promote the discovery of new research areas and collaborations across disciplines. The ontology is constructed by applying BERTopic to a dataset of 393,991 scientific articles collected from Semantic Scholar from October 2021 to August 2022, covering four fields of science. Currently, S&TO includes 5,153 topics and 13,155 semantic relations. S&TO model can be updated by running BERTopic on more recent datasets
Joint Compression and Deadline Optimization for Wireless Federated Learning

Federated edge learning (FEEL) is a popular distributed learning framework for privacy-preserving at the edge, in which densely distributed edge devices periodically exchange model-updates with the server to complete the global model training. Due to limited bandwidth and uncertain wireless environment, FEEL may impose heavy burden to the current communication system. In addition, under the common FEEL framework, the server needs to wait for the slowest device to complete the update uploading before starting the aggregation process, leading to the straggler issue that causes prolonged communication time. In this paper, we propose to accelerate FEEL from two aspects: i.e., 1) performing data compression on the edge devices and 2) setting a deadline on the edge server to exclude the straggler devices. However, undesired gradient compression errors and transmission outage are introduced by the aforementioned operations respectively, affecting the convergence of FEEL as well. In view of these practical issues, we formulate a training time minimization problem, with the compression ratio and deadline to be optimized. To this end, an asymptotically unbiased aggregation scheme is first proposed to ensure zero optimality gap after convergence, and the impact of compression error and transmission outage on the overall training time are quantified through convergence analysis. Then, the formulated problem is solved in an alternating manner, based on which, the novel joint compression and deadline optimization (JCDO) algorithm is derived. Numerical experiments for different use cases in FEEL including image classification and autonomous driving show that the proposed method is nearly 30X faster than the vanilla FedAVG algorithm, and outperforms the state-of-the-art schemes.
Sufficient conditions for the unique solvability of absolute value matrix equations

In this paper, we discussed the unique solvability of the two absolute value matrix equations. The unique solvability condition $\rho (\vert A^{-1} B \vert)<1$ is provided for the generalized absolute value matrix equation (GAVME) $AX + B \vert X \vert = F$. This condition is superior to that of Kumar et al. [J. Numer. Anal. Approx. Theory, 51(1) (2022) 83-87]. We also discussed different conditions for the unique solvability of the new generalized absolute value matrix equation (NGAVME) $AX+B\vert CX \vert=F$ with $A, B, C, F, X \in \mathcal{R}^{n \times n}$. We also provided the corrected version of Corollary 2.1 from the published work by Wang et al. [Appl. Math. Lett. , 116 (2021) 106966].
LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition

Long-tailed multi-label visual recognition (LTML) task is a highly challenging task due to the label co-occurrence and imbalanced data distribution. In this work, we propose a unified framework for LTML, namely prompt tuning with class-specific embedding loss (LMPT), capturing the semantic feature interactions between categories by combining text and image modality data and improving the performance synchronously on both head and tail classes. Specifically, LMPT introduces the embedding loss function with class-aware soft margin and re-weighting to learn class-specific contexts with the benefit of textual descriptions (captions), which could help establish semantic relationships between classes, especially between the head and tail classes. Furthermore, taking into account the class imbalance, the distribution-balanced loss is adopted as the classification loss function to further improve the performance on the tail classes without compromising head classes. Extensive experiments are conducted on VOC-LT and COCO-LT datasets, which demonstrates that the proposed method significantly surpasses the previous state-of-the-art methods and zero-shot CLIP in LTML. Our codes are fully available at \url{https://github.com/richard-peng-xia/LMPT}.
A Sensor Fusion Approach for Improving Implementation Speed and Accuracy of RTAB-Map Algorithm Based Indoor 3D Mapping

Due to its numerous successful applications in industries like robotics, and autonomous navigation, 3D mapping for indoor environments has undergone much research and development. The complexity of the environment, a real-time embedding issue, and positioning mistakes of the robot system affect the creation of an accurate 3D map of indoor. Our research proposes a method to improve the 3D map construction performance by fusing data from the Ultrasonic-based Indoor Positioning System (IPS), the Inertial Measurement System (IMU) of the Intel Realsense D435i camera, and the encoder of the robot's wheel using the extended Kalman filter (EKF) algorithm. A Real-time Image Based Mapping algorithm (RTAB-Map) is used to handle the combined data, with the processing frequency updated in time with the IPS device's position frequency. The results indicate that combining sensors data considerably increases the speed, accuracy, and quality of the 3D mapping process. Our research demonstrates the potential of the integration of diverse data sources may be a useful tool for producing high-standard 3D indoor maps.
Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport

Answering complex queries on knowledge graphs is important but particularly challenging because of the data incompleteness. Query embedding methods address this issue by learning-based models and simulating logical reasoning with set operators. Previous works focus on specific forms of embeddings, but scoring functions between embeddings are underexplored. In contrast to existing scoring functions motivated by local comparison or global transport, this work investigates the local and global trade-off with unbalanced optimal transport theory. Specifically, we embed sets as bounded measures in $\real$ endowed with a scoring function motivated by the Wasserstein-Fisher-Rao metric. Such a design also facilitates closed-form set operators in the embedding space. Moreover, we introduce a convolution-based algorithm for linear time computation and a block-diagonal kernel to enforce the trade-off. Results show that WFRE can outperform existing query embedding methods on standard datasets, evaluation sets with combinatorially complex queries, and hierarchical knowledge graphs. Ablation study shows that finding a better local and global trade-off is essential for performance improvement.
Surface-based shape classification using Wasserstein distance

Surface based shape analysis plays a fundamental role in computer vision and medical imaging. In this work, we proposes a novel method for shape classiﬁcation of brain’s hippocampus us-ing Wasserstein distance based on optimal mass transport theory. In comparison with the conventional method based on Monge-Kantorovich theory, our proposed method employs Monge-Brenier theory for the computation of the optimal mass transport map, which remarkably ameliorates the eﬃciency by reducing computational complexity from O ( n 2 ) to O ( n ). Using the conformal mapping, our method maps the metric surface with disk topology to the unit planar disk, which pushes the area element on the surface to the disk and incurs the area distortion. A probability measure is then determined by this area distortion. Given any two probability measures on two surfaces, our method is capable of obtaining a unique optimal mass transport map between them. The transportation cost of this optimal mass transport deﬁnes the Wasser-stein distance between two surfaces, which intrinsically measures the dissimilarities between surface based shapes and thus can be used for shape classiﬁcation. Experimental results on surface based hippocampal shape analysis demonstrates the eﬃciency and eﬃ-cacy of our proposed method.
Revisiting the Dragonfly galaxy I. High-resolution ALMA and VLA observations of the radio hotspots in a hyper-luminous infrared galaxy at z = 1.92

Radio-loud active galactic nuclei (RLAGNs) are rare among AGN populations. Lacking high-resolution and high-frequency observations, their structure and evolution stages are not well understood at high redshifts. In this work, we report ALMA 237 GHz continuum observation at 0${_{. }^{\prime\prime}}$23 resolution and VLA 44 GHz continuum observation at 0${_{. }^{\prime\prime}}$8 resolution of the radio continuum emission from a high-redshift radio and hyper-luminous infrared galaxy at z = 1.92. The new observations confirm the South-East (SE) and North-West (NW) hotspots identified by previous low-resolution VLA observations at 4.7 and 8.2 GHz and identify a radio core undetected in all previous observations. The SE hotspot has a higher flux density than the NW one does by a factor of 6, suggesting that there can be a Doppler boosting effect in the SE one. In this scenario, we estimate the advance speed of the jet head, ranging from ∼0.1c–0.3c, which yields a mildly relativistic case. The projected linear distance between the two hotspots is ∼13 kpc, yielding a linear size (≤20 kpc) of a Compact-Steep-Spectrum (CSS) source. Combined with new high-frequency (νobs ≥ 44 GHz) and archived low-frequency observations (νobs ≤ 8.2 GHz), we find that injection spectra of both NW and SE hotspots can be fitted with a continuous injection (CI) model. Based on the CI model, the synchrotron ages of NW and SE hotspots have an order of 105 yr, consistent with the order of magnitude 103–105 yr observed in CSS sources associated with radio AGNs at an early evolution stage. The CI model also favors the scenario in which the double hotspots have experienced a quiescent phase, suggesting that this RLAGN may have transient or intermittent activities.
Are Synonym Substitution Attacks Really Synonym Substitution Attacks?

In this paper, we explore the following question: Are synonym substitution attacks really synonym substitution attacks (SSAs)? We approach this question by examining how SSAs replace words in the original sentence and show that there are still unresolved obstacles that make current SSAs generate invalid adversarial samples. We reveal that four widely used word substitution methods generate a large fraction of invalid substitution words that are ungrammatical or do not preserve the original sentence's semantics. Next, we show that the semantic and grammatical constraints used in SSAs for detecting invalid word replacements are highly insufficient in detecting invalid adversarial samples.
Superoscillating Quantum Control Induced By Sequential Selections

Superoscillation is a counterintuitive phenomenon for its mathematical feature of"faster-than-Fourier", which has allowed novel optical imaging beyond the diffraction limit. Here, we provide a superoscillating quantum control protocol realized by sequential selections in the framework of weak measurement, which drives the apparatus (target) by repeatedly applying optimal pre- and post-selections to the system (controller). Our protocol accelerates the adiabatic transport of trapped ions and adiabatic quantum search algorithm at a finite energy cost. We demonstrate the accuracy and robustness of the protocol in the presence of decoherence and fluctuating noise and elucidate the trade-off between fidelity and rounds of selections. Our findings provide avenues for quantum state control and wave-packet manipulation using superoscillation in quantum platforms such as trapped ions.
Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels

Frame semantic parsing is an important component of task-oriented dialogue systems. Current models rely on a significant amount training data to successfully identify the intent and slots in the user’s input utterance. This creates a significant barrier for adding new domains to virtual assistant capabilities, as creation of this data requires highly specialized NLP expertise. In this work we propose OpenFSP, a framework that allows for easy creation of new domains from a handful of simple labels that can be generated without specific NLP knowledge. Our approach relies on creating a small, but expressive, set of domain agnostic slot types that enables easy annotation of new domains. Given such annotation, a matching algorithm relying on sentence encoders predicts the intent and slots for domains defined by end-users. Experiments on the TopV2 dataset shows that our model trained on these simple labels have strong performance against supervised baselines.
How mature are survival data at the time of an interim analysis in a clinical trial with a survival outcome?

In a clinical trial with a survival outcome, an interim analysis is often performed to allow for early stopping for efficacy. If the interim analysis is early in the trial, one might conclude that a new treatment is more effective (compared to e.g.\ a placebo) and stop the trial, whereas the survival curves in the trial arms are not mature for the research question under investigation, for example because the curves are still close to 1 at that time. This means that the decision is based on a small percentage of the events in the long run only; possibly the events of the more frail patients in the trial who may not be representative for the whole group of patients. It may not be sensible to conclude effectiveness based on so little information. Criteria to determine the moment the interim analysis will be performed, should be chosen with care, and include the maturity of the data at the time of the interim analysis. Here, the expected survival rates at the interim analysis play a role. In this paper we will derive the asymptotic distribution of the Kaplan-Meier curves at the (random) moment the interim analysis will be performed for a one and two arm clinical trial. Based on this distribution, an interval in which the Kaplan Meier curves will fall into (with probability 95\%) is derived and could be used to plan the moment of the interim analysis in the design stage of the trial, so before the trial starts.
Symmetric Ternary Logic and Its Systematic Logic Composition Methodology

Ternary logic is expected to increase the area efficiency of VLSI due to its expressiveness compared to the traditional binary logic. This paper proposes a new symmetric ternary logic and a systematic logic composition methodology that enables us to design any ternary logic circuits. The methodology is demonstrated by implementing the ternary inverters, ternary NAND, ternary NOR, and ternary half-adder operators with the proposed symmetric ternary operators.
A Reduced Basis LES turbulence model based upon Kolmogorov's equilibrium turbulence theory

In this work, we introduce an a posteriori error indicator for the reduced basis modelling of turbulent flows. It is based upon the $k^{-5/3}$ Kolmogorov turbulence theory, thus it may be applied to any numerical discretisation of LES turbulence models. The main idea of this indicator is that if the full-order solution and the Reduced Order solution are close enough, then their flow energy spectrum within the inertial range should also be close. We present some numerical tests which supports that the use of this indicator is helpful, obtaining large computational speed-ups. We use as full-order model a Finite Element discretisation of the unsteady LES Smagorinsky turbulence model.
Adaptive Learning Path Navigation Based on Knowledge Tracing and Reinforcement Learning

This paper introduces the Adaptive Learning Path Navigation (ALPN) system, a novel approach for enhancing E-learning platforms by providing highly adaptive learning paths for students. The ALPN system integrates the Attentive Knowledge Tracing (AKT) model, which assesses students' knowledge states, with the proposed Entropy-enhanced Proximal Policy Optimization (EPPO) algorithm. This new algorithm optimizes the recommendation of learning materials. By harmonizing these models, the ALPN system tailors the learning path to students' needs, significantly increasing learning effectiveness. Experimental results demonstrate that the ALPN system outperforms previous research by 8.2% in maximizing learning outcomes and provides a 10.5% higher diversity in generating learning paths. The proposed system marks a significant advancement in adaptive E-learning, potentially transforming the educational landscape in the digital era.
Q&A Label Learning

Abstract Assigning labels to instances is crucial for supervised machine learning. In this letter, we propose a novel annotation method, Q&A labeling, which involves a question generator that asks questions about the labels of the instances to be assigned and an annotator that answers the questions and assigns the corresponding labels to the instances. We derived a generative model of labels assigned according to two Q&A labeling procedures that differ in the way questions are asked and answered. We showed that in both procedures, the derived model is partially consistent with that assumed in previous studies. The main distinction of this study from previous ones lies in the fact that the label generative model was not assumed but, rather, derived based on the definition of a specific annotation method, Q&A labeling. We also derived a loss function to evaluate the classification risk of ordinary supervised machine learning using instances assigned Q&A labels and evaluated the upper bound of the classification error. The results indicate statistical consistency in learning with Q&A labels.
Improving 2D face recognition via fine-level facial depth generation and RGB-D complementary feature learning

Face recognition in complex scenes suffers severe challenges coming from perturbations such as pose deformation, ill illumination, partial occlusion. Some methods utilize depth estimation to obtain depth corresponding to RGB to improve the accuracy of face recognition. However, the depth generated by them suffer from image blur, which introduces noise in subsequent RGB-D face recognition tasks. In addition, existing RGB-D face recognition methods are unable to fully extract complementary features. In this paper, we propose a fine-grained facial depth generation network and an improved multimodal complementary feature learning network. Extensive experiments on the Lock3DFace dataset and the IIIT-D dataset show that the proposed FFDGNet and I MCFLNet can improve the accuracy of RGB-D face recognition while achieving the state-of-the-art performance.
Value Reflection: Motivation, Examples and The Role of Technology

Findings from existing research provide possible evidence that values are an important construct that should be part of the self-reflection process. However, many questions about value reflection remain unexplored. As such, this position paper aims to provide an overview of relevant research and frameworks, example studies on value reflection pursued by the authors, and design aspects that might both improve value reflection processes and provide a brief mapping of where and how technology could be of help.
Towards Accurate Human Motion Prediction via Iterative Refinement

Human motion prediction aims to forecast an upcoming pose sequence given a past human motion trajectory. To address the problem, in this work we propose FreqMRN, a human motion prediction framework that takes into account both the kinematic structure of the human body and the temporal smoothness nature of motion. Specifically, FreqMRN first generates a fixed-size motion history summary using a motion attention module, which helps avoid inaccurate motion predictions due to excessively long motion inputs. Then, supervised by the proposed spatial-temporal-aware, velocity-aware and global-smoothness-aware losses, FreqMRN iteratively refines the predicted motion though the proposed motion refinement module, which converts motion representations back and forth between pose space and frequency space. We evaluate FreqMRN on several standard benchmark datasets, including Human3.6M, AMASS and 3DPW. Experimental results demonstrate that FreqMRN outperforms previous methods by large margins for both short-term and long-term predictions, while demonstrating superior robustness.