Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical world assistive signals for deep neural network classifiers - neither defense nor attack

Deep Neural Networks lead the state of the art of computer vision tasks. Despite this, Neural Networks are brittle in that small changes in the input can drastically affect their prediction outcome and confidence. Consequently and naturally, research in this area mainly focus on adversarial attacks and defenses. In this paper, we take an alternative stance and introduce the concept of Assistive Signals, which are optimized to improve a model's confidence score regardless if it's under attack or not. We analyse some interesting properties of these assistive perturbations and extend the idea to optimize assistive signals in the 3D space for real-life scenarios simulating different lighting conditions and viewing angles. Experimental evaluations show that the assistive signals generated by our optimization method increase the accuracy and confidence of deep models more than those generated by conventional methods that work in the 2D space. In addition, our Assistive Signals illustrate the intrinsic bias of ML models towards certain patterns in real-life objects. We discuss how we can exploit these insights to re-think, or avoid, some patterns that might contribute to, or degrade, the detectability of objects in the real-world.
Pedestrian Collision Avoidance for Autonomous Vehicles at Unsignalized Intersection Using Deep Q-Network

Prior research has extensively explored Autonomous Vehicle (AV) navigation in the presence of other vehicles, however, navigation among pedestrians, who are the most vulnerable element in urban environments, has been less examined. This paper explores AV navigation in crowded, unsignalized intersections. We compare the performance of different deep reinforcement learning methods trained on our reward function and state representation. The performance of these methods and a standard rule-based approach were evaluated in two ways, first at the unsignalized intersection on which the methods were trained, and secondly at an unknown unsignalized intersection with a different topology. For both scenarios, the rule-based method achieves less than 40\% collision-free episodes, whereas our methods result in a performance of approximately 100\%. Of the three methods used, DDQN/PER outperforms the other two methods while it also shows the smallest average intersection crossing time, the greatest average speed, and the greatest distance from the closest pedestrian.
Feedback control of event cameras

Dynamic vision sensor event cameras produce a variable data rate stream of brightness change events. Event production at the pixel level is controlled by threshold, bandwidth, and refractory period bias current parameter settings. Biases must be adjusted to match application requirements and the optimal settings depend on many factors. As a first step towards automatic control of biases, this paper proposes fixed-step feedback controllers that use measurements of event rate and noise. The controllers regulate the event rate within an acceptable range using threshold and refractory period control, and regulate noise using bandwidth control. Experiments demonstrate model validity and feedback control.
Reachability Map for Diverse Balancing Strategies and Energy Efficient Stepping of Humanoids

In legged locomotion, the relationship between different gait behaviors and energy consumption must consider the full-body dynamics and the robot control as a whole, which cannot be captured by simple models. This work studies the robot dynamics and whole-body optimal control as a coupled system to investigate energy consumption during balance recovery. We developed a 2-phase nonlinear optimization pipeline for dynamic stepping, which generates reachability maps showing complex energy-stepping relations. We optimize gait parameters to search all reachable locations and quantify the energy cost during dynamic transitions, which allows studying the relationship between energy consumption and stepping locations given different initial conditions. We found that to achieve efficient actuation, the stepping location and timing can have simple approximations close to the underlying optimality. Despite the complexity of this nonlinear process, we show that near-minimal effort stepping locations fall within a region of attractions, rather than a narrow solution space suggested by a simple model. This provides new insights into the non-uniqueness of near-optimal solutions in robot motion planning and control, and the diversity of stepping behavior in humans.
Exploiting Audio-Visual Consistency with Partial Supervision for Spatial Audio Generation

Human perceives rich auditory experience with distinct sound heard by ears. Videos recorded with binaural audio particular simulate how human receives ambient sound. However, a large number of videos are with monaural audio only, which would degrade the user experience due to the lack of ambient information. To address this issue, we propose an audio spatialization framework to convert a monaural video into a binaural one exploiting the relationship across audio and visual components. By preserving the left-right consistency in both audio and visual modalities, our learning strategy can be viewed as a self-supervised learning technique, and alleviates the dependency on a large amount of video data with ground truth binaural audio data during training. Experiments on benchmark datasets confirm the effectiveness of our proposed framework in both semi-supervised and fully supervised scenarios, with ablation studies and visualization further support the use of our model for audio spatialization.
Observation of Sb2S3-type post-post-perovskite in NaFeF3. Implications for ABX3 and A2X3 systems at ultrahigh pressure

Abstract We describe the structures and transformations that lead to the crystallization of a post-post-perovskite of Sb2S3 type in a GdFeO3-type fluoroperovskite system at high-pressure conditions, through use of largevolume powder and single-crystal x-ray diffraction techniques. The results of this analysis gives unique access to the relative crystallographic orientations of all the polymorphs encountered (GdFeO3 type, CaIrO3 type and Sb2S3 type). We use this information to extend this description to include other calculated and observed forms that are competitive in ABX3 and A2X3 stoichiometries (e.g. α-Gd2S3 and Be3N2 types) and provide substantial information on inter-relationships between these structures. Such information is critical to the interpretation of transition mechanisms, predicting transition sequences and to the expression of directional properties in those transformed structures. The transformation from CaIrO3 type to Sb2S3 type is group-subgroup, from Cmcm with fc2a, to Pnma c5, with no observable volume change, but considerable change to the morphology of the lattice. There is a concomitant increase in coordination and average bond length compared to the post-perovskite form of NaFeF3.
How Bayesian should Bayesian optimisation be?

Bayesian optimisation (BO) uses probabilistic surrogate models - usually Gaussian processes (GPs) - for the optimisation of expensive black-box functions. At each BO iteration, the GP hyperparameters are fit to previously-evaluated data by maximising the marginal likelihood. However, this fails to account for uncertainty in the hyperparameters themselves, leading to overconfident model predictions. This uncertainty can be accounted for by taking the Bayesian approach of marginalising out the model hyperparameters. We investigate whether a fully-Bayesian treatment of the Gaussian process hyperparameters in BO (FBBO) leads to improved optimisation performance. Since an analytic approach is intractable, we compare FBBO using three approximate inference schemes to the maximum likelihood approach, using the Expected Improvement (EI) and Upper Confidence Bound (UCB) acquisition functions paired with ARD and isotropic Matérn kernels, across 15 well-known benchmark problems for 4 observational noise settings. FBBO using EI with an ARD kernel leads to the best performance in the noise-free setting, with much less difference between combinations of BO components when the noise is increased. FBBO leads to over-exploration with UCB, but is not detrimental with EI. Therefore, we recommend that FBBO using EI with an ARD kernel as the default choice for BO.
Online Fashion Commerce: Modelling Customer Promise Date

In the e-commerce space, accurate prediction of delivery dates plays a major role in customer experience as well as in optimizing the supply chain operations. Predicting a date later than the actual delivery date might sometimes result in the customer not placing the order (lost sales) while promising a date earlier than the actual delivery date would lead to a bad customer experience and consequent customer churn. In this paper, we present a machine learning-based approach for penalizing incorrect predictions differently using non-conventional loss functions, while working under various uncertainties involved in making successful deliveries such as traffic disruptions, weather conditions, supply chain, and logistics. We examine statistical, deep learning, and conventional machine learning approaches, and we propose an approach that outperformed the pre-existing rule-based models. The proposed model is deployed internally for Fashion e-Commerce and is operational.
Intelligent Conversational Android ERICA Applied to Attentive Listening and Job Interview

Following the success of spoken dialogue systems (SDS) in smartphone assistants and smart speakers, a number of communicative robots are developed and commercialized. Compared with the conventional SDSs designed as a human-machine interface, interaction with robots is expected to be in a closer manner to talking to a human because of the anthropomorphism and physical presence. The goal or task of dialogue may not be information retrieval, but the conversation itself. In order to realize human-level"long and deep"conversation, we have developed an intelligent conversational android ERICA. We set up several social interaction tasks for ERICA, including attentive listening, job interview, and speed dating. To allow for spontaneous, incremental multiple utterances, a robust turn-taking model is implemented based on TRP (transition-relevance place) prediction, and a variety of backchannels are generated based on time frame-wise prediction instead of IPU-based prediction. We have realized an open-domain attentive listening system with partial repeats and elaborating questions on focus words as well as assessment responses. It has been evaluated with 40 senior people, engaged in conversation of 5-7 minutes without a conversation breakdown. It was also compared against the WOZ setting. We have also realized a job interview system with a set of base questions followed by dynamic generation of elaborating questions. It has also been evaluated with student subjects, showing promising results.
QDOT: Quantized Dot Product Kernel for Approximate High-Performance Computing

Approximate computing techniques have been successful in reducing computation and power costs in several domains. However, error sensitive applications in high-performance computing are unable to benefit from existing approximate computing strategies that are not developed with guaranteed error bounds. While approximate computing techniques can be developed for individual high-performance computing applications by domain specialists, this often requires additional theoretical analysis and potentially extensive software modification. Hence, the development of low-level error-bounded approximate computing strategies that can be introduced into any high-performance computing application without requiring additional analysis or significant software alterations is desirable. In this paper, we provide a contribution in this direction by proposing a general framework for designing error-bounded approximate computing strategies and apply it to the dot product kernel to develop qdot -- an error-bounded approximate dot product kernel. Following the introduction of qdot, we perform a theoretical analysis that yields a deterministic bound on the relative approximation error introduced by qdot. Empirical tests are performed to illustrate the tightness of the derived error bound and to demonstrate the effectiveness of qdot on a synthetic dataset, as well as two scientific benchmarks -- Conjugate Gradient (CG) and the Power method. In particular, using qdot for the dot products in CG can result in a majority of components being perforated or quantized to half precision without increasing the iteration count required for convergence to the same solution as CG using a double precision dot product.
Learning Graph Embeddings for Open World Compositional Zero-Shot Learning

Compositional Zero-Shot learning (CZSL) aims to recognize unseen compositions of state and object visual primitives seen during training. A problem with standard CZSL is the assumption of knowing which unseen compositions will be available at test time. In this work, we overcome this assumption operating on the open world setting, where no limit is imposed on the compositional space at test time, and the search space contains a large number of unseen compositions. To address this problem, we propose a new approach, Compositional Cosine Graph Embedding (Co-CGE), based on two principles. First, Co-CGE models the dependency between states, objects and their compositions through a graph convolutional neural network. The graph propagates information from seen to unseen concepts, improving their representations. Second, since not all unseen compositions are equally feasible, and less feasible ones may damage the learned representations, Co-CGE estimates a feasibility score for each unseen composition, using the scores as margins in a cosine similarity-based loss and as weights in the adjacency matrix of the graphs. Experiments show that our approach achieves state-of-the-art performances in standard CZSL while outperforming previous methods in the open world scenario.
RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users

Object detection using automotive radars has not been explored with deep learning models in comparison to the camera based approaches. This can be attributed to the lack of public radar datasets. In this paper, we collect a novel radar dataset that contains radar data in the form of Range-AzimuthDoppler tensors along with the bounding boxes on the tensor for dynamic road users, category labels, and 2D bounding boxes on the Cartesian Bird-Eye-View range map. To build the dataset, we propose an instance-wise auto-annotation method. Furthermore, a novel Range-Azimuth-Doppler based multiclass object detection deep learning model is proposed. The algorithm is a one-stage anchor-based detector that generates both 3D bounding boxes and 2D bounding boxes on RangeAzimuth-Doppler and Cartesian domains, respectively. Our proposed algorithm achieves 56.3% AP with IOU of 0.3 on 3D bounding box predictions, and 51.6% with IOU of 0.5 on 2D bounding box prediction. Our dataset and the code can be found at https://github.com/ZhangAoCanada/RADDet.git.
Developing an Automatic System for Classifying Chatter About Health Services on Twitter: Case Study for Medicaid

Background The wide adoption of social media in daily life renders it a rich and effective resource for conducting near real-time assessments of consumers’ perceptions of health services. However, its use in these assessments can be challenging because of the vast amount of data and the diversity of content in social media chatter. Objective This study aims to develop and evaluate an automatic system involving natural language processing and machine learning to automatically characterize user-posted Twitter data about health services using Medicaid, the single largest source of health coverage in the United States, as an example. Methods We collected data from Twitter in two ways: via the public streaming application programming interface using Medicaid-related keywords (Corpus 1) and by using the website’s search option for tweets mentioning agency-specific handles (Corpus 2). We manually labeled a sample of tweets in 5 predetermined categories or other and artificially increased the number of training posts from specific low-frequency categories. Using the manually labeled data, we trained and evaluated several supervised learning algorithms, including support vector machine, random forest (RF), naïve Bayes, shallow neural network (NN), k-nearest neighbor, bidirectional long short-term memory, and bidirectional encoder representations from transformers (BERT). We then applied the best-performing classifier to the collected tweets for postclassification analyses to assess the utility of our methods. Results We manually annotated 11,379 tweets (Corpus 1: 9179; Corpus 2: 2200) and used 7930 (69.7%) for training, 1449 (12.7%) for validation, and 2000 (17.6%) for testing. A classifier based on BERT obtained the highest accuracies (81.7%, Corpus 1; 80.7%, Corpus 2) and F1 scores on consumer feedback (0.58, Corpus 1; 0.90, Corpus 2), outperforming the second best classifiers in terms of accuracy (74.6%, RF on Corpus 1; 69.4%, RF on Corpus 2) and F1 score on consumer feedback (0.44, NN on Corpus 1; 0.82, RF on Corpus 2). Postclassification analyses revealed differing intercorpora distributions of tweet categories, with political (400778/628411, 63.78%) and consumer feedback (15073/27337, 55.14%) tweets being the most frequent for Corpus 1 and Corpus 2, respectively. Conclusions The broad and variable content of Medicaid-related tweets necessitates automatic categorization to identify topic-relevant posts. Our proposed system presents a feasible solution for automatic categorization and can be deployed and generalized for health service programs other than Medicaid. Annotated data and methods are available for future studies.
Temporal Dynamic Changes of Intrinsic Brain Activity Associated with Cognitive Reserve in Prodromal Alzheimer's Disease.

BACKGROUND Cognitive reserve (CR) is an important protective factor for Alzheimer's disease (AD), yet its mechanism has not been fully elucidated. OBJECTIVE To explore the effect of CR on resting and dynamic brain intrinsic activity in patients with mild cognitive impairment (MCI). METHODS 65 amyloid-β PET-negative (Aβ-) normal controls (NC) and 30 amyloid-β PET-positive (Aβ+) MCI patients underwent resting-state functional magnetic resonance imaging were included from Alzheimer's Disease Neuroimaging Initiative. According to the years of education, the subjects were divided into high education group and low education group. A two-way analysis of variance was employed for the fractional amplitude of low-frequency fluctuation (fALFF) and dynamic fALFF (dfALFF) comparisons among the four groups. Moreover, the interaction effect of neuroimaging×pathology on clinical cognitive function was tested with linear regression analysis. RESULTS The value of fALFF in the left prefrontal lobe was increased in Aβ+ MCI patients compared to Aβ- NC. The significant interactive effect between disease state and education (binary factor) was observed in the right parahippocampal gyrus (PHG) for fALFF, the right PHG and the right inferior parietal lobule for dfALFF. While no significant results between education (continuous factor) and brain activity was found in voxel-by-voxel analysis. For MCI patients, a significant fluorodeoxyglucose hypometabolic convergence index×right PHG dfALFF interaction was found, indicating the maintenance of executive function at higher levels of dfALFF in the right PHG. CONCLUSION High CR can alleviate the impairment of hypometabolism on executive function in MCI patients, which is partially achieved by regulating the dynamic brain activity in the right PHG.
Stigma as a Barrier to Participant Recruitment of Minority Populations in Diabetes Research: Development of a Community-Centered Recruitment Approach

Background The development of evidence-based care geared towards Black and Latina women living with uncontrolled type 2 diabetes is contingent upon their active recruitment into clinical interventions. Well-documented impediments to recruitment include a historical mistrust of the research community and socioeconomic factors that limit awareness and access to research studies. Although sociocultural and socioeconomic factors deter minorities from participating in clinical research, it is equally important to consider the role of stigma in chronic disease intervention studies. Objective We aim to share our discovery of diabetes-related stigma as an underrecognized impediment to recruitment for the Women in Control 2.0 virtual diabetes self-management education study. Methods Our initial recruitment plan used traditional strategies to recruit minority women with uncontrolled type 2 diabetes, which included letters and phone calls to targeted patients, referrals from clinicians, and posted flyers. After engaging a patient advisory group and consulting with experts in community advocacy, diabetes-related stigma emerged as a prominent barrier to recruitment. The study team reviewed and revised recruitment scripts and outreach material in order to better align with the lived experience and needs of potential enrollees. Results Using a more nuanced, community-centered recruitment approach, we achieved our target recruitment goal, enrolling 309 participants into the study, exceeding our target of 212. Conclusions There is a need for updated recruitment methods that can increase research participation of patients who experience internalized diabetes stigma. To address disparities in minority health, further research is needed to better understand diabetes-related stigma and devise strategies to avert or address it.
Limb preference impacts single‐leg forward hop limb symmetry index values following ACL reconstruction

Following anterior cruciate ligament (ACL) reconstruction limb dominance for performing tasks is not considered when making rehabilitation progression decisions. The purpose of this study was to determine if strength and functional outcomes differ between individuals who injured their preferred or nonpreferred jumping limb and to determine if these same outcomes differ between individuals who injured their preferred or nonpreferred limb used to kick a ball. A secondary purpose was to determine the association of quadriceps strength and single‐leg forward hop performance with patient self‐reported function. Forty individuals with ACL reconstruction (age = 20.0 ± 4.6 years, height = 174.2 ± 12.7 cm, mass = 71.2 ± 12.7 kg, time since surgery = 5.3 ± 0.8 months) were included in the study. Primary outcome measures included, International Knee Documentation Committee Subjective Knee Form (IKDC) scores, quadriceps limb symmetry index (LSI) values, and single‐leg forward hop LSI values. Limb preference was defined two ways, kicking a ball and performing a unilateral jump. There were no significant differences between groups based on injury to the preferred limb to kick a ball for any of the outcome variables. Individuals who injured their nonpreferred jumping limb demonstrated significantly (p = 0.05, d = 0.77) lower single‐leg forward hop LSI values (81.1% ± 19.5%) compared to individuals who injured their preferred jumping limb (94.1% ± 12.6%), but demonstrated no differences in IKDC scores or quadriceps LSI values. Quadriceps LSI and single‐leg forward hop LSI explained 73% of the variance in IKDC scores, but quadriceps LSI had the strongest association (r = 0.790). These findings suggests that limb preference influences single forward hop LSI values and should be considered following ACL reconstruction.