Miracle-dz/UniFAR-FTUs huggingface dataset:
MTrans: Multi-Modal Transformer for Accelerated MR Imaging

Accelerating multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply introduce the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing two modalities. Further, they usually rely on the convolutional neural networks (CNNs), which focus on local information and prevent them from fully capturing the long-distance dependencies of global knowledge. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. By restructuring the transformer architecture, our MTrans gains a powerful ability to capture deep multi-modal information. More specifically, the target modality and the auxiliary modality are first split into two branches and then fused using a multi-modal transformer module. This module is based on an improved multi-head attention mechanism, named the cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides two appealing benefits: (i) MTrans is the first attempt at using improved transformers for multi-modal MR imaging, affording more global information compared with CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each branch at different scales. It affords both distinct structural information and subtle pixel-level information, which supplement the target modality effectively. We evaluate MTrans with different accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets.
Learning stochastic object models from medical imaging measurements by use of advanced AmbientGANs

—In order to objectively assess new medical imaging technologies via computer-simulations, it is important to account for all sources of variability that contribute to image data. One important source of variability that can signiﬁcantly limit observer performance is associated with the variability in the ensemble of objects to-be-imaged. This source of variability can be described by stochastic object models (SOMs), which are generative models that can be employed to sample from a distribution of to-be-virtually-imaged objects. It is generally desirable to establish SOMs from experimental imaging measurements acquired by use of a well-characterized imaging system, but this task has remained challenging. Deep generative neural networks, such as generative adversarial networks (GANs) hold potential for such tasks. To establish SOMs from imaging measurements, an AmbientGAN has been proposed that augments a GAN with a measurement operator. However, the original AmbientGAN could not immediately beneﬁt from modern training procedures and GAN architectures, which limited its ability to be applied to realistically sized medical image data. To circumvent this, in this work, a modiﬁed AmbientGAN training strategy is proposed that is suitable for modern progressive or multi-resolution training approaches such as employed in the Progressive Growing of GANs and Style-based GANs. AmbientGANs established by use of the proposed training procedure are systematically validated in a controlled way by use of computer-simulated measurement data corresponding to a stylized imaging system. Finally, emulated single-coil experimental magnetic resonance imaging data are employed to demonstrate the methods under less stylized conditions.
Nanoscale thermal conductivity of Kapton-derived carbonaceous materials

* Corresponding authors: kirill.kondratenko@iemn.fr; stephane.lenfant@iemn.fr _________________________________________________________________________ Abstract This study explores the potentialities of Scanning Thermal Microscopy (SThM) technique as a tool for measuring thermal transporting properties of carbon-derived materials issued from thermal conversion of organic polymers, such as the most commonly known polyimide (PI), Kapton®. For quantitative measurements, the Null Point SThM (NPSThM) technique is used in order to avoid unwanted effects as the parasitic heat flows through the air and the probe cantilever. Kapton® HN films were pyrolysed in an inert atmosphere at temperatures up to 1200°C to produce carbon-based residues with varying degree of conversion to free sp2 disordered carbon. The thermal conductivity of carbon materials ranges from 0.2 to 2 Wm-1K-1 depending on the temperature of the carbonization process (varied between 500°C and 1200°C). In order to validate the applicability of NPSThM approach to these materials, the results were compared to those obtained with the three more traditional techniques, namely photo-thermal radiometry, flash laser analysis and micro-Raman thermometry. It was found that NP SThM data are in excellent agreement with previous work using more traditional techniques. We used the NP-SThM technique to differentiate structural heterogeneities or imperfections at the surface of the pyrolysed Kapton on the basis of measured local thermal conductivity.
Nonuniform Defocus Removal for Image Classification

We propose and study the single-frame anisoplanatic deconvolution problem associated with image classification using machine learning algorithms, named the nonuniform defocus removal (NDR) problem. Mathematical analysis of the NDR problem is done and the so-called defocus removal (DR) algorithm for solving it is proposed. Global convergence of the DR algorithm is established without imposing any unverifiable assumption. Numerical results on simulation data show significant features of DR including solvability, noise robustness, convergence, model insensitivity and computational efficiency. Physical relevance of the NDR problem and practicability of the DR algorithm are tested on experimental data. Back to the application that originally motivated the investigation of the NDR problem, we show that the DR algorithm can improve the accuracy of classifying distorted images using convolutional neural networks. The key difference of this paper compared to most existing works on single-frame anisoplanatic deconvolution is that the new method does not require the data image to be decomposable into isoplanatic subregions. Therefore, solution approaches partitioning the image into isoplanatic zones are not applicable to the NDR problem and those handling the entire image such as the DR algorithm need to be developed and analyzed.
Improving Grasp Planning Efficiency with Human Grasp Tendencies

—After a grasp has been planned, if the object orientation changes, the initial grasp may but not always have to be modified to accommodate the orientation change. For example, rotation of a cylinder by any amount around its centerline does not change its geometric shape relative to the grasper. Objects that can be approximated to solids of revolution or contain other geometric symmetries are prevalent in everyday life, and this information can be employed to improve the efficiency of existing grasp planning models. This paper experimentally investigates change in human-planned grasps under varied object orientations. With 13,440 recorded human grasps, our results indicate that during pick-and-place task of ordinary objects, stable grasps can be achieved with a small subset of grasp types, and the wrist-related parameters follow normal distribution. Furthermore, we show this knowledge can allow faster convergence of grasp planning algorithm.
Disconnectivity-Aware Energy-Efficient Cargo-UAV Trajectory Planning with Minimum Handoffs

On-board battery consumption, cellular disconnectivity, and frequent handoff are key challenges for unmanned aerial vehicle (UAV) based delivery missions, a.k. a. , cargo-UAV. Indeed, with the introduction of UAV technology into cargo shipping and logistics, designing energy-efficient paths becomes a serious issue for the next retail industry transformation. Typically, the latter has to guarantee uninterrupted or slightly interrupted cellular connectivity for the UAV’s command and control through a small number of handoffs. In this paper, we formulate the trajectory planning as a multi-objective problem aiming to minimize both the UAV’s energy consumption and the handoff rate, constrained by the UAV battery size and disconnectivity rate. Due to the problem’s complexity, we propose a dynamic programming based solution. Through simulations, we demonstrate the efficiency of our approach in providing opti-mized UAV trajectories. Also, the impact of several parameters, such as the cargo-UAV altitude, disconnectivity rate, and type of environment, are investigated. The obtained results allow to draw recommendations and guidelines for cargo-UAV operations.
Listen As You Wish: Audio based Event Detection via Text-to-Audio Grounding in Smart Cities

With the development of internet of things technologies, tremendous sensor audio data has been produced, which poses great challenges to audio-based event detection in smart cities. In this paper, we target a challenging audio-based event detection task, namely, text-to-audio grounding. In addition to precisely localizing all of the desired on- and off-sets in the untrimmed audio, this challenging new task requires extensive acoustic and linguistic comprehension as well as the reasoning for the crossmodal matching relations between the audio and query. The current approaches often treat the query as an entire one through a global query representation in order to address those issues. We contend that this strategy has several drawbacks. Firstly, the interactions between the query and the audio are not fully utilized. Secondly, it has not distinguished the importance of different keywords in a query. In addition, since the audio clips are of arbitrary lengths, there exist many segments which are irrelevant to the query but have not been filtered out in the approach. This further hinders the effective grounding of desired segments. Motivated by the above concerns, a novel Cross-modal Graph Interaction (CGI) model is proposed to comprehensively model the relations between the words in a query through a novel language graph. To capture the fine-grained relevances between the audio and query, a cross-modal attention module is introduced to generate snippet-specific query representations and automatically assign higher weights to keywords with more important semantics. Furthermore, we develop a cross-gating module for the audio and query to weaken irrelevant parts and emphasize the important ones.
How many moments does MMD compare?

We present a new way of study of Mercer kernels, by corresponding to a special kernel a pseudo-differential operator ? (x, ) such that F ? (x, )†? (x, )F −1 acts on smooth functions in the same way as an integral operator associated with (where F is the Fourier transform). We show that kernels defined by pseudo-differential operators are able to approximate uniformly any continuous Mercer kernel on a compact set. The symbol ? (x, y) encapsulates a lot of useful information about the structure of the Maximum Mean Discrepancy distance defined by the kernel . We approximate ? (x,y) with the sum of the first A terms of the Singular Value Decomposition of ? , denoted by ? A (x, y). If ordered singular values of the integral operator associated with ? (x,y) die down rapidly, the MMD distance defined by the new symbol ? A differs from the initial one only slightly. Moreover, the new MMD distance can be interpreted as an aggregated result of comparing A local moments of two probability distributions. The latter results holds under the condition that right singular vectors of the integral operator associated with ? are uniformly bounded. But even if this is not satisfied we can still hold that the Hilbert-Schmidt distance between ? and ? A vanishes. Thus, we report an interesting phenomenon: the MMD distance measures the difference of two probability distributions with respect to a certain number of local moments, A∗, and this number A∗ depends on the speed with which singular values of ? die down.
2‐Arylimino(diferrocenyl)‐ and (di‐p‐anisyl)dihydropyrimidines: Novel Synthesis, Structures, and Electrochemistry

2,3-Differocenyl- and 2,3-dianisyl-1-methylsulfanylcyclopropenilium iodides react with 1,3-diphenyl- and 1,3-di-o-tolylguanidine to give 1-aryl-2-arylimino-5,6- (5a,5b) and -4,5-diferrocenyl-1,2-dihydropyrimidines (6a,6b) (∼ 2:1) and, respectively, 5,6- and 4,5-dianisyl-3-phenyl-2-phenylimino-1,2-dihydropyrimidines (∼ 2:1). Their structures were established based on the spectroscopic data and X-ray diffraction analysis of 5,6-diferrocenyl-1-(o-tolyl)-2-(o-tolyl)imino- and 4,5-diferrocenyl-1-phenyl-2-phenylimino-1,2-dihydropyrimidines (5b and 6a, respectively). Electrochemical behavior of compounds 5b, 6b, and 5a+6a were investigated using experiments of cyclic voltammetry and chronoamperometry. For all the compounds, two electrochemical processes (I, II), attributed to the oxidations of the ferrocenes moieties were observed. The values of ΔE0′ (II-I) and comproportionation constant Kcom are also reported. Additionally, an electrochemical oxidation with a fast coupled chemical reaction related to the pyrimide ring was also detected.
Quantum Dynamics Interpretation of Black-box Optimization

—In recent decades, with the emergence of numerous novel intelligent optimization algorithms, many optimization researchers have begun to look for a basic search mechanism for their schemes that provides a more essential explanation of their studies. This paper aims to study the basic mechanism of an algorithm for black-box optimization with quantum theory. To achieve this goal, the Schr¨odinger equation is employed to establish the relationship between the optimization problem and the quantum system, which makes it possible to study the dynamic search behaviors in the evolution process with quantum theory. Moreover, to explore the basic behavior of the optimization system, the optimization problem is assumed to be decomposed and approximated. Then, a multilevel approximation quantum dynamics model of the optimization algorithm is established, which provides a mathematical and physical framework for the analysis of the optimization algorithm. Correspondingly, the basic search behavior based on this model is derived, which is governed by quantum theory. Comparison experiments and analysis between different bare-bones algorithms conﬁrm the existence of the quantum mechanic based basic search mechanism of the algorithm on black-box optimization.
Semi-Supervised Deep Ensembles for Blind Image Quality Assessment

Ensemble methods are generally regarded to be better than a single model if the base learners are deemed to be"accurate"and"diverse. "Here we investigate a semi-supervised ensemble learning strategy to produce generalizable blind image quality assessment models. We train a multi-head convolutional network for quality prediction by maximizing the accuracy of the ensemble (as well as the base learners) on labeled data, and the disagreement (i.e., diversity) among them on unlabeled data, both implemented by the fidelity loss. We conduct extensive experiments to demonstrate the advantages of employing unlabeled data for BIQA, especially in model generalization and failure identification.
KMTNet Nearby Galaxy Survey. III. Deficient Hα Flux in the Extended Disks of Spiral Galaxies

We perform a deep, wide-field imaging survey of nearby galaxies using Hα and broadband filters to investigate the characteristics of star formation in galaxies. Motivated by the finding that star formation rates (SFRs) derived from Hα fluxes in dwarf galaxies are systematically lower than those inferred from far-ultraviolet (FUV) fluxes, we attempt to determine whether the same trend exists in the extended disks of two star-forming galaxies. We perform spatially resolved photometry using grid-shaped apertures to measure the FUV and Hα fluxes of star-forming regions. We also perform spectral energy distribution (SED) fittings using 11 photometric data points (FUV-to-MIR), including data from the literature, to estimate the local properties such as internal attenuation of individual star-forming clumps. Comparing SFRFUV and SFRHα , which are converted from the Hα and FUV fluxes corrected for the local properties, we find that SFRHα /SFRFUV tend to decrease as the SFR decreases. We evaluate possible causes of this discrepancy between the two SFRs by restricting parameters in the SED fitting and conclude that deficient Hα fluxes in the extended disks of galaxies are tightly correlated with recent starbursts. The strong and short starburst, which has been rapidly suppressed over the last 10 Myr, seems to induce a significant discrepancy between the SFRHα and SFRFUV. In addition, the recent bursts in the extended disks of galaxies appear to have occurred azimuth-symmetrically, implying that these were likely triggered by gas accretion or internal processes rather than external perturbation.
Functional Classwise Principal Component Analysis: A Novel Classification Framework

In recent times, functional data analysis (FDA) has been successfully applied in the field of high dimensional data classification. In this paper, we present a novel classification framework using functional data and classwise Principal Component Analysis (PCA). Our proposed method can be used in high dimensional time series data which typically suffers from small sample size problem. Our method extracts a piece wise linear functional feature space and is particularly suitable for hard classification problems.The proposed framework converts time series data into functional data and uses classwise functional PCA for feature extraction followed by classification using a Bayesian linear classifier. We demonstrate the efficacy of our proposed method by applying it to both synthetic data sets and real time series data from diverse fields including but not limited to neuroscience, food science, medical sciences and chemometrics.
Center Smoothing: Provable Robustness for Functions with Metric-Space Outputs

Randomized smoothing has been successfully applied to classiﬁcation tasks on high-dimensional inputs, such as images, to obtain models that are provably robust against adversarial perturbations of the input. We extend this technique to produce provable robustness for functions that map inputs into an arbitrary metric space rather than discrete classes. Such functions are used in many machine learning problems like image reconstruction, dimensionality reduction, facial recognition, etc. Our robustness certiﬁcates guarantee that the change in the output of the smoothed model as measured by the distance metric remains small for any norm-bounded perturbation of the input. We can certify robustness under a variety of different output metrics, such as total variation distance, Jaccard distance, perceptual metrics, etc. In our experiments, we apply our procedure to create certiﬁably robust models with disparate output spaces – from sets to images – and show that it yields meaningful certiﬁcates without signiﬁcantly degrading the performance of the base model. The code for our experiments is available at: https://github.com/aounon/center-smoothing .
Mitigating Traffic Remapping Attacks in Autonomous Multihop Wireless Networks

Multihop wireless networks with autonomous nodes are susceptible to selfish traffic remapping attacks (TRAs). Nodes launching TRAs leverage the underlying channel access function to receive an unduly high Quality of Service (QoS) for packet flows traversing source-to-destination routes. TRAs are easy to execute, impossible to prevent, difficult to detect, and harmful to the QoS of honest nodes. Recognizing the need for providing QoS security, we use a novel network-oriented QoS metric to propose a self-enforcing game-theoretic mitigation approach. By switching between TRA and honest behavior, selfish nodes engage in a noncooperative multistage game in pursuit of high QoS. We analyze feasible node strategies and design a distributed signaling mechanism called DISTRESS, under which, given certain conditions, the game produces a desirable outcome: after an upper bounded play time, honesty tends to become a selfish node’s best reply behavior, while yielding acceptable QoS to most or all nodes. We verify these findings by Monte Carlo and ns-3 simulations of static and mobile nodes.
An evaluation of template and ML-based generation of user-readable text from a knowledge graph

Typical user-friendly renderings of knowledge graphs are visualisations and natural language text. Within the latter HCI solution approach, data-driven natural language generation systems receive increased attention, but they are often outperformed by template-based systems due to suffering from errors such as content dropping, hallucination, or repetition. It is unknown which of those errors are associated significantly with low quality judgements by humans who the text is aimed for, which hampers addressing errors based on their impact on improving human evaluations. We assessed their possible association with an experiment availing of expert and crowdsourced evaluations of human authored text, template generated text, and sequence-to-sequence model generated text. The results showed that there was no significant association between human authored texts with errors and the low human judgements of naturalness and quality. There was also no significant association between machine learning generated texts with dropped or hallucinated slots and the low human judgements of naturalness and quality. Thus, both approaches appear to be viable options for designing a natural language interface for knowledge graphs.
PYInfer: Deep Learning Semantic Type Inference for Python Variables

Python type inference is challenging in practice. Due to its dynamic properties and extensive dependencies on third-party libraries without type annotations, the performance of traditional static analysis techniques is limited. Although semantics in source code can help manifest intended usage for variables (thus help infer types), they are usually ignored by existing tools. In this paper, we propose PYInfer, an end-to-end learning-based type inference tool that automatically generates type annotations for Python variables. The key insight is that contextual code semantics is critical in inferring the type for a variable. For each use of a variable, we collect a few tokens within its contextual scope, and design a neural network to predict its type. One challenge is that it is difficult to collect a high-quality human-labeled training dataset for this purpose. To address this issue, we apply an existing static analyzer to generate the ground truth for variables in source code. Our main contribution is a novel approach to statically infer variable types effectively and efficiently. Formulating the type inference as a classification problem, we can handle user-defined types and predict type probabilities for each variable. Our model achieves 91.2% accuracy on classifying 11 basic types in Python and 81.2% accuracy on classifying 500 most common types. Our results substantially outperform the state-of-the-art type annotators. Moreover, PYInfer achieves 5.2X more code coverage and is 187X faster than a state-of-the-art learning-based tool. With similar time consumption, our model annotates 5X more variables than a state-of-the-art static analysis tool. Our model also outperforms a learning-based function-level annotator on annotating types for variables and function arguments. All our tools and datasets are publicly available to facilitate future research in this direction.