Miracle-dz/UniFAR-FTUs huggingface dataset:
On deployment and security in mobile wireless sensor networks

Wireless sensor networks have become increasingly pervasive with promises to fulfill many of our critical necessities today. One issue that has permeated sensor networks recently is mobility. Broadly, mobility in sensor networks can be categorized into two classes: Internal mobility and External mobility. Internal mobility is the class where sensors themselves can move from one location to another, while external mobility is the class where certain external agents (not sensors) move in the network. Both mobility classes have patent and significant impacts to sensor networks operation. However, being an emerging topic, a clear understanding of opportunities and challenges of sensor networks mobility is lacking today, and hence is an important need of the hour. In this dissertation, we make contributions in both classes of sensor networks mobility. First, we study the issue of how sensors can use their mobility to enhance quality of network deployment. We define two representative mobility assisted sensor network deployment problems. In our mobility model, there are hard limitations in both sensors' mobility pattern and distance. Such limitations are natural due to constraints on sensors' form-factor and energy. We identify critical challenges arising in deployment under such hard mobility limitations. We then propose a suit of sensor mobility algorithms for our deployment problems, and demonstrate their performance using theoretical analysis and extensive simulations. Second, we study the issue of external mobility in sensor networks from a security perspective. We identify a unique security threat in sensor networks called physical attacks. We define a representative model of physical attacks, wherein an external mobile agent (human being or robot) moves in the network detecting inherent physical/electronic sensor signals to localize sensors, and then physically destroys them. We formally model such attacks in sensor networks, demonstrate their destruction potential, identify variations, and finally propose countermeasure guidelines against them. With the emergence of mobility in wireless sensor networks, coupled with its significances, we hope that our work in this dissertation can provide strong foundations and further motivations for researchers to explore this topic that promises to revolutionarize sensor networks research in the near future and beyond.
flopp: Extremely Fast Long-Read Polyploid Haplotype Phasing by Uniform Tree Partitioning

Resolving haplotypes in polyploid genomes using phase information from sequencing reads is an important and challenging problem. We introduce two new mathematical formulations of polyploid haplotype phasing: (1) the min-sum max tree partition problem, which is a more flexible graphical metric compared with the standard minimum error correction (MEC) model in the polyploid setting, and (2) the uniform probabilistic error minimization model, which is a probabilistic analogue of the MEC model. We incorporate both formulations into a long-read based polyploid haplotype phasing method called flopp. We show that flopp compares favorably with state-of-the-art algorithms—up to 30 times faster with 2 times fewer switch errors on 6 × ploidy simulated data. Further, we show using real nanopore data that flopp can quickly reveal reasonable haplotype structures from the autotetraploid Solanum tuberosum (potato).
Mammals' humoral immune proteins and peptides targeting the bacterial envelope: from natural protection to therapeutic applications against multidrug‐resistant Gram‐negatives

Mammalian innate immunity employs several humoral ‘weapons’ that target the bacterial envelope. The threats posed by the multidrug‐resistant ‘ESKAPE’ Gram‐negative pathogens (Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) are forcing researchers to explore new therapeutic options, including the use of these immune elements. Here we review bacterial envelope‐targeting (peptidoglycan and/or membrane‐targeting) proteins/peptides of the mammalian immune system that are most likely to have therapeutic applications. Firstly we discuss their general features and protective activity against ESKAPE Gram‐negatives in the host. We then gather, integrate, and discuss recent research on experimental therapeutics harnessing their bactericidal power, based on their exogenous administration and also on the discovery of bacterial and/or host targets that improve the performance of this endogenous immunity, as a novel therapeutic concept. We identify weak points and knowledge gaps in current research in this field and suggest areas for future work to obtain successful envelope‐targeting therapeutic options to tackle the challenge of antimicrobial resistance.
Introducing the Continuous Glucose Data Analysis (CGDA) R Package: An Intuitive Package to Analyze Continuous Glucose Monitoring Data

The first continuous glucose monitors (CGM) have been introduced more than 20 years ago and today multiple options exist.1 These options consist of real-time CGM (rtCGM) and intermittent CGM (iCGM) or Flash monitors. The rt-CGM measure the glucose levels continuously and can alert if a patient is moving toward a hypoor a hyperglycaemic event. The iCGM, however, only show the glucose levels when the patient actively scans the device. Multiple studies have shown that implementing CGM in routine clinical care has positive effects, regardless of the type of CGM.2 The main benefit is that CGM show real-time glucose levels and fluctuation, thereby overcoming limitations of “old” metrics such as HbA1c and fasting glucose levels. However, despite recommendations, routine use of CGM in clinical practice remains scare.3 A possible explanation for this is the fact that using and interpreting results from CGM remains difficult for health care practitioners. Furthermore, analyses of the data in clinical trials differ widely and are therefore difficult to generalize and use in clinical practice.2 Recently, the American Diabetes Association (ADA) published a guideline on how to use CGM in daily practice.3 To date, no tool exists that can easily and intuitively provide these data from CGM. Several statistical packages do exist that can assist in extracting data from the CGM (e.g., HbA1c, glycemic excursions). 4,5 However, a major drawback of these packages is that the user needs to be familiar with programming and the output is often in mg/dL or %, making implementation and interpretation for clinicians that use other metrics difficult. Moreover, analyses packages of manufacturers are not insightful in how analyses are done and which algorithms are used. Through this letter we wish to introduce the Continuous Glucose Data Analysis (CGDA) Package in the programming language R. This is an easy-to-use, free package that incorporates the ADA recommendations, has graphical output, and provides output in all commonly used metrics (i.e., glucose mmol/mol, mg/dL, and HbA1c % as well as mg/dL).
High-fidelity 3D Model Compression based on Key Spheres

In recent years, neural signed distance function (SDF) has become one of the most effective representation methods for 3D models. By learning continuous SDFs in 3D space, neural networks can predict the distance from a given query space point to its closest object surface, whose positive and negative signs denote inside and outside of the object, respectively. Training a specific network for each 3D model, which individually embeds its shape, can realize compressed representation of objects by storing fewer network (and possibly latent) parameters. Consequently, reconstruction through network inference and surface recovery can be achieved. In this paper, we propose an SDF prediction network using explicit key spheres as input. Key spheres are extracted from the internal space of objects, whose centers either have relatively larger SDF values (sphere radii), or are located at essential positions. By inputting the spatial information of multiple spheres which imply different local shapes, the proposed method can significantly improve the reconstruction accuracy with a negligible storage cost. Compared to previous works, our method achieves the high-fidelity and high-compression 3D object coding and reconstruction. Experiments conducted on three datasets verify the superior performance of our method.
Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation

Deep image matting methods have achieved increasingly better results on benchmarks (e.g., Composition-1k/alphamatting.com). However, the robustness, including robustness to trimaps and generalization to images from different domains, is still underexplored. Although some works propose to either refine the trimaps or adapt the algorithms to real-world images via extra data augmentation, none of them has taken both into consideration, not to mention the significant performance deterioration on benchmarks while using those data augmentation. To fill this gap, we propose an image matting method which achieves higher robustness (RMat) via multilevel context assembling and strong data augmentation targeting matting. Specifically, we first build a strong matting framework by modeling ample global information with transformer blocks in the encoder, and focusing on details in combination with convolution layers as well as a low-level feature assembling attention block in the decoder. Then, based on this strong baseline, we analyze current data augmentation and explore simple but effective strong data augmentation to boost the baseline model and contribute a more generalizable matting method. Compared with previous methods, the proposed method not only achieves state-of-the-art results on the Composition-1k benchmark (11 % improvement on SAD and 27% improvement on Grad) with smaller model size, but also shows more robust generalization results on other benchmarks, on real-world images, and also on varying coarse-to-fine trimaps with our extensive experiments.11This work was in part done when YD was an intern at Adobe and CS was with The University of Adelaide. CS is the corresponding author. Project page: https://dongdong93.github. io/RMat/.
Representation Learning on Heterostructures via Heterogeneous Anonymous Walks

Capturing structural similarity has been a hot topic in the field of network embedding (NE) recently due to its great help in understanding node functions and behaviors. However, existing works have paid very much attention to learning structures on homogeneous networks, while the related study on heterogeneous networks is still void. In this article, we try to take the first step for representation learning on heterostructures, which is very challenging due to their highly diverse combinations of node types and underlying structures. To effectively distinguish diverse heterostructures, we first propose a theoretically guaranteed technique called heterogeneous anonymous walk (HAW) and give two more applicable variants. Then, we devise the HAW embedding (HAWE) and its variants in a data-driven manner to circumvent using an extremely large number of possible walks and train embeddings by predicting occurring walks in the neighborhood of each node. Finally, we design and apply extensive and illustrative experiments on synthetic and real-world networks to build a benchmark on heterostructure learning and evaluate the effectiveness of our methods. The results demonstrate our methods achieve outstanding performance compared with both homogeneous and heterogeneous classic methods and can be applied on large-scale networks.
Context-Aware Scene Prediction Network (CASPNet)

Predicting the future motion of surrounding road users is a crucial and challenging task for autonomous driving (AD) and various advanced driver-assistance systems (ADAS). Planning a safe future trajectory heavily depends on understanding the traffic scene and anticipating its dynamics. The challenges do not only lie in understanding the complex driving scenarios but also the numerous possible interactions among road users and environments, which are practically not feasible for explicit modeling. In this work, we tackle the above challenges by jointly learning and predicting the motion of all road users in a scene, using a novel convolutional neural network (CNN) and recurrent neural network (RNN) based architecture. Moreover, by exploiting grid-based input and output data structures, the computational cost is independent of the number of road users and multi-modal predictions become inherent properties of our proposed method. Evaluation on the nuScenes dataset shows that our approach reaches state-of-the-art results in the prediction benchmark.
K-Theoretic Descendent Series for Hilbert Schemes of Points on Surfaces

We study the holomorphic Euler characteristics of tautological sheaves on Hilbert schemes of points on surfaces. In particular, we establish the rationality of K-theoretic descendent series. Our approach is to control equivariant holomorphic Euler characteristics over the Hilbert scheme of points on the affine plane. To do so, we slightly modify a Macdonald polynomial identity of Mellit.
Defining Security Requirements With the Common Criteria: Applications, Adoptions, and Challenges

Advances in emerging Information and Communications Technology (ICT) technologies push the boundaries of what is possible and open up new markets for innovative ICT products and services. The adoption of ICT products and systems with security properties depends on consumers’ confidence and markets’ trust in the security functionalities and whether the assurance measures applied to these products meet the inherent security requirements. Such confidence and trust are primarily gained through the rigorous development of security requirements, validation criteria, evaluation, and certification. The Common Criteria for Information Technology Security Evaluation (often referred to as Common Criteria or CC) is an international standard (ISO/IEC 15408) for cyber security. Motivated by encouraging the adoption of the CC that is used for ICT security evaluation and certification, in this paper, we conduct a systematic review of the CC standard and its adoptions. Adoption barriers of the CC are investigated based on the analysis of current trends in cyber security evaluation. In addition, we share the experiences and lessons gained through the recent Development of Australian Cyber Criteria Assessment (DACCA) project on the development of the Protection Profile that defines security requirements with the CC. Best practices, challenges, and future directions on defining security requirements for trusted cyber security advancement are presented.
Numerical gate synthesis for quantum heuristics on bosonic quantum processors

There is a recent surge of interest and insights regarding the interplay of quantum optimal control and variational quantum algorithms. We study the framework in the context of qudits which are, for instance, definable as controllable electromagnetic modes of a superconducting cavity system coupled to a transmon. By employing recent quantum optimal control approaches described in (Petersson and Garcia, 2021), we showcase control of single-qudit operations up to eight states, and two-qutrit operations, mapped respectively onto a single mode and two modes of the resonator. We discuss the results of numerical pulse engineering on the closed system for parametrized gates useful to implement Quantum Approximate Optimization Algorithm (QAOA) for qudits. The results show that high fidelity ( > 0.99) is achievable with sufficient computational effort for most cases under study, and extensions to multiple modes and open, noisy systems are possible. The tailored pulses can be stored and used as calibrated primitives for a future compiler in circuit quantum electrodynamics (cQED) systems.
Contextual road lane and symbol generation for autonomous driving

In this paper we present a novel approach for lane detection and segmentation using generative models. Traditionally discriminative models have been employed to classify pixels semantically on a road. We model the probability distribution of lanes and road symbols by training a generative adversarial network. Based on the learned probability distribution, context aware lanes and road signs are generated for a given image which are further quantized for nearest class label. Proposed method has been tested on BDDIOOK and Baidu’s ApolloScape datasets and performs better than state of the art and exhibits robustness to adverse conditions by generating lanes in faded out and occluded scenarios.
Stability of Deep Neural Networks via discrete rough paths

Using rough path techniques, we provide a priori estimates for the output of Deep Residual Neural Networks in terms of both the input data and the (trained) network weights. As trained network weights are typically very rough when seen as functions of the layer, we propose to derive stability bounds in terms of the total $p$-variation of trained weights for any $p\in[1,3]$. Unlike the $C^1$-theory underlying the neural ODE literature, our estimates remain bounded even in the limiting case of weights behaving like Brownian motions, as suggested in [arXiv:2105.12245]. Mathematically, we interpret residual neural network as solutions to (rough) difference equations, and analyse them based on recent results of discrete time signatures and rough path theory.
Multiway Spherical Clustering Via Degree-Corrected Tensor Block Models

We consider the problem of multiway clustering in the presence of unknown degree heterogeneity. Such data problems arise commonly in applications such as recommendation system, neuroimaging, community detection, and hypergraph partitions in social networks. The allowance of degree heterogeneity provides great flexibility in clustering models, but the extra complexity poses significant challenges in both statistics and computation. Here, we develop a degree-corrected tensor block model with estimation accuracy guarantees. We present the phase transition of clustering performance based on the notion of angle separability, and we characterize three signal-to-noise regimes corresponding to different statistical-computational behaviors. In particular, we demonstrate that an intrinsic statistical-to-computational gap emerges only for tensors of order three or greater. Further, we develop an efficient polynomial-time algorithm that provably achieves exact clustering under mild signal conditions. The efficacy of our procedure is demonstrated through two data applications, one on human brain connectome project, and another on Peru Legislation network dataset.
WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking

Unmanned aerial vehicle (UAV) tracking is of great significance for a wide range of applications, such as delivery and agriculture. Previous benchmarks in this area mainly focused on small-scale tracking problems while ignoring the amounts of data, types of data modalities, diversities of target categories and scenarios, and evaluation protocols involved, greatly hiding the massive power of deep UAV tracking. In this article, we propose WebUAV-3M, the largest public UAV tracking benchmark to date, to facilitate both the development and evaluation of deep UAV trackers. WebUAV-3M contains over 3.3 million frames across 4,500 videos and offers 223 highly diverse target categories. Each video is densely annotated with bounding boxes by an efficient and scalable semi-automatic target annotation (SATA) pipeline. Importantly, to take advantage of the complementary superiority of language and audio, we enrich WebUAV-3M by innovatively providing both natural language specifications and audio descriptions. We believe that such additions will greatly boost future research in terms of exploring language features and audio cues for multi-modal UAV tracking. In addition, a fine-grained UAV tracking-under-scenario constraint (UTUSC) evaluation protocol and seven challenging scenario subtest sets are constructed to enable the community to develop, adapt and evaluate various types of advanced trackers. We provide extensive evaluations and detailed analyses of 43 representative trackers and envision future research directions in the field of deep UAV tracking and beyond. The dataset, toolkits, and baseline results are available at https://github.com/983632847/WebUAV-3M.
What Makes the Story Forward?: Inferring Commonsense Explanations as Prompts for Future Event Generation

Prediction over event sequences is critical for many real-world applications in Information Retrieval and Natural Language Processing. Future Event Generation (FEG) is a challenging task in event sequence prediction because it requires not only fluent text generation but also commonsense reasoning to maintain the logical coherence of the entire event story. In this paper, we propose a novel explainable FEG framework, Coep. It highlights and integrates two types of event knowledge, sequential knowledge of direct event-event relations and inferential knowledge that reflects the intermediate character psychology between events, such as intents, causes, reactions, which intrinsically pushes the story forward. To alleviate the knowledge forgetting issue, we design two modules, IM and GM, for each type of knowledge, which are combined via prompt tuning. First, IM focuses on understanding inferential knowledge to generate commonsense explanations and provide a soft prompt vector for GM. We also design a contrastive discriminator for better generalization ability. Second, GM generates future events by modeling direct sequential knowledge with the guidance of IM. Automatic and human evaluation demonstrate that our approach can generate more coherent, specific, and logical future events.
Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis

Social media data such as Twitter messages (“tweets”) pose a particular challenge to NLP systems because of their short, noisy, and colloquial nature. Tasks such as Named Entity Recognition (NER) and syntactic parsing require highly domain-matched training data for good performance. To date, there is no complete training corpus for both NER and syntactic analysis (e.g., part of speech tagging, dependency parsing) of tweets. While there are some publicly available annotated NLP datasets of tweets, they are only designed for individual tasks. In this study, we aim to create Tweebank-NER, an English NER corpus based on Tweebank V2 (TB2), train state-of-the-art (SOTA) Tweet NLP models on TB2, and release an NLP pipeline called Twitter-Stanza. We annotate named entities in TB2 using Amazon Mechanical Turk and measure the quality of our annotations. We train the Stanza pipeline on TB2 and compare with alternative NLP frameworks (e.g., FLAIR, spaCy) and transformer-based models. The Stanza tokenizer and lemmatizer achieve SOTA performance on TB2, while the Stanza NER tagger, part-of-speech (POS) tagger, and dependency parser achieve competitive performance against non-transformer models. The transformer-based models establish a strong baseline in Tweebank-NER and achieve the new SOTA performance in POS tagging and dependency parsing on TB2. We release the dataset and make both the Stanza pipeline and BERTweet-based models available “off-the-shelf” for use in future Tweet NLP research. Our source code, data, and pre-trained models are available at: https://github.com/social-machines/TweebankNLP.