Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Parallel Imaging and Compressed Sensing MRI Reconstruction with GRAPPA Integrated Multi-loss Supervised GAN

Objective: Parallel imaging accelerates the acquisition of magnetic resonance imaging (MRI) data by acquiring additional sensitivity information with an array of receiver coils resulting in reduced phase encoding steps. Compressed sensing magnetic resonance imaging (CS-MRI) has achieved popularity in the ﬁeld of medical imaging because of its less data requirement than parallel imaging. Parallel imaging and compressed sensing (CS) both speed up traditional MRI acquisition by minimizing the amount of data captured in the k -space. As acquisition time is inversely proportional to the number of samples, the inverse formation of an image from reduced k -space samples leads to faster acquisition but with aliasing artifacts. This paper proposes a novel Generative Adversarial Network (GAN) namely RECGAN-GR supervised with multi-modal losses for de-aliasing the reconstructed image. Methods: In contrast to existing GAN networks, our proposed method introduces a novel generator network namely RemU-Net integrated with dual-domain loss functions including weighted magnitude and phase loss functions along with parallel imaging-based loss i.e., GRAPPA consistency loss. A k -space correction block is proposed as reﬁnement learning to make the GAN network self-resistant to generating unnecessary data which drives the convergence of the reconstruction process faster. Re-sults: Comprehensive results show that the proposed RECGAN-GR achieves a 4 dB improvement in the PSNR among the GAN-based methods and a 2 dB improvement among conventional state-of-the-art CNN methods available in the literature. Conclusion and signiﬁcance: The proposed work contributes to signiﬁcant improvement in the image quality for low retained data leading to 5 × or 10 × faster acquisition.
Thompson Sampling with Virtual Helping Agents

We address the problem of online sequential decision making, i.e., balancing the trade-off between exploiting the current knowledge to maximize immediate performance and exploring the new information to gain long-term benefits using the multi-armed bandit framework. Thompson sampling is one of the heuristics for choosing actions that address this exploration-exploitation dilemma. We first propose a general framework that helps heuristically tune the exploration versus exploitation trade-off in Thompson sampling using multiple samples from the posterior distribution. Utilizing this framework, we propose two algorithms for the multi-armed bandit problem and provide theoretical bounds on the cumulative regret. Next, we demonstrate the empirical improvement in the cumulative regret performance of the proposed algorithm over Thompson Sampling. We also show the effectiveness of the proposed algorithm on real-world datasets. Contrary to the existing methods, our framework provides a mechanism to vary the amount of exploration/ exploitation based on the task at hand. Towards this end, we extend our framework for two additional problems, i.e., best arm identification and time-sensitive learning in bandits and compare our algorithm with existing methods.
TANDEM3D: Active Tactile Exploration for 3D Object Recognition

Tactile recognition of 3D objects remains a challenging task. Compared to 2D shapes, the complex geometry of 3D surfaces requires richer tactile signals, more dexterous actions, and more advanced encoding techniques. In this work, we propose TANDEM3D, a method that applies a co-training framework for exploration and decision making to 3D object recognition with tactile signals. Starting with our previous work, which introduced a co-training paradigm for 2D recognition problems, we introduce a number of advances that enable us to scale up to 3D. TANDEM3D is based on a novel encoder that builds 3D object representation from contact positions and normals using PointNet++. Furthermore, by enabling 6DOF movement, TANDEM3D explores and collects discriminative touch information with high efficiency. Our method is trained entirely in simulation and validated with real-world experiments. Compared to state-of-the-art baselines, TANDEM3D achieves higher accuracy and a lower number of actions in recognizing 3D objects and is also shown to be more robust to different types and amounts of sensor noise.
A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection

Many challenging real-world problems require the deployment of ensembles multiple complementary learning models to reach acceptable performance levels. While effective, applying the entire ensemble to every sample is costly and often unnecessary. Deep Reinforcement Learning (DRL) offers a cost-effective alternative, where detectors are dynamically chosen based on the output of their predecessors, with their usefulness weighted against their computational cost. Despite their potential, DRL-based solutions are not widely used in this capacity, partly due to the difficulties in configuring the reward function for each new task, the unpredictable reactions of the DRL agent to changes in the data, and the inability to use common performance metrics (e.g., TPR/FPR) to guide the algorithm's performance. In this study we propose methods for fine-tuning and calibrating DRL-based policies so that they can meet multiple performance goals. Moreover, we present a method for transferring effective security policies from one dataset to another. Finally, we demonstrate that our approach is highly robust against adversarial attacks.
Dynamic Walking of Bipedal Robots on Uneven Stepping Stones via Adaptive-Frequency MPC

This letter presents a novel Adaptive-frequency MPC framework for bipedal locomotion over terrain with uneven stepping stones. In detail, we intend to achieve adaptive gait periods with variable MPC frequency for bipedal periodic walking gait to traverse terrain with discontinuities without slowing down. We pair this adaptive-frequency MPC with kino-dynamics trajectory optimization to obtain MPC adaptive frequencies (in terms of sampling times), center of mass (CoM) trajectory, and foot placements. We use whole-body control (WBC) along with adaptive-frequency MPC to track the optimal trajectories from offline optimization. In numerical validations, our adaptive-frequency optimization and MPC framework have shown advantages over fixed-frequency MPC. The proposed framework can control the bipedal robot to traverse through uneven stepping stone terrains with perturbed stone heights, widths, and surface shapes while maintaining an average speed of 1.5 m/s.
Comparative Study of Q-Learning and NeuroEvolution of Augmenting Topologies for Self Driving Agents

Autonomous driving vehicles have been of keen interest ever since automation of various tasks started. Humans are prone to exhaustion and have a slow response time on the road, and on top of that driving is already quite a dangerous task with around 1.35 million road traffic incident deaths each year [1]. It is expected that autonomous driving can reduce the number of driving accidents around the world which is why this problem has been of keen interest for researchers. Currently, self-driving vehicles use different algorithms for various sub-problems in making the vehicle autonomous. We will focus reinforcement learning algorithms, more specifically Q-learning algorithms and NeuroEvolution of Augment Topologies (NEAT), a combination of evolutionary algorithms and artificial neural networks, to train a model agent to learn how to drive on a given path. This paper will focus on drawing a comparison between the two aforementioned algorithms.
On the number of antichains of sets in a finite universe

Properties of intervals in the lattice of antichains of subsets of a universe of finite size are investigated. New objects and quantities in this lattice are defined. Expressions and numerical values are deduced for the number of connected antichains and the number of fully distinguishing antichains. The latter establish a connection with Stirling numbers of the second kind. Decomposition properties of intervals in the lattice of antichains are proven. A new operator allowing partitioning the full lattice in intervals derived from lower dimensional sub-lattices is introduced. Special posets underlying an interval of antichains are defined. The poset allows the derivation of a powerful formula for the size of an interval. This formula allows computing intervals in the six dimensional space. Combinatorial coefficients allowing another decomposition of the full lattice are defined. In some specific cases, related to connected components in graphs, these coefficients can be efficiently computed. This formula allows computing the size of the lattice of order 8 efficiently. This size is the number of Dedekind of order 8, the largest one known so far.
KNOT: Knowledge Distillation Using Optimal Transport for Solving NLP Tasks

We propose a new approach, Knowledge Distillation using Optimal Transport (KNOT), to distill the natural language semantic knowledge from multiple teacher networks to a student network. KNOT aims to train a (global) student model by learning to minimize the optimal transport cost of its assigned probability distribution over the labels to the weighted sum of probabilities predicted by the (local) teacher models, under the constraints that the student model does not have access to teacher models’ parameters or training data. To evaluate the quality of knowledge transfer, we introduce a new metric, Semantic Distance (SD), that measures semantic closeness between the predicted and ground truth label distributions. The proposed method shows improvements in the global model’s SD performance over the baseline across three NLP tasks while performing on par with Entropy-based distillation on standard accuracy and F1 metrics. The implementation pertaining to this work is publicly available at https://github.com/declare-lab/KNOT.
Hand and Arm Gesture-based Human-Robot Interaction: A Review

The study of Human-Robot Interaction (HRI) aims to create close and friendly communication between humans and robots. In the human-center HRI, an essential aspect of implementing a successful and effective HRI is building a natural and intuitive interaction, including verbal and nonverbal. As a prevalent nonverbally communication approach, hand and arm gesture communication happen ubiquitously in our daily life. A considerable amount of work on gesture-based HRI is scattered in various research domains. However, a systematic understanding of the works on gesture-based HRI is still lacking. This paper intends to provide a comprehensive review of gesture-based HRI and focus on the advanced finding in this area. Following the stimulus-organism-response framework, this review consists of: (i) Generation of human gesture(stimulus). (ii) Robot recognition of human gesture(organism). (iii) Robot reaction to human gesture(response). Besides, this review summarizes the research status of each element in the framework and analyze the advantages and disadvantages of related works. Toward the last part, this paper discusses the current research challenges on gesture-based HRI and provide possible future directions.
Dormant opers and Gauss maps in positive characteristic

The Gauss map of a given projective variety is the rational map that sends a smooth point to the tangent space at that point, considered as a point of the Grassmann variety. The present paper aims to generalize a result by H. Kaji on Gauss maps in positive characteristic and establish an interaction with the study of dormant opers, as well as Frobenius-projective structures. We ﬁrst prove a correspondence between dormant opers on a smooth projective variety X and closed immersions from X into a projective space with purely inseparable Gauss map. By using this, we determine the subﬁelds of the function ﬁeld of a smooth curve in positive characteristic induced by Gauss maps. Moreover, the correspondence gives us a Frobenius-projective structure on a Fermat hypersurface. This example embodies an exotic phenomenon of algebraic geometry in positive characteristic.
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs

Networks are ubiquitous in many real-world applications (e.g., social networks encoding trust/distrust relationships, correlation networks arising from time series data). While many networks are signed or directed, or both, there is a lack of unified software packages on graph neural networks (GNNs) specially designed for signed and directed networks. In this paper, we present PyTorch Geometric Signed Directed (PyGSD), a software package which fills this gap. Along the way, we evaluate the implemented methods with experiments with a view to providing insights into which method to choose for a given task. The deep learning framework consists of easy-to-use GNN models, synthetic and real-world data, as well as task-specific evaluation metrics and loss functions for signed and directed networks. As an extension library for PyG, our proposed software is maintained with open-source releases, detailed documentation, continuous integration, unit tests and code coverage checks. The GitHub repository of the library is https://github.com/SherylHYX/pytorch_geometric_signed_directed.
Efficient Information Retrieval for Sensing via Continuous Measurement

Continuous monitoring of driven-dissipative quantum optical systems is a crucial element in the implementation of quantum metrology, providing essential strategies for achieving highly precise measurements beyond the classical limit. In this context, the relevant figure of merit is the quantum Fisher information of the radiation field emitted by the driven-dissipative sensor. Saturation of the corresponding precision limit as defined by the quantum Cramer-Rao bound is typically not achieved by conventional, temporally local continuous measurement schemes such as counting or homodyning. To address the outstanding open challenge of efficient retrieval of the quantum Fisher information of the emission field, we design a novel continuous measurement strategy featuring temporally quasilocal measurement bases as captured by matrix product states. Such measurement can be implemented effectively by injecting the emission field of the sensor into an auxiliary open system, a `quantum decoder' module, which `decodes' specific input matrix product states into simple product states as its output field, and performing conventional continuous measurement at the output. We devise a universal recipe for the construction of the decoder by exploiting time reversal transformation of quantum optical input-output channels, thereby establishing a universal method to achieve the quantum Cramer-Rao precision limit for generic sensors based on continuous measurement. As a by-product, we establish an effective formula for the evaluation of the quantum Fisher information of the emission field of generic driven-dissipative open sensors. We illustrate the power of our scheme with paramagnetic open sensor designs including linear force sensors, fibre-interfaced nonlinear emitters, and driven-dissipative many-body sensors, and demonstrate that it can be robustly implemented under realistic experimental imperfections.
A Masked Bounding-Box Selection Based ResNet Predictor for Text Rotation Prediction

The existing Optical Character Recognition (OCR) systems are capable of recognizing images with horizontal texts. However, when the rotation of the texts increases, it becomes harder to recognizing these texts. The performance of the OCR systems decreases. Thus predicting the rotations of the texts and correcting the images are important. Previous work mainly uses traditional Computer Vision methods like Hough Transform and Deep Learning methods like Convolutional Neural Network. However, all of these methods are prone to background noises commonly existing in general images with texts. To tackle this problem, in this work, we introduce a new masked bounding-box selection method, that incorporating the bounding box information into the system. By training a ResNet predictor to focus on the bounding box as the region of interest (ROI), the predictor learns to overlook the background noises. Evaluations on the text rotation prediction tasks show that our method improves the performance by a large margin.
The 3d Transition Metal Single Atom Catalysis for Prominent Adsorption Performance Towards Cr(VI) : A First Principle Study

The reduction reaction of turning Cr(VI) into Cr(III) by appropriate oxidants together with nanoscale catalysts in contaminated soil and water received significant amount of attention for its environmental benefits. Single-atom catalysts (SACs), as an emergent new type of catalysts, show great potential for simultaneous adsorption for the Cr(VI) removal. In this work, we investigated the efficiency of 3 d transitional metal (from Sc to Zn) SACs in adsorption for the Cr(VI) removal via a theoretical approach. DFT calculations were used to measure the structural and energetic of Cr(VI) (H 2 CrO 4 and Cr 2 O 72- ) adsorption behavior on the SACs systems. The Ti-N-C has a good performance for the H 2 CrO 4 and Cr 2 O 72- removal and has important environmental protection significance for water purification. In addition, our results show that the binding energy of the SACs systems for H 2 CrO 4 adsorption is found to be much less than that for Cr 2 O 72- adsorption, indicating that reduction of Cr(VI) in Cr 2 O 72- is energetically favorable than that of Cr(VI) in H 2 CrO 4.
Belief Revision Based Caption Re-ranker with Visual Semantic Information

In this work, we focus on improving the captions generated by image-caption generation systems. We propose a novel re-ranking approach that leverages visual-semantic measures to identify the ideal caption that maximally captures the visual information in the image. Our re-ranker utilizes the Belief Revision framework (Blok et al., 2003) to calibrate the original likelihood of the top-n captions by explicitly exploiting semantic relatedness between the depicted caption and the visual context. Our experiments demonstrate the utility of our approach, where we observe that our re-ranker can enhance the performance of a typical image-captioning system without necessity of any additional training or fine-tuning.
Towards Optimal Use of Surrogate Markers to Improve Power

Motivated by increasing pressure for decision makers to shorten the time required to evaluate the efficacy of a treatment such that treatments deemed safe and effective can be made publicly available, there has been substantial recent interest in using an earlier or easier to measure surrogate marker, $S$, in place of the primary outcome, $Y$. To validate the utility of a surrogate marker in these settings, a commonly advocated measure is the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker (PTE). Model based and model free estimators for PTE have also been developed. While this measure is very intuitive, it does not directly address the important questions of how $S$ can be used to make inference of the unavailable $Y$ in the next phase clinical trials. In this paper, to optimally use the information of surrogate S, we provide a framework for deriving an optimal transformation of $S$, $g_{opt}(S)$, such that the treatment effect on $g_{opt}(S)$ maximally approximates the treatment effect on $Y$ in a certain sense. Based on the optimally transformed surrogate, $g_{opt}(S)$, we propose a new measure to quantify surrogacy, the relative power (RP), and demonstrate how RP can be used to make decisions with $S$ instead of $Y$ for next phase trials. We propose nonparametric estimation procedures, derive asymptotic properties, and compare the RP measure with the PTE measure. Finite sample performance of our estimators is assessed via a simulation study. We illustrate our proposed procedures using an application to the Diabetes Prevention Program (DPP) clinical trial to evaluate the utility of hemoglobin A1c and fasting plasma glucose as surrogate markers for diabetes.
Genuine multipartite entanglement in 1D Bose–Hubbard model with frustrated hopping

Frustration and quantum entanglement are two exotic quantum properties in quantum many-body systems. However, despite several eﬀorts, an exact relation between them remains elusive. In this work, we explore the relationship between frustration and quantum entanglement in a physical model describing strongly correlated ultracold bosonic atoms in optical lattices. In particular, we consider the one-dimensional Bose–Hubbard model comprising both nearest-neighbor ( t 1 ) and frustrated next-nearest neighbor ( t 2 ) hoppings and examine how the interplay of onsite interaction ( U ) and hoppings results in diﬀerent quantum correlations dominating in the ground state of the system. We then analyze the behavior of quantum entanglement in the model. In particular, we compute genuine multipartite entanglement as quantiﬁed through the generalized geometric measure and make a comparative study with bipartite entanglement and other relevant order parameters. We observe that genuine multipartite entanglement has a very rich behavior throughout the considered parameter regime and frustration does not necessarily favor generating a high amount of it. Moreover, we show that in the region with strong quantum ﬂuctuations, the particles remain highly delocalized in all momentum modes and share a very low amount of both bipartite and multipartite entanglement. Our work illustrates the necessity to give separate attention to dominating ordering behavior and quantum entanglement in the ground state of strongly correlated systems.
Mobility Aware Optimization in the Metaverse

Metaverse applications that incorporate Mobile Augmented Reality (MAR) provide mixed and immersive experiences by amalgamating the virtual with the physical world. Notably, due to their multi-modality such applications are demanding in terms of energy consumption, computing and caching resources to efficiently support foreground interactions of participating users and rich background content. In this paper, the metaverse service is decomposed and anchored at suitable edge caching/computing nodes in 5G and beyond networks to enable efficient processing of background metaverse region models embedded with target AROs. To achieve that, a joint optimization problem is proposed, which explicitly considers the user physical mobility, service decomposition. It manages to achieve the balance between service delay capturing wireless, link and processing parts, power consumption capturing transmission and cpu power, and user perception quality measured by the difference from original images as Structural Similarity (SSIM). A wide set of numerical investigations reveal that, the proposed scheme could provide optimal decision making and outperform other nominal baseline schemes which are oblivious of user mobility as well as do not consider service decomposition.
Exploring the Effects of Multicast Communication on DDS Performance

The Data Distribution Service (DDS) is an Object Management Group (OMG) standard for high-performance and real-time systems. DDS is a data-centric middleware based on the publish-subscribe communication pattern and is used in many mission-critical, or even safety-critical, systems such as air traffic control and robot operating system (ROS2). This research aims at identifying how the usage of multicast affects the performance of DDS communication for varying numbers of participants (publishers and subscribers). The results show that DDS configured for multicast communication can exhibit worse performance under a high load (a greater number of participants) than DDS configured for unicast communication. This counter-intuitive result reinforces the need for researchers and practitioners to be clear about the details of how multicast communication operates on the network.