Miracle-dz/UniFAR-FTUs huggingface dataset:
A Unified Mathematical Approach for Foraging and Construction Systems in a 1, 000, 000 Robot Swarm

Automation in construction is possible with systems designed using the swarm robotic principles of scalability, flexibility, robustness, and emergence. We derive quantitative measurements of these principles in 10,000 robot swarms as a first step in achieving this goal. We summarize our recent task allocation work in the context of an object gathering task and demonstrate its feasibility in the context of automated construction tasks. We present a trajectory to extend our current task allocation methodology using stochastic processes in order to present a unified approach to task allocation in swarm-robotic construction
Pose-preserving Cross Spectral Face Hallucination

To narrow the inherent sensing gap in heterogeneous face recognition (HFR), recent methods have resorted to generative models and explored the ? recognition via generation? framework. Even though, it remains a very challenging task to synthesize photo-realistic visible faces (VIS) from near-infrared (NIR) images especially when paired training data are unavailable. We present an approach to avert the data misalignment problem and faithfully preserve pose, expression and identity information during cross-spectral face hallucination. At the pixel level, we introduce an unsupervised attention mechanism to warping that is jointly learned with the generator to derive pixel-wise correspondence from unaligned data. At the image level, an auxiliary generator is employed to facilitate the learning of mapping from NIR to VIS domain. At the domain level, we first apply the mutual information constraint to explicitly measure the correlation between domains and thus benefit synthesis. Extensive experiments on three heterogeneous face datasets demonstrate that our approach not only outperforms current state-of-the-art HFR methods but also produce visually appealing results at a high resolution.
Hierarchical Diffusion Attention Network

A series of recent studies formulated the diffusion prediction problem as a sequence prediction task and proposed several sequential models based on recurrent neural networks. However, non-sequential properties exist in real diffusion cascades, which do not strictly follow the sequential assumptions of previous work. In this paper, we propose a hierarchical diffusion attention network (HiDAN), which adopts a non-sequential framework and two-level attention mechanisms, for diffusion prediction. At the user level, a dependency attention mechanism is proposed to dynamically capture historical user-to-user dependencies and extract the dependency-aware user information. At the cascade (i.e., sequence) level, a time-aware influence attention is designed to infer possible future user's dependencies on historical users by considering both inherent user importance and time decay effects. Significantly higher effectiveness and efficiency of HiDAN over state-of-the-art sequential models are demonstrated when evaluated on three real diffusion datasets. The further case studies illustrate that HiDAN can accurately capture diffusion dependencies.
Cap-and-Trade Emissions Regulation: A Strategic Analysis

Cap-and-trade schemes are designed to achieve target levels of regulated emissions in a socially efficient manner. These schemes work by issuing regulatory credits and allowing firms to buy and sell them according to their relative compliance costs. Analyzing the efficacy of such schemes in concentrated industries is complicated by the strategic interactions among firms producing heterogeneous products. We tackle this complexity via an agent-based microeconomic model of the US market for personal vehicles. We calculate Nash equilibria among credits-trading strategies in a variety of scenarios and regulatory models. We find that while cap-and-trade results improves efficiency overall, consumers bear a disproportionate share of regulation cost, as firms use credit trading to segment the vehicle market. Credits trading volume decreases when firms behave more strategically, which weakens the segmentation effect.
Self-supervised learning of predictive segmentation models from video. (Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos)

Les modeles predictifs ont le potentiel de permettre le transfert des succes recents en apprentissage par renforcement a de nombreuses tâches du monde reel, en diminuant le nombre d’interactions necessaires avec l’environnement.La tâche de prediction video a attire un interet croissant de la part de la communaute ces dernieres annees, en tant que cas particulier d’apprentissage predictif dont les applications en robotique et dans les systemes de navigations sont vastes.Tandis que les trames RGB sont faciles a obtenir et contiennent beaucoup d’information, elles sont extremement difficile a predire, et ne peuvent etre interpretees directement par des applications en aval.C’est pourquoi nous introduisons ici une tâche nouvelle, consistant a predire la segmentation semantique ou d’instance de trames futures.Les espaces de descripteurs que nous considerons sont mieux adaptes a la prediction recursive, et nous permettent de developper des modeles de segmentation predictifs performants jusqu’a une demi-seconde dans le futur.Les predictions sont interpretables par des applications en aval et demeurent riches en information, detaillees spatialement et faciles a obtenir, en s’appuyant sur des methodes etat de l’art de segmentation.Dans cette these, nous nous attachons d’abord a proposer pour la tâche de segmentation semantique, une approche discriminative se basant sur un entrainement par reseaux antagonistes.Ensuite, nous introduisons la tâche nouvelle de prediction de segmentation semantique future, pour laquelle nous developpons un modele convolutionnel autoregressif.Enfin, nous etendons notre methode a la tâche plus difficile de prediction de segmentation d’instance future, permettant de distinguer entre differents objets.Du fait du nombre de classes variant selon les images, nous proposons un modele predictif dans l’espace des descripteurs d’image convolutionnels haut niveau du reseau de segmentation d’instance Mask R-CNN.Cela nous permet de produire des segmentations visuellement plaisantes en haute resolution, pour des scenes complexes comportant un grand nombre d’objets, et avec une performance satisfaisante jusqu’a une demi seconde dans le futur.
Sketched Iterative Algorithms for Structured Generalized Linear Models

Recent years have seen advances in optimizing large scale statistical estimation problems. In statistical learning settings iterative optimization algorithms have been shown to enjoy geometric convergence. While powerful, such results only hold for the original dataset, and may face computational challenges when the sample size is large. In this paper, we study sketched iterative algorithms, in particular sketched-PGD (projected gradient descent) and sketched-SVRG (stochastic variance reduced gradient) for structured generalized linear model, and illustrate that these methods continue to have geometric convergence to the statistical error under suitable assumptions. Moreover, the sketching dimension is allowed to be even smaller than the ambient dimension, thus can lead to significant speed-ups. The sketched iterative algorithms introduced provide an additional dimension to study the trade-offs between statistical accuracy and time.
Towards a dynamic vision system: computational modelling of insect motion sensitive neural systems

For motion perception, vision plays an irreplaceable role, which can extract more abundant useful movement features from an unpredictable dynamic environment compared to other sensing modalities. Nowadays, building a dynamic vision system for motion perception in a both reliable and efficient manner is still an open challenge. Millions of years of evolutionary development has provided, in nature, animals that possess robust vision systems capable of motion perception to deal with a variety of aspects of life. Insects, in particular, have a relatively smaller number of visual neurons compared to vertebrates and humans, but can still navigate smartly through visually cluttered and dynamic environments. Understanding the insects' visual processing pathways and methods thus are not only attractive to neural system modellers but also critical in providing effective solutions for future intelligent machines. Originated from biological researches in insect visual systems, this thesis investigates computational modelling of motion sensitive neural systems and potential applications to robotics. This proposes novel modelling of the locust and fly visual systems for sensing looming and translating stimuli. Specifically, the proposed models comprise collision selective neural networks of two lobula giant movement detectors (LGMD1 and LGMD2) in locusts, and translating sensitive neural networks of direction selective neurons (DSNs) in flies, as well as hybrid visual neural systems of their combinations. In all these proposed models, the functionality of ON and OFF pathways is highlighted, which separate visual processing into parallel computation. This works effectively to realise neural characteristics of both the LGMD1 and the LGMD2 in locusts and plays crucial roles in separating the different looming selectivity between the two visual neurons. Such a biologically plausible structure can also implement the fly DSNs for translational movements perception and guide fast motion tracking with a behavioural response to visual fixation. The effectiveness and flexibility of the proposed motion sensitive neural systems have been validated by systematic and comparative experiments ranging from off-line synthetic and real-world tests to on-line bio-robotic tests. The underlying characteristics and functionality of the locust LGMDs and the fly DSNs have been achieved by the proposed models. All the proposed visual models have been successfully realised on the embedded system in a vision-based ground mobile robot. The robot tests have verified the computational simplicity and efficiency of proposed bio-inspired methodologies, which hit at great potential of building neuromorphic sensors in autonomous machines for motion perception in a fast, reliable and low-energy manner.
Incremental Elicitation of Rank-Dependent Aggregation Functions based on Bayesian Linear Regression

We introduce a new model-based incremental choice procedure for multicriteria decision support, that interleaves the analysis of the set of alternatives and the elicitation of weighting coefficients that specify the role of criteria in rank-dependent models such as ordered weighted averages (OWA) and Choquet integrals. Starting from a prior distribution on the set of weighting parameters, we propose an adaptive elicitation approach based on the minimization of the expected regret to iteratively generate preference queries. The answers of the Decision Maker are used to revise the current distribution until a solution can be recommended with sufficient confidence. We present numerical tests showing the interest of the proposed approach.
Neural Networks for Predicting Human Interactions in Repeated Games

We consider the problem of predicting human players' actions in repeated strategic interactions. Our goal is to predict the dynamic step-by-step behavior of individual players in previously unseen games. We study the ability of neural networks to perform such predictions and the information that they require. We show on a dataset of normal-form games from experiments with human participants that standard neural networks are able to learn functions that provide more accurate predictions of the players' actions than established models from behavioral economics. The networks outperform the other models in terms of prediction accuracy and cross-entropy, and yield higher economic value. We show that if the available input is only of a short sequence of play, economic information about the game is important for predicting behavior of human agents. However, interestingly, we find that when the networks are trained with long enough sequences of history of play, action-based networks do well and additional economic details about the game do not improve their performance, indicating that the sequence of actions encode sufficient information for the success in the prediction task.
VulSniper: Focus Your Attention to Shoot Fine-Grained Vulnerabilities

With the explosive development of information technology, vulnerabilities have become one of the major threats to computer security. Most vulnerabilities with similar patterns can be detected effectively by static analysis methods. However, some vulnerable and non-vulnerable code is hardly distinguishable, resulting in low detection accuracy. In this paper, we define the accurate identification of vulnerabilities in similar code as a fine-grained vulnerability detection problem. We propose VulSniper which is designed to detect fine-grained vulnerabilities more effectively. In VulSniper, attention mechanism is used to capture the critical features of the vulnerabilities. Especially, we use bottom-up and top-down structures to learn the attention weights of different areas of the program. Moreover, in order to fully extract the semantic features of the program, we generate the code property graph, design a 144-dimensional vector to describe the relation between the nodes, and finally encode the program as a feature tensor. VulSniper achieves F1-scores of 80.6% and 73.3% on the two benchmark datasets, the SARD Buffer Error dataset and the SARD Resource Management Error dataset respectively, which are significantly higher than those of the state-of-the-art methods.
FakeTables: Using GANs to Generate Functional Dependency Preserving Tables with Bounded Real Data

In many cases, an organization wishes to release some data, but is restricted in the amount of data to be released due to legal, privacy and other concerns. For instance, the US Census Bureau releases only 1% of its table of records every year, along with statistics about the entire table. However, the machine learning (ML) models trained on the released sub-table are usually sub-optimal. In this paper, our goal is to find a way to augment the sub-table by generating a synthetic table from the released sub-table, under the constraints that the generated synthetic table (i) has similar statistics as the entire table, and (ii) preserves the functional dependencies of the released sub-table. We propose a novel generative adversarial network framework called ITS-GAN, where both the generator and the discriminator are specifically designed to satisfy these two constraints. By evaluating the augmentation performance of ITS-GAN on two representative datasets, the US Census Bureau data and US Bureau of Transportation Statistics (BTS) data, we show that ITS-GAN yields high quality classification results, and significantly outperforms various state-of-the-art data augmentation approaches.
Graph Convolutional Networks on User Mobility Heterogeneous Graphs for Social Relationship Inference

Inferring social relations from user trajectory data is of great value in real-world applications such as friend recommendation and ride-sharing. Most existing methods predict relationship based on a pairwise approach using some hand-crafted features or rely on a simple skip-gram based model to learn embeddings on graphs. Using hand-crafted features often fails to capture the complex dynamics in human social relations, while the graph embedding based methods only use random walks to propagate information and cannot incorporate external semantic data provided. We propose a novel model that utilizes Graph Convolutional Networks (GCNs) to learn user embeddings on the User Mobility Heterogeneous Graph in an unsupervised manner. This model is capable of propagating relation layer-wisely as well as combining both the rich structural information in the heterogeneous graph and predictive node features provided. Our method can also be extended to a semi-supervised setting if a part of the social network is available. The evaluation on three real-world datasets demonstrates that our method outperforms the state-of-the-art approaches.
Network Embedding under Partial Monitoring for Evolving Networks

Network embedding has been extensively studied in recent years. In addition to the works on static networks, some researchers try to propose new models for evolving networks. However, sometimes most of these dynamic network embedding models are still not in line with the actual situation, since these models have a strong assumption that we can achieve all the changes in the whole network, while in fact we cannot do this in some real world networks, such as the web networks and some large social networks. So in this paper, we study a novel and challenging problem, i.e., network embedding under partial monitoring for evolving networks. We propose a model on dynamic networks in which we cannot perceive all the changes of the structure. We analyze our model theoretically, and give a bound to the error between the results of our model and the potential optimal cases. We evaluate the performance of our model from two aspects. The experimental results on real world datasets show that our model outperforms the baseline models by a large margin.
Is Trust Important in Technology Adoption? A Policy Capturing Approach

The study of trust in the context of technology adoption is one of initial trust formation. Due to the lack of complete information, an individual must make a ‘leap of faith’ when committing to the new technology. This leap of faith is trust. This study uses institution-based trust theory to link the characteristics of a new technology to the trusting stance of the potential adopter. This trusting stance is then linked to the technology adoption decision. The research model was tested using policy capturing. Policy capturing is a simulation-based decision modeling technique that allows the researcher to observe a simulation of actual decisions being made. The questionnaire was developed and administered to 181 undergraduate students. The results of this exploratory study suggest that trust is an important component in technology acceptance and adoption and that this construct should be incorporated into future research which focuses on technology adoption.
From cachexia to obesity: the role of host metabolism in cancer immunotherapy.

PURPOSE OF REVIEW Currently, several clinical trials in cancer therapy have demonstrated the success of immunomodulatory therapies. However, only a variable fraction of patients actually benefit from these treatments. The understanding of key mechanisms behind this response heterogeneity is one of the major unmet need and intense research field in immuno-oncology. This review will discuss the host metabolic dysfunctions derived from cachexia or obesity that can affect the response to cancer immunotherapy. RECENT FINDINGS Preclinical studies demonstrated that chronic inflammation, nutritional intake impairment and endocrine dysfunction may affect anticancer innate and adaptive immunity, both in cachexia and obesity. New emerging clinical findings have highlighted the impact of metabolic biomarkers in predicting response to immune checkpoint inhibitors in cancer patients. SUMMARY Patient's weight and inflammatory status could be relevant in the clinical decision-making process before starting cancer immunotherapy and for an effective patient selection and stratification in future clinical trials employing this class of anticancer agents.
Development of the paratympanic pneumatic system of Japanese quail

Avian heads are characterized as having two extensive air‐filled systems lined with epithelia; the paranasal and paratympanic sinuses. Many diverticula derived from the paratympanic sinus system are known to reticulate with each other to form a single merged pneumatic space within the adult braincase. However, the development of these complex branching and reticulating epithelia has not been examined in detail. In this study, we describe the comprehensive developmental pattern of the paratympanic sinus and its associated soft tissues in a model bird, Japanese quail (Coturnix japonica). The data are derived from three‐dimensional reconstructions based on histological sections and soft tissue enhanced micro‐CT data. Those data provide the foundation of the complex hierarchical developmental pattern of the paratympanic sinus system. Moreover, associations with other tissues help establish key morphologies that identify each pneumatic entity. This study clarifies the developmental relationships of the ventral portions of the paratympanic sinus system, the siphoneal diverticulum and marginal sinus, based on the ligaments associated with the Eustachian tube. In addition, detailed histological pneumatic morphologies reveal hitherto unknown epithelial diversity, which may be indicative of equally complex developmental processes. We use the pneumatization of the quadrate as an example to support a close relationship with vascular growth and pneumatic epithelia invasion into ossified bone. We confirm pneumatic diverticula never enter into cartilages, possibly due to the absence of vasculature in these tissues. Lastly, we use the concept of a morphogenetic tree as a tool to help present the complex developmental pattern of the paratympanic sinus system and apply it toward inferring pneumatic morphologies in a nonavian theropod braincase.
Potential Role of Cartilage Oligomeric Matrix Protein in the Modulation of Pulmonary Arterial Smooth Muscle Superoxide by Hypoxia.

Changes in reactive oxygen species and extracellular matrix seem to participate in pulmonary hypertension development. Since we recently reported evidence for chronic hypoxia decreasing expression of cartilage oligomeric matrix protein (COMP) and evidence for this controlling loss of pulmonary arterial smooth muscle BMP receptor-2 (BMPR2) and contractile phenotype proteins, we examined if changes in superoxide metabolism could be an important factor in a bovine pulmonary artery (BPA), organoid cultured under hypoxia for 48hrs model. Hypoxia (3% oxygen) caused a depletion of COMP in BPA, but not in bovine coronary arteries. Knockdown of COMP by siRNA increased BPA levels of mitochondrial and extra-mitochondrial superoxide detected by MitoSOX and dihydroethidium (DHE) HPLC products. COMP siRNA treated BPA showed reduced levels of SOD2 and SOD3 and increased levels of NADPH oxidases NOX2 and NOX4. Hypoxia increased BPA levels of MitoSOX detected superoxide, and caused changes in NOX2 and SOD2 expression similar to COMP siRNA, and exogenous COMP (0.5μM) prevented the effects of hypoxia. In the presence of COMP, BMPR2 siRNA treated BPA showed increases in superoxide detected by MitoSOX and depletion of SOD2. Scavenging superoxide (0.5μM TEMPO or mitoTEMPO) maintained the expression of contractile phenotype proteins Calponin and SM22α decreased by 48hrs hypoxia (1% oxygen). Adenoviral delivery of BMPR2 to rat pulmonary artery smooth muscle cells prevented the depletion of Calponin and SM22α by COMP siRNA. Thus, COMP regulation of BMPR2 appears to have an important role in controlling hypoxia-elicited changes in BPA superoxide and its potential regulation of contractile phenotype proteins.
Glial Dysfunction in MeCP2 Deficiency Models: Implications for Rett Syndrome

Rett syndrome (RTT) is a rare, X-linked neurodevelopmental disorder typically affecting females, resulting in a range of symptoms including autistic features, intellectual impairment, motor deterioration, and autonomic abnormalities. RTT is primarily caused by the genetic mutation of the Mecp2 gene. Initially considered a neuronal disease, recent research shows that glial dysfunction contributes to the RTT disease phenotype. In the following manuscript, we review the evidence regarding glial dysfunction and its effects on disease etiology.