Miracle-dz/UniFAR-FTUs huggingface dataset:
DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features

Image Retrieval is a fundamental task of obtaining images similar to the query one from a database. A common image retrieval practice is to firstly retrieve candidate images via similarity search using global image features and then re-rank the candidates by leveraging their local features. Previous learning-based studies mainly focus on either global or local image representation learning to tackle the retrieval task. In this paper, we abandon the two-stage paradigm and seek to design an effective single-stage solution by integrating local and global information inside images into compact image representations. Specifically, we propose a Deep Orthogonal Local and Global (DOLG) information fusion framework for end-to-end image retrieval. It attentively extracts representative local information with multi-atrous convolutions and self-attention at first. Components orthogonal to the global image representation are then extracted from the local information. At last, the orthogonal components are concatenated with the global representation as a complementary, and then aggregation is performed to generate the final representation. The whole framework is end-to-end differentiable and can be trained with image-level labels. Extensive experimental results validate the effectiveness of our solution and show that our model achieves state-of-the-art image retrieval performances on Revisited Oxford and Paris datasets. 1
FloMore: Meeting bandwidth requirements of flows

Wide-area cloud provider networks must support the bandwidth requirements of diverse services (e.g., applications, product groups, customers) despite failures. Existing traffic engineering (TE) schemes operate at much coarser granularity than services, which we show necessitates unduly conservative decisions. To tackle this, we present FloMore, which directly considers the bandwidth needs of individual services and ensures they are met a desired percentage of time. Rather than meet the requirements for all services over the same set of failure states, FloMore exploits a key opportunity that each service could meet its bandwidth requirements over a different set of failure states. FloMore consists of an offline phase that identifies the critical failure states of each service, and on failure allocates traffic in a manner that prioritizes those services for which that failure state is critical. We present a novel decomposition scheme to handle FloMore's offline phase in a tractable manner. Our evaluations show that FloMore outperforms state-of-the-art TE schemes including SMORE and Teavar, and also out-performs extensions of these schemes that we devise. The results also show FloMore's decomposition approach allows it to scale well to larger network topologies.
Optimal Integrating Learning for Split Questionnaire Design Type Data

Abstract In the era of data science, it is common to encounter data with different subsets of variables obtained for different cases. An example is the split questionnaire design (SQD), which is adopted to reduce respondent fatigue and improve response rates by assigning different subsets of the questionnaire to different sampled respondents. A general question then is how to estimate the regression function based on such block-wise observed data. Currently, this is often carried out with the aid of missing data methods, which may unfortunately suffer from intensive computational cost, high variability, and possible large modeling biases in real applications. In this article, we develop a novel approach for estimating the regression function for SQD-type data. We first construct a list of candidate models using available data-blocks separately, and then combine the estimates properly to make an efficient use of all the information. We show the resulting averaged model is asymptotically optimal in the sense that the squared loss and risk are asymptotically equivalent to those of the best but infeasible averaged estimator. Both simulated examples and an application to the SQD dataset from the European Social Survey show the promise of the proposed method. Supplementary materials for this article are available online.
Gravitational-wave glitches: Resonant islands and frequency jumps in nonintegrable extreme-mass-ratio inspirals

The detection of gravitational waves from extreme-mass-ratio inspirals with upcoming space-borne detectors will allow for unprecedented tests of general relativity in the strong-field regime. Aside from assessing whether black holes are unequivocally described by the Kerr metric, such detections may place constraints on the degree of spacetime symmetry. In particular, depending on exactly how a hypothetical departure from the Kerr metric manifests, the Carter symmetry, which implies the integrability of the geodesic equations, may be broken. Here, we examine the gravitational waveforms associated with non-integrable extreme-mass-ratio inspirals involving a small-mass companion and a supermassive compact object of general relativity, namely the Manko-Novikov spacetime. We show that the waveforms displays sudden frequency jumps, when the companion transverses resonant islands. These findings demonstrate that such abrupt manifestations in the gravitational-wave frequencies are generic, have a genuine astrophysical origin and function as a distinctive signature of chaotic phenomena in extreme-mass-ratio binaries.
Sequen-C: A Multilevel Overview of Temporal Event Sequences

Building a visual overview of temporal event sequences with an optimal level-of-detail (i.e. simplified but informative) is an ongoing challenge - expecting the user to zoom into every important aspect of the overview can lead to missing insights. We propose a technique to build a multilevel overview of event sequences, whose granularity can be transformed across sequence clusters (vertical level-of-detail) or longitudinally (horizontal level-of-detail), using hierarchical aggregation and a novel cluster data representation Align-Score-Simplify. By default, the overview shows an optimal number of sequence clusters obtained through the average silhouette width metric – then users are able to explore alternative optimal sequence clusterings. The vertical level-of-detail of the overview changes along with the number of clusters, whilst the horizontal level-of-detail refers to the level of summarization applied to each cluster representation. The proposed technique has been implemented into a visualization system called Sequence Cluster Explorer (Sequen-C) that allows multilevel and detail-on-demand exploration through three coordinated views, and the inspection of data attributes at cluster, unique sequence, and individual sequence level. We present two case studies using real-world datasets in the healthcare domain: CUREd and MIMIC-III; which demonstrate how the technique can aid users to obtain a summary of common and deviating pathways, and explore data attributes for selected patterns.
RIS-Assisted UAV Communications for IoT With Wireless Power Transfer Using Deep Reinforcement Learning

Many of the devices used in Internet-of-Things (IoT) applications are energy-limited, and thus supplying energy while maintaining seamless connectivity for IoT devices is of considerable importance. In this context, we propose a simultaneous wireless power transfer and information transmission scheme for IoT devices with support from the reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communications. In particular, in the first phase, IoT devices harvest energy from the UAV through wireless power transfer; and then in the second phase, the UAV collects data from the IoT devices through information transmission. To characterise the agility of the UAV, we consider two scenarios: a hovering UAV and a mobile UAV. Aiming at maximising the total network sum-rate, we jointly optimise the trajectory and the power allocation of the UAV, the energy harvesting scheduling of IoT devices, and the phase-shift matrix of the RIS. We formulate a Markov decision process and propose two deep reinforcement learning algorithms to solve the optimisation problem of maximising the total network sum-rate. Numerical results illustrate the effectiveness of the UAV’s flying path optimisation and the network’s throughput of our proposed techniques compared with other benchmark schemes. Given the strict requirements of the RIS and UAV, the significant improvement in processing time and throughput performance demonstrates that our proposed scheme is well applicable for practical IoT applications.
Vision-Based Food Analysis for Automatic Dietary Assessment

Background: Maintaining a healthy diet is vital to avoid health-related issues, e.g., undernu-trition, obesity and many non-communicable diseases. An indispensable part of the health diet is dietary assessment. Traditional manual recording methods are burdensome and contain substantial biases and errors. Recent advances in Artiﬁcial Intelligence, especially computer vision technologies, have made it possible to develop automatic dietary assessment solutions, which are more convenient, less time-consuming and even more accurate to monitor daily food intake. Scopeandapproach: ThisreviewpresentsoneuniﬁedVision-BasedDietaryAssessment(VBDA) framework, which generally consists of three stages: food image analysis, volume estimation and nutrient derivation. Vision-based food analysis methods, including food recognition, detection and segmentation, are systematically summarized, and methods of volume estimation and nutrient derivation are also given. The prosperity of deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition. The recently proposed end-to-end methods are also discussed. We further analyze existing dietary assessment datasets, indicating that one large-scale benchmark is urgently needed, and ﬁnally highlight key challenges and future trends for VBDA. Key ﬁndings and conclusions: After thorough exploration, we ﬁnd that multi-task end-to-end deep learning approaches are one important trend of VBDA. Despite considerable research progress, many challenges remain for VBDA due to the meal complexity. We also provide the latest ideas for future development of VBDA, e.g., ﬁne-grained food analysis and accurate volume estimation. This survey aims to encourage researchers to propose more practical solutions for VBDA.
Adapting Segmentation Networks to New Domains by Disentangling Latent Representations

—Deep learning models achieve outstanding accuracy in semantic segmentation, however they require a huge amount of labeled data for their optimization. Hence, domain adaptation approaches have come into play to transfer knowledge acquired on a label-abundant source domain to a related label-scarce target domain. However, such models do not generalize well to data with statistical properties not perfectly matching the ones of the training samples. In this work, we design and carefully analyze multiple latent space-shaping regularization strategies that work in conjunction to reduce the domain discrepancy in semantic segmentation. In particular, we devise a feature clustering strategy to increase domain alignment, a feature perpendicularity constraint to space apart feature belonging to different semantic classes, including those not present in the current batch, and a feature norm alignment strategy to separate active and inactive channels. Additionally, we propose a novel performance metric to capture the relative efﬁcacy of an adaptation strategy compared to supervised training. We verify the effectiveness of our framework in synthetic-to-real and real-to-real adaptation scenarios, outperforming previous state-of-the-art methods on multiple road scenes benchmarks and using different backbones.
Low-Temperature Plasma for Biology, Hygiene, and Medicine: Perspective and Roadmap

Plasma, the fourth and most pervasive state of matter in the visible universe, is a fascinating medium that is connected to the beginning of our universe itself. Man-made plasmas are at the core of many technological advances that include the fabrication of semiconductor devices, which enabled the modern computer and communication revolutions. The introduction of low temperature, atmospheric pressure plasmas to the biomedical field has ushered a new revolution in the healthcare arena that promises to introduce plasma-based therapies to combat some thorny and long-standing medical challenges. This article presents an overview of where research is at today and discusses innovative concepts and approaches to overcome present challenges and take the field to the next level. It is written by a team of experts who took an in-depth look at the various applications of plasma in hygiene, decontamination, and medicine, made critical analysis, and proposed ideas and concepts that should help the research community focus their efforts on clear and practical steps necessary to keep the field advancing for decades to come.
Polyquercetin/MWNT‐modified Electrode for the Determination of Natural Phenolic Antioxidants

Novel highly sensitive voltammetric method for the gallic acid, catechin and epigallocatechin gallate (EGCG) quantification has been developed using glassy carbon electrode modified with multi-walled carbon nanotubes and polyquercetin (polyquercetin/MWNT/GCE). The effect of electropolymerization parameters (number of cycles, monomer concentration and polarization window) on the electrode characteristics has been evaluated. The electrode surface has been characterized with SEM, CV and EIS. The linear dynamic ranges of 0.50–10 and 10–750 μM for gallic acid, 0.10–10 and 10.0–250 μM for catechin and 0.050–10 and 10–100 μM for EGCG have been obtained in differential pulse mode with the detection limits of 0.10, 0.024 and 0.014 μM, respectively. The approach has been successfully applied for the tea antioxidant capacity evaluation (AOC). The positive correlations with antioxidant activity towards DPPH• and total phenolics (r=0.7845 and 0.7658 at rcrit=0.4227, n=22) have been obtained.
Strong magneto-optical effect and anomalous transport in the two-dimensional van der Waals magnets 
FenGeTe2
 (
n=3
, 4, 5)

van der Waals magnets FenGeTe2 (n = 3, 4, 5) Xiuxian Yang, 2, 3 Xiaodong Zhou, 2 Wanxiang Feng, 2, ∗ and Yugui Yao 2 Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China Beijing Key Lab of Nanophotonics & Ultrafine Optoelectronic Systems, School of Physics, Beijing Institute of Technology, Beijing, 100081, China Kunming Institute of Physics, Kunming 650223, China (Dated: October 6, 2021)
3DRIMR: 3D Reconstruction and Imaging via mmWave Radar based on Deep Learning

mmWave radar has been shown as an effective sensing technique in low visibility, smoke, dusty, and dense fog environment. However tapping the potential of radar sensing to reconstruct 3D object shapes remains a great challenge, due to the characteristics of radar data such as sparsity, low resolution, specularity, high noise, and multi-path induced shadow reflections and artifacts. In this paper we propose 3D Reconstruction and Imaging via mmWave Radar (3DRIMR), a deep learning based architecture that reconstructs 3D shape of an object in dense detailed point cloud format, based on sparse raw mmWave radar intensity data. The architecture consists of two back-to-back conditional GAN deep neural networks: the first generator network generates 2D depth images based on raw radar intensity data, and the second generator network outputs 3D point clouds based on the results of the first generator. The architecture exploits both convolutional neural network’s convolutional operation (that extracts local structure neighborhood information) and the efficiency and detailed geometry capture capability of point clouds (other than costly voxelization of 3D space or distance fields). Our experiments have demonstrated 3DRIMR’s effectiveness in reconstructing 3D objects, and its performance improvement over standard techniques.
CFGConf: Supporting high level requirements for visualizing Control Flow Graphs

— Control Flow Graphs (CFGs) are directed graphs that represent all possible walks a program can take during its execution. CFGs are used to analyze computer programs for purposes such as compilation, performance, and security. They are commonly drawn using hierarchical layouts. However, the general nature of such layouts may not capture CFG-speciﬁc structures, making it more difﬁcult to match the drawing to the domain. Domain-speciﬁc drawings often require the help of a graph drawing expert, despite the computing expertise of the target audience. To alleviate these issues, we conduct a survey of drawing conventions and needs for CFGs. We then, through an iterative design process, design a ﬂexible set of representations based on these ﬁndings and develop CFGConf , a JSON speciﬁcation for specifying and drawing these higher-level drawing requirements, thereby allowing users to generate and integrate their own CFG-aware graph drawings. The CFGConf language enables the creation of domain-aware graph drawings of CFGs by increasing the notational efﬁciency of specifying the requirements while also retaining the expressiveness found in commonly used systems such as dot/graphviz . We evaluate CFGConf in terms of notational efﬁciency, expressiveness, and accessibility through user study and illustrative examples.
Beam-Slicing for Jammer Mitigation in mmWave Massive MU-MIMO

Millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO) technology promises unprecedentedly high data rates for next-generation wireless systems. To be practically viable, mmWave massive MU-MIMO basestations (BS) must (i) rely on low-resolution data-conversion and (ii) be robust to jammer interference. This paper considers the problem of mitigating the impact of a permanently transmitting jammer during uplink transmission to a BS equipped with low-resolution analog-to-digital converters (ADCs). To this end, we propose SNIPS, short for Soft-Nulling of Interferers with Partitions in Space. SNIPS combines beam-slicing—a localized, analog spatial transform that focuses the jammer energy onto a subset of all ADCs—together with a soft-nulling data detector that exploits knowledge of which ADCs are contaminated by jammer interference. Our numerical results show that SNIPS is able to successfully serve 65% of the user equipments (UEs) for scenarios in which a conventional antenna-domain soft-nulling data detector is only able to serve 2% of the UEs.
An Efficient Egocentric Regulator for Continuous Targeting Problems of the Underactuated Quadrotor

Currently, for the continuous targeting problem with the underactuated quadrotor, a nonlinear optimal control method with high computational efficiency is still missing. To tackle this problem, a novel efficient egocentric regulation approach with high computational efficiency is proposed in this article. Specifically, it directly formulates the optimal control problem in an egocentric manner regarding the quadrotor's body coordinates. Meanwhile, the nonlinearities of the system are decoupled through a mapping of the feedback states and control inputs, between the inertial and body coordinates. In this way, it only requires solving a quadratic performance objective with linear constraints and then generates control inputs analytically. Simulations and mimic biological experiments are carried out to verify the effectiveness and computational efficiency. Results demonstrate that the proposed control approach presents the highest and most stable computational efficiency compared with generic optimizers on different platforms. Particularly, on a commonly utilized onboard computer, our method can compute the control action in approximately 0.3 ms, which is on the order of 350 times faster than that of generic optimizers, establishing a control frequency around 3000 Hz.
MmWave Radar and Vision Fusion based Object Detection for Autonomous Driving: A Survey

With autonomous driving developing in a booming stage, accurate object detection in complex scenarios attract wide attention to ensure the safety of autonomous driving. Millimeter wave (mmWave) radar and vision fusion is a mainstream solution for accurate obstacle detection. This article presents a detailed survey on mmWave radar and vision fusion based obstacle detection methods. Firstly, we introduce the tasks, evaluation criteria and datasets of object detection for autonomous driving. Then, the process of mmWave radar and vision fusion is divided into three parts: sensor deployment, sensor calibration and sensor fusion, which are reviewed comprehensively. Especially, we classify the fusion methods into data level, decision level and feature level fusion methods. Besides, we introduce the fusion of lidar and vision in autonomous driving in the aspects of obstacle detection, object classiﬁcation and road segmentation, which is promising in the future. Finally, we summarize this article.
Sentence Semantic Regression for Text Generation

Recall the classical text generation works, the generation framework can be briefly divided into two phases: \textbf{idea reasoning} and \textbf{surface realization}. The target of idea reasoning is to figure out the main idea which will be presented in the following talking/writing periods. Surface realization aims to arrange the most appropriate sentence to depict and convey the information distilled from the main idea. However, the current popular token-by-token text generation methods ignore this crucial process and suffer from many serious issues, such as idea/topic drift. To tackle the problems and realize this two-phase paradigm, we propose a new framework named Sentence Semantic Regression (\textbf{SSR}) based on sentence-level language modeling. For idea reasoning, two architectures \textbf{SSR-AR} and \textbf{SSR-NonAR} are designed to conduct sentence semantic regression autoregressively (like GPT2/3) and bidirectionally (like BERT). In the phase of surface realization, a mixed-granularity sentence decoder is designed to generate text with better consistency by jointly incorporating the predicted sentence-level main idea as well as the preceding contextual token-level information. We conduct experiments on four tasks of story ending prediction, story ending generation, dialogue generation, and sentence infilling. The results show that SSR can obtain better performance in terms of automatic metrics and human evaluation.
Stochastic Deep Model Reference Adaptive Control

In this paper, we present a Stochastic Deep Neural Network-based Model Reference Adaptive Control. Building on our work "Deep Model Reference Adaptive Control", we extend the controller capability by using Bayesian deep neural networks (DNN) to represent uncertainties and model nonlinearities. Stochastic Deep Model Reference Adaptive Control uses a Lyapunov-based method to adapt the outputlayer weights of the DNN model in real-time, while a data-driven supervised learning algorithm is used to update the inner-layers parameters. This asynchronous network update ensures boundedness and guaranteed tracking performance with a learning-based real-time feedback controller. A Bayesian approach to DNN learning helped avoid over-fitting the data and provide confidence intervals over the predictions. The controller’s stochastic nature also ensured "Induced Persistency of excitation," leading to convergence of the overall system signal.