Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation

The ever-growing multimedia traffic has underscored the importance of effective multimedia codecs. Among them, the up-to-date lossy video coding standard, Versatile Video Coding (VVC), has been attracting attentions of video coding community. However, the gain of VVC is achieved at the cost of significant encoding complexity, which brings the need to realize fast encoder with comparable Rate Distortion (RD) performance. In this paper, we propose to optimize the VVC complexity at intra-frame prediction, with a two-stage framework of deep feature fusion and probability estimation. At the first stage, we employ the deep convolutional network to extract the spatial-temporal neighboring coding features. Then we fuse all reference features obtained by different convolutional kernels to determine an optimal intra coding depth. At the second stage, we employ a probability-based model and the spatial-temporal coherence to select the candidate partition modes within the optimal coding depth. Finally, these selected depths and partitions are executed whilst unnecessary computations are excluded. Experimental results on standard database demonstrate the superiority of proposed method, especially for High Definition (HD) and Ultra-HD (UHD) video sequences.
The Soft Skills of Software Learning Development: the Psychological Dimensions of Computing and Security Behaviours

When writing software code, developers typically prioritise functionality over security, either consciously or unconsciously through biases and heuristics. This is often attributed to tangible pressures such as client requirements, but little is understood about the psychological dimensions affecting security behaviours. There is an increasing demand for understanding how psychological skills affect secure software development and to understand how these skills themselves are developed during the learning process. This doctoral research explores this research space, with aims to identify important workplace-based skills for software developers; to identify and empirically investigate the soft skills behind these workplace skills in order to understand how soft skills can influence security behaviours; and, to identify ways to introduce and teach soft skills to computer science students to prepare the future generation of software developers. The motivations behind this research are presented alongside the work plan. Three distinct phases are introduced, along with planned analyses. Phase one is currently in the data collection stage, with the second phase in planning. Prior relevant work is highlighted, and the paper concludes with a presentation of preliminary results and the planned next steps.
PGADA: Perturbation-Guided Adversarial Alignment for Few-shot Learning Under the Support-Query Shift

Few-shot learning methods aim to embed the data to a low-dimensional embedding space and then classify the unseen query data to the seen support set. While these works assume that the support set and the query set lie in the same embedding space, a distribution shift usually occurs between the support set and the query set, i.e., the Support-Query Shift, in the real world. Though optimal transportation has shown convincing results in aligning different distributions, we find that the small perturbations in the images would significantly misguide the optimal transportation and thus degrade the model performance. To relieve the misalignment, we first propose a novel adversarial data augmentation method, namely Perturbation-Guided Adversarial Alignment (PGADA), which generates the hard examples in a self-supervised manner. In addition, we introduce Regularized Optimal Transportation to derive a smooth optimal transportation plan. Extensive experiments on three benchmark datasets manifest that our framework significantly outperforms the eleven state-of-the-art methods on three datasets.
Verifying Integrity of Deep Ensemble Models by Lossless Black-box Watermarking with Sensitive Samples

With the widespread use of deep neural networks (DNNs) in many areas, more and more studies focus on protecting DNN models from intellectual property (IP) infringement. Many existing methods apply digital watermarking to protect the DNN models. The majority of them either embed a watermark directly into the internal network structure/parameters or insert a zero-bit watermark by fine-tuning a model to be protected with a set of so-called trigger samples. Though these methods work very well, they were designed for individual DNN models, which cannot be directly applied to deep ensemble models (DEMs) that combine multiple DNN models to make the final decision. It motivates us to propose a novel black-box watermarking method in this paper for DEMs, which can be used for verifying the integrity of DEMs. In the proposed method, a certain number of sensitive samples are carefully selected through mimicking real-world DEM attacks and analyzing the prediction results of the sub-models of the non-attacked DEM and the attacked DEM on the carefully crafted dataset. By analyzing the prediction results of the target DEM on these carefully crafted sensitive samples, we are able to verify the integrity of the target DEM. Different from many previous methods, the proposed method does not modify the original DEM to be protected, which indicates that the proposed method is lossless. Experimental results have shown that the DEM integrity can be reliably verified even if only one sub-model was attacked, which has good potential in practice.
Reliability of Robotic Ultrasound Scanning for Scoliosis Assessment in Comparison with Manual Scanning

Background: Ultrasound (US) imaging for scoliosis assessment is challenging for a non-experienced operator. The robotic scanning was developed to follow a spinal curvature with deep learning and apply consistent forces to the patient’s back. Methods: 23 scoliosis patients were scanned with US device both, robotically and manually. Two human raters measured each subject’s spinous process angles (SPA) on robotic and manual coronal images. Results: The robotic method showed high intra- ( ICC > 0 . 85) and inter-rater ( ICC > 0 . 77) reliabilities. Compared with the manual method, the robotic approach showed no signiﬁcant diﬀerence ( p < 0 . 05 ) when measuring coronal deformity angles. The MAD for intra-rater analysis lies within an acceptable range from 0 ° to 5 ° for the minimum of 86% and maximum 97% of a total number of the measured angles. Conclusions: This study demonstrated that scoliosis deformity angles measured on ultrasound images obtained with robotic scanning are comparable to those obtained by manual scanning.
Seamless Integration of Analysis and Design: Automatic CAD Reconstruction of Post-Analysis Geometries

A key step during industrial design is the passing of design information from computer aided design (CAD) to analysis tools (CAE) and vice versa. Here, one is faced with a severe incompatibility in geometry representation: While CAD is usually based on surface representations, analysis mostly relies on volumetric representations. The forward pass, i.e., converting CAD data to computational meshes, is well understood and established. However, the same does not hold for the inverse direction, i.e., CAD reconstruction of deformed geometries resulting, e.g., from shape optimization. The few reconstruction methods reported mainly rely on spline fitting, in particular methods that rely on creating new splines simililar to shape reconstruction from 3D imaging. In contrast, this paper studies a novel approach that reuses the CAD data given in the initial design. We show that this concept leads to precise shape reconstructions while also preserving the initial notion of features defined during design. Furthermore, reusing the initial CAD representation reduces the shape reconstruction problem to a shape modification problem. We study this unique feature and show that it enables the reconstruction of CAD data from computational meshes by composing each spline in the initial CAD data with a single, global deformation spline. While post-processing is needed for use in current CAD software, this novel approach not only allows creating watertight models, but also enables reconstructing complete CAD models even from defeatured computational meshes.
Write It Like You See It: Detectable Differences in Clinical Notes by Race Lead to Differential Model Recommendations

Clinical notes are becoming an increasingly important data source for machine learning (ML) applications in healthcare. Prior research has shown that deploying ML models can perpetuate existing biases against racial minorities, as bias can be implicitly embedded in data. In this study, we investigate the level of implicit race information available to ML models and human experts and the implications of model-detectable differences in clinical notes. Our work makes three key contributions. First, we find that models can identify patient self-reported race from clinical notes even when the notes are stripped of explicit indicators of race. Second, we determine that human experts are not able to accurately predict patient race from the same redacted clinical notes. Finally, we demonstrate the potential harm of this implicit information in a simulation study, and show that models trained on these race-redacted clinical notes can still perpetuate existing biases in clinical treatment decisions.
Context-Aware Abbreviation Expansion Using Large Language Models

Motivated by the need for accelerating text entry in augmentative and alternative communication (AAC) for people with severe motor impairments, we propose a paradigm in which phrases are abbreviated aggressively as primarily word-initial letters. Our approach is to expand the abbreviations into full-phrase options by leveraging conversation context with the power of pretrained large language models (LLMs). Through zero-shot, few-shot, and fine-tuning experiments on four public conversation datasets, we show that for replies to the initial turn of a dialog, an LLM with 64B parameters is able to exactly expand over 70% of phrases with abbreviation length up to 10, leading to an effective keystroke saving rate of up to about 77% on these exact expansions. Including a small amount of context in the form of a single conversation turn more than doubles abbreviation expansion accuracies compared to having no context, an effect that is more pronounced for longer phrases. Additionally, the robustness of models against typo noise can be enhanced through fine-tuning on noisy data.
EigenNoise: A Contrastive Prior to Warm-Start Representations

In this work, we present a naïve initialization 001 scheme for word vectors based on a dense, in- 002 dependent co-occurrence model and provide 003 preliminary results that suggests it is competi- 004 tive, and warrants further investigation. Specif- 005 ically, we demonstrate through information- 006 theoretic minimum description length (MDL) 007 probing that our model, EigenNoise, can ap- 008 proach the performance of empirically trained 009 GloVe despite the lack of any pre-training 010 data (in the case of EigenNoise). We present 011 these preliminary results with interest to set 012 the stage for further investigations into how 013 this competitive initialization works without 014 pre-training data, as well as to invite the 015 exploration of more intelligent initialization 016 schemes informed by the theory of harmonic 017 linguistic structure. Our application of this the- 018 ory likewise contributes a novel (and effective) 019 interpretation of recent discoveries which have 020 elucidated the underlying distributional infor- 021 mation that linguistic representations capture 022 from data and contrast distributions. 023
OpenPodcar: an Open Source Vehicle for Self-Driving Car Research

OpenPodcar is a low-cost, open source hardware and software, autonomous vehicle research platform based on an off-the-shelf, hard-canopy, mobility scooter donor vehicle. Hardware and software build instructions are provided to convert the donor vehicle into a low-cost and fully autonomous platform. The open platform consists of (a) hardware components: CAD designs, bill of materials, and build instructions; (b) Arduino, ROS and Gazebo control and simulation software files which provide standard ROS interfaces and simulation of the vehicle; and (c) higher-level ROS software implementations and configurations of standard robot autonomous planning and control, including the move_base interface with Timed-Elastic-Band planner which enacts commands to drive the vehicle from a current to a desired pose around obstacles. The vehicle is large enough to transport a human passenger or similar load at speeds up to 15km/h, for example for use as a last-mile autonomous taxi service or to transport delivery containers similarly around a city center. It is small and safe enough to be parked in a standard research lab and be used for realistic human-vehicle interaction studies. System build cost from new components is around USD7,000 in total in 2022. OpenPodcar thus provides a good balance between real world utility, safety, cost and research convenience.
Effectively Using Long and Short Sessions for Multi-Session-based Recommendations

It is not accurate to make recommendations only based one single current session. Therefore, multi-session-based recommendation(MSBR) is a solution for the problem. Compared with the previous MSBR models, we have made three improvements in this paper. First, the previous work choose to use all the history sessions of the user and/or of his similar users. When the user's current interest changes greatly from the past, most of these sessions can only have negative impacts. Therefore, we select a large number of randomly chosen sessions from the dataset as candidate sessions to avoid over depending on history data. Then we only choose to use the most similar sessions to get the most useful information while reduce the noise caused by dissimilar sessions. Second, in real-world datasets, short sessions account for a large proportion. The RNN often used in previous work is not suitable to process short sessions, because RNN only focuses on the sequential relationship, which we find is not the only relationship between items in short sessions. So, we designed a more suitable method named GAFE based on attention to process short sessions. Third, Although there are few long sessions, they can not be ignored. Not like previous models, which simply process long sessions in the same way as short sessions, we propose LSIS, which can split the interest of long sessions, to make better use of long sessions. Finally, to help recommendations, we also have considered users' long-term interests captured by a multi-layer GRU. Considering the four points above, we built the model ENIREC. Experiments on two real-world datasets show that the comprehensive performance of ENIREC is better than other existing models.
The Detection of Deuterated Water in the Large Magellanic Cloud with ALMA

We report the first detection of deuterated water (HDO) toward an extragalactic hot core. The HDO 211–212 line has been detected toward hot cores N 105–2 A and 2 B in the N 105 star-forming region in the low-metallicity Large Magellanic Cloud (LMC) dwarf galaxy with the Atacama Large Millimeter/submillimeter Array (ALMA). We have compared the HDO line luminosity (L HDO) measured toward the LMC hot cores to those observed toward a sample of 17 Galactic hot cores covering three orders of magnitude in L HDO, four orders of magnitude in bolometric luminosity (L bol), and a wide range of Galactocentric distances (thus metallicities). The observed values of L HDO for the LMC hot cores fit very well into the L HDO trends with L bol and metallicity observed toward the Galactic hot cores. We have found that L HDO seems to be largely dependent on the source luminosity, but metallicity also plays a role. We provide a rough estimate of the H2O column density and abundance ranges toward the LMC hot cores by assuming that HDO/H2O toward the LMC hot cores is the same as that observed in the Milky Way; the estimated ranges are systematically lower than Galactic values. The spatial distribution and velocity structure of the HDO emission in N 105–2 A is consistent with HDO being the product of the low-temperature dust grain chemistry. Our results are in agreement with the astrochemical model predictions that HDO is abundant regardless of the extragalactic environment and should be detectable with ALMA in external galaxies.
Approaches and Challenges in Robotic Perception for Table-top Rearrangement and Planning

Table-top Rearrangement and Planning is a challenging problem that relies heavily on an excellent perception stack. The perception stack involves observing and registering the 3D scene on the table, detecting what objects are on the table, and how to manipulate them. Consequently, it greatly influences the system's task-planning and motion-planning stacks that follow. We present a comprehensive overview and discuss the different challenges associated with the perception module. This work is a result of our extensive involvement in the ICRA 2022 Open Cloud Robot Table Organization Challenge, in which we stood third in the final rankings.
Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation

Virtual facial avatars will play an increasingly important role in immersive communication, games and the metaverse, and it is therefore critical that they be inclusive. This requires accurate recovery of the appearance, represented by albedo, regardless of age, sex, or ethnicity. While significant progress has been made on estimating 3D facial geometry, albedo estimation has received less attention. The task is fundamentally ambiguous because the observed color is a function of albedo and lighting, both of which are unknown. We find that current methods are biased towards light skin tones due to (1) strongly biased priors that prefer lighter pigmentation and (2) algorithmic solutions that disregard the light/albedo ambiguity. To address this, we propose a new evaluation dataset (FAIR) and an algorithm (TRUST) to improve albedo estimation and, hence, fairness. Specifically, we create the first facial albedo evaluation benchmark where subjects are balanced in terms of skin color, and measure accuracy using the Individual Typology Angle (ITA) metric. We then address the light/albedo ambiguity by building on a key observation: the image of the full scene -- as opposed to a cropped image of the face -- contains important information about lighting that can be used for disambiguation. TRUST regresses facial albedo by conditioning both on the face region and a global illumination signal obtained from the scene image. Our experimental results show significant improvement compared to state-of-the-art methods on albedo estimation, both in terms of accuracy and fairness. The evaluation benchmark and code will be made available for research purposes at https://trust.is. tue.mpg. de.
Stochastic gravitational waves from long cosmic strings

We compute the expected strain power spectrum and energy density parameter of the stochastic gravitational wave background (SGWB) created by a network of long cosmic strings evolving during the whole cosmic history. As opposed to other studies, the contribution of cosmic string loops is discarded and our result provides a robust lower bound of the expected signal that is applicable to most string models. Our approach uses Nambu-Goto numerical simulations, running during the radiation, transition and matter eras, in which we compute the two-point unequal-time anisotropic stress correlators. These ones act as source terms in the linearised equations of motion for the tensor modes, that we solve using an exact Green's function integrator. Today, we find that the rescaled strain power spectrum (k/ℋ0)2𝒫 h peaks on Hubble scales and exhibits, at large wavenumbers, high frequency oscillations around a plateau of amplitude 100 (GU)2. Most of the high frequency power is generated by the long strings present in the matter era, the radiation era contribution being smaller.
Multi-segment preserving sampling for deep manifold sampler

Deep generative modeling for biological sequences presents a unique challenge in reconciling the bias-variance trade-off between explicit biological insight and model flexibility. The deep manifold sampler (Gligorijevi´c et al., 2021) was recently proposed as a means to iteratively sample variable-length protein sequences. Sampling was done by exploiting the gradients from a function predictor trained on top of the manifold sampler. In this work, we introduce an alternative approach to guided sampling that enables the direct inclusion of domain-specific knowledge by designating preserved and non-preserved segments along the input sequence, thereby restricting variation to only select regions. We call this method “multi-segment preserving sampling” and present its effectiveness in the context of antibody design. We train two models: a deep manifold sampler and a GPT-2 language model on nearly six million heavy chain sequences anno-tated with the IGHV1-18 gene. During sampling, we restrict variation to only the complementarity-determining region 3 (CDR3) of the input. We obtain log probability scores from a GPT-2 model for each sampled CDR3 and demonstrate that multi-segment preserving sampling generates reasonable designs while maintain-ing the desired, preserved regions.
Slicing of Probabilistic Programs based on Specifications

This paper presents the first slicing approach for probabilistic programs based on specifications. We show that when probabilistic programs are accompanied by their specifications in the form of pre- and post-condition, we can exploit this semantic information to produce specification-preserving slices strictly more precise than slices yielded by conventional techniques based on data/control dependency. To achieve this goal, our technique is based on the backward propagation of post-conditions via the greatest pre-expectation transformer -- the probabilistic counterpart of Dijkstra weakest pre-condition transformer. The technique is termination-sensitive, allowing to preserve the partial as well as the total correctness of probabilistic programs w.r. t. their specifications. It is modular, featuring a local reasoning principle, and is formally proved correct. As fundamental technical ingredients of our technique, we design and prove sound verification condition generators for establishing the partial and total correctness of probabilistic programs, which are of interest on their own and can be exploited elsewhere for other purposes. On the practical side, we demonstrate the applicability of our approach by means of a few illustrative examples and a case study from the probabilistic modelling field. We also describe an algorithm for computing least slices among the space of slices derived by our technique.
Realization of Lattice Formation in Nonlinear Two-dimensional Potential by Mobile Robots

Formation control in multi-agent system has earned signiﬁcant research interests in both theorical aspect and applications over the past two decades. However, the study on how the external environment shapes swarm formation dynamics, and the design of formation control algorithm for multi-agent system in nonlinear external potential have not been rigorously investigated. In this paper, we present a formation control algorithm for mobile robots travelling in nonlinear external potential. Experiments are performed on real mobile robots to verify the algorithm, and the eﬀec-tiveness of Dynamical Mode Decomposition in robot’s velocity prediction in unknown environment is demonstrated.