Miracle-dz/UniFAR-FTUs huggingface dataset:
MeshMVS: Multi-View Stereo Guided Mesh Reconstruction

Deep learning based 3D shape generation methods generally utilize latent features extracted from color images to encode the semantics of objects and guide the shape generation process. These color image semantics only implicitly encode 3D information, potentially limiting the accuracy of the generated shapes. In this paper we propose a multi-view mesh generation method which incorporates geometry information explicitly by using the features from intermediate depth representations of multi-view stereo and regularizing the 3D shapes against these depth images. First, our system predicts a coarse 3D volume from the color images by probabilistically merging voxel occupancy grids from the prediction of individual views. Then the depth images from multi-view stereo along with the rendered depth images of the coarse shape are used as a contrastive input whose features guide the refinement of the coarse shape through a series of graph convolution networks. Notably, we achieve superior results than state-of-the-art multi-view shape generation methods with 34% decrease in Chamfer distance to ground truth and 14% increase in F1-score on ShapeNet dataset.
A Backbone Replaceable Fine-tuning Network for Stable Face Alignment

Heatmap regression based face alignment has achieved prominent performance on static images. However, the stability and accuracy are remarkably discounted when applying the existing methods on dynamic videos. We attribute the degradation to random noise and motion blur, which are common in videos. The temporal information is critical to address this issue yet not fully considered in the existing works. In this paper, we visit the video-oriented face alignment problem in two perspectives: detection accuracy prefers lower error for a single frame, and detection consistency forces better stability between adjacent frames. On this basis, we propose a Jitter loss function that leverages temporal information to suppress inaccurate as well as jittered landmarks. The Jitter loss is involved in a novel framework with a fine-tuning ConvLSTM structure over a backbone replaceable network. We further demonstrate that accurate and stable landmarks are associated with different regions with overlaps in a canonical coordinate, based on which the proposed Jitter loss facilitates the optimization process during training. The proposed framework achieves at least 40% improvement on stability evaluation metrics while enhancing detection accuracy versus state-of-the-art methods. Generally, it can swiftly convert a landmark detector for facial images to a better-performing one for videos without retraining the entire model.
Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control

The energy-efficient control of mobile robots is crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces, which cannot be offset by limited on-board resources. An emerging non-Von Neumann model of intelligence, where spiking neural networks (SNNs) are run on neuromorphic processors, is regarded as an energy-efficient and robust alternative to the state-of-the-art real-time robotic controllers for low dimensional control tasks. The challenge now for this new computing paradigm is to scale so that it can keep up with real-world tasks. To do so, SNNs need to overcome the inherent limitations of their training, namely the limited ability of their spiking neurons to represent information and the lack of effective learning algorithms. Here, we propose a population-coded spiking actor network (PopSAN) trained in conjunction with a deep critic network using deep reinforcement learning (DRL). The population coding scheme dramatically increased the representation capacity of the network and the hybrid learning combined the training advantages of deep networks with the energy-efficient inference of spiking networks. To show the general applicability of our approach, we integrated it with a spectrum of both on-policy and off-policy DRL algorithms. We deployed the trained PopSAN on Intel's Loihi neuromorphic chip and benchmarked our method against the mainstream DRL algorithms for continuous control. To allow for a fair comparison among all methods, we validated them on OpenAI gym tasks. Our Loihi-run PopSAN consumed 140 times less energy per inference when compared against the deep actor network on Jetson TX2, and had the same level of performance. Our results support the efficiency of neuromorphic controllers and suggest our hybrid RL as an alternative to deep learning, when both energy-efficiency and robustness are important.
Wireless Localisation in WiFi using Novel Deep Architectures

This paper studies the indoor localisation of WiFi devices based on a commodity chipset and standard channel sounding. First, we present a novel shallow neural network (SNN) in which features are extracted from the channel state information (CSI) corresponding to WiFi subcarriers received on different antennas and used to train the model. The single-layer architecture of this localisation neural network makes it lightweight and easy-to-deploy on devices with stringent constraints on computational resources. We further investigate for localisation the use of deep learning models and design novel architectures for convolutional neural network (CNN) and long-short term memory (LSTM). We extensively evaluate these localisation algorithms for continuous tracking in indoor environments. Experimental results prove that even an SNN model, after a careful handcrafted feature extraction, can achieve accurate localisation. Meanwhile, using a well-organised architecture, the neural network models can be trained directly with raw data from the CSI and localisation features can be automatically extracted to achieve accurate position estimates. We also found that the performance of neural network-based methods are directly affected by the number of anchor access points (APs) regardless of their structure. With three APs, all neural network models proposed in this paper can obtain localisation accuracy of around 0.5 metres. In addition the proposed deep NN architecture reduces the data pre-processing time by 6.5 hours compared with a shallow NN using the data collected in our testbed. In the deployment phase, the inference time is also significantly reduced to 0.1 ms per sample. We also demonstrate the generalisation capability of the proposed method by evaluating models using different target movement characteristics to the ones in which they were trained.
Graphite: Graph-Induced Feature Extraction for Point Cloud Registration

3D Point clouds are a rich source of information that enjoy growing popularity in the vision community. However, due to the sparsity of their representation, learning models based on large point clouds is still a challenge. In this work, we introduce Graphite, a GRAPH-Induced feaTure Extraction pipeline, a simple yet powerful feature transform and keypoint detector. Graphite enables intensive down-sampling of point clouds with keypoint detection accompanied by a descriptor. We construct a generic graph-based learning scheme to describe point cloud regions and extract salient points. To this end, we take advantage of 6D pose information and metric learning to learn robust descriptions and keypoints across different scans. We Reformulate the 3D keypoint pipeline with graph neural networks which allow efficient processing of the point set while boosting its descriptive power which ultimately results in more accurate 3D registrations. We demonstrate our lightweight descriptor on common 3D descriptor matching and point cloud registration benchmarks [76], [71] and achieve comparable results with the state of the art. Describing 100 patches of a point cloud and detecting their keypoints takes only 0.018 seconds with our proposed network.
A Model of Choice with Minimal Compromise.

I formulate and characterize the following two-stage choice behavior. The decision maker is endowed with two preferences. She shortlists all maximal alternatives according to the first preference. If the first preference is decisive, in the sense that it shortlists a unique alternative, then that alternative is the choice. If multiple alternatives are shortlisted, then, in a second stage, the second preference vetoes its minimal alternative in the shortlist, and the remaining members of the shortlist form the choice set. Only the final choice set is observable. I assume that the first preference is a weak order and the second is a linear order. Hence the shortlist is fully rationalizable but one of its members can drop out in the second stage, leading to bounded rational behavior. Given the asymmetric roles played by the underlying binary relations, the consequent behavior exhibits a minimal compromise between two preferences. To our knowledge it is the first Choice function that satisfies Sen's $\beta$ axiom of choice,but not $\alpha$.
Optimism in the Face of Adversity: Understanding and Improving Deep Learning Through Adversarial Robustness

Driven by massive amounts of data and important advances in computational resources, new deep learning systems have achieved outstanding results in a large spectrum of applications. Nevertheless, our current theoretical understanding of the mathematical foundations of deep learning lags far behind its empirical success. However, the field of adversarial robustness has recently become one of the main sources of explanations of our deep models. In this article, we provide an in-depth review of the field and give a self-contained introduction to its main notions. However, in contrast to the mainstream pessimistic perspective of adversarial robustness, we focus on the main positive aspects that it entails. We highlight the intuitive connection between adversarial examples and the geometry of deep neural networks and, eventually, explore how the geometric study of adversarial examples can serve as a powerful tool to understand deep learning. Furthermore, we demonstrate the broad applicability of adversarial robustness, providing an overview of the main emerging applications of adversarial robustness beyond security. The goal of this article is to provide readers with a set of new perspectives to understand deep learning and supply them with intuitive tools and insights on how to use adversarial robustness to improve it.
Bi-Real Net V2: Rethinking Non-linearity for 1-bit CNNs and Going Beyond

Binary neural networks (BNNs), where both weights and activations are binarized into 1 bit, have been widely studied in recent years due to its great benefit of highly accelerated computation and substantially reduced memory footprint that appeal to the development of resource constrained devices. In contrast to previous methods tending to reduce the quantization error for training BNN structures, we argue that the binarized convolution process owns an increasing linearity towards the target of minimizing such error, which in turn hampers BNN's discriminative ability. In this paper, we re-investigate and tune proper non-linear modules to fix that contradiction, leading to a strong baseline which achieves state-of-the-art performance on the large-scale ImageNet dataset in terms of accuracy and training efficiency. To go further, we find that the proposed BNN model still has much potential to be compressed by making a better use of the efficient binary operations, without losing accuracy. In addition, the limited capacity of the BNN model can also be increased with the help of group execution. Based on these insights, we are able to improve the baseline with an additional 4~5% top-1 accuracy gain even with less computational cost. Our code will be made public at this https URL.
Maps for Learning Indexable Classes

We study learning of indexed families from positive data where a learner can freely choose a hypothesis space (with uniformly decidable membership) comprising at least the languages to be learned. This abstracts a very universal learning task which can be found in many areas, for example learning of (subsets of) regular languages or learning of natural languages. We are interested in various restrictions on learning, such as consistency, conservativeness or set-drivenness, exemplifying various natural learning restrictions. Building on previous results from the literature, we provide several maps (depictions of all pairwise relations) of various groups of learning criteria, including a map for monotonicity restrictions and similar criteria and a map for restrictions on data presentation. Furthermore, we consider, for various learning criteria, whether learners can be assumed consistent.
POTENTIAL VALUE AND METHOD OF PRODUCING HAPLOIDS IN THE APPLE TREE, MALUS PUMILA (MILL.)

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Potential value and method of producing haploids in the apple tree, malus pumila (Mill.) Yves Lespinasse, Marie Godicheau, Michel Duron
Bayesian Hierarchical Methods and the Use of Ecological Thresholds and Changepoints for Habitat Selection Models

Modeling the complex relationships between habitat characteristics and a species’ habitat preferences pose many difficult problems for ecological researchers. These problems are complicated further when information is collected over a range of time or space. Additionally, the variety of factors affecting these choices is difficult to understand and even more difficult to accurately collect information about. In light of these concerns, we evaluate the performance of current standard habitat preference models that are based on Bayesian methods and then present some extensions and supplements to those methods that prove to be very useful. More specifically, we demonstrate the value of extending the standard Bayesian hierarchical model using finite mixture model methods. Additionally, we demonstrate that an extension of the Bayesian hierarchical changepoint model to allow for estimating multiple changepoints simultaneously can be very informative when applied to data about multiple habitat locations or species. These models allow the researcher to compare the sites or species with respect to a very specific ecological question and consequently provide definitive answers that are often not available with more commonly used models containing many explanatory factors. Throughout our work we use a complex data set containing information about horseshoe crab spawning habitat preferences in the Delaware Bay over a five-year period. These data epitomize some of the difficult issues inherent to studying habitat preferences. The data are collected over time at many sites, have missing observations, and include explanatory variables that, at best, only provide surrogate information for what researchers feel is important in explaining spawning preferences throughout the bay. We also looked at a smaller data set of freshwater mussel habitat selection preferences in relation to bridge construction on the Kennerdell River in Western Pennsylvania. Together, these two data sets provided us with insight in developing and refining the methods we present. They also help illustrate the strengths and weaknesses of the methods we discuss by assessing their performance in real situations where data are inevitably complex and relationships are difficult to discern.
Spore morphology of Haplopteris C. Presl species (Vittarioideae, Pteridaceae) from China

A comparative study of spores of four species of Haplopteris C. Presl, H. amboinensis (Fee) X.C. Zhang, H. forrestiana (Ching) E.H. Crane, H. linearifolia (Ching) X.C. Zhang, and H. mediosora (Hayata) X.C. Zhang, from China was performed using the method of scanning electronic microscopy (SEM). Spores of Haplopteris species are bilateral monolete ellipsoidal or ellipsoidal, but slightly narrowed in the middle. Perispore thin, easily breakable, smooth; exospore smooth, its surface is finely granulate or finely undulate (as seen at higher magnification). Haplopteris amboinensis have the largest spores (79.5×34.3×40.9 μм), H. mediosora – the smallest one 49.2×24.5×24.9 μм).
Scleractinian micromorphology: Taxonomic value vs. phenotypic plasticity

Zooxanthellate corals (Order: Scleractinia) are undergoing revision due to a discrepancy between the traditional classification and molecular phylogenetics. Traditional taxonomy of Scleractinia relies upon macromorphological features that are visible in hand sample. These characters have been shown to be highly convergent and prone to phenotypic plasticity. By contrast micromorphological characters morphological features examined under scanning electron microscopy (SEM) – have been found to be more congruent with molecular phylogenies. In this paper the stabilities of macromorphological and micromorphological characters are examined by growing corals under controlled conditions using an environmental trigger known for causing changes in morphology (light intensity). Micromussa lordhowensis, Dipsastraea speciosa, and Caulastraea furcata were selected for this analysis. 2D and 3D geometric morphometrics were performed on the experimental specimens to evaluate changes in macromorphology and contrast the change in macromorphology with the change in micromorphology. Changes in micromorphological features were qualitatively assessed. The results show a change in macromorphology, but not in micromorphology. The results confirm previous results in morphological phylogenetics indicating that
Functional properties of CD8+ T cells under lymphopenic conditions

A peripheral pool of T lymphocytes consists of several functionally distinct populations of CD8+ T cells. One of the major surface markers that allow to define different populations of T cells are CD44 and CD62L. Expression profile of these markers depends on the functional status of T lymphocyte. Naive CD8+ T cells express CD62L and do not express CD44 (CD62LhiCD44lo), clones of T cells activated during the primary immune response lose CD62L and express CD44 (CD62LloCD44hi). Central memory CD8+ T cells express both CD44 and CD62L (CD44hiCD62Lhi). However expression of activation markers not always correlate with an antigen experience of T cell. It is known, that in lymphopenic conditions peripheral T cells undergo homeostatic proliferation and acquire the memory like surface phenotype CD44hiCD62Lhi, but data about functional activity of these cells remain controversial. We studied relationship between surface expression of the markers CD44 and CD62L and functional properties of CD8+ T cells under lymphopenic conditions. We proved that surface expression of CD44 in not the only condition for T lymphocyte to acquire the functional properties of memory T-cells. It means that identification of CD8 + T-memory cells based solely on an expression profile of surface markers is not completely correct and requires confirmation by functional tests. Moreover, results of our research may become of practical importance for blood transfusion and bone marrow transplantation.
Investigation of genetic loci associated with parkinson's disease and the functional effect of LRRK2 mutations / Aroma Agape Gopalai

Parkinson’s disease (PD) is a progressive movement disorder which results in bradykinesia, rigidity, resting tremors and postural instability. The role of genetics in modulating the risk of developing PD has been highlighted through genome wide association studies (GWAS) and candidate gene screening. This thesis explores the extent to which variants identified through studies in Caucasian populations are also relevant to the Malaysian PD population. Fourteen variants spanning several different loci including the HLA-DRA, PARK16 and GAK/DGKQ loci and two genes, GRN as well as LRRK2 were screened. The HLA-DRA locus had a protective association with an odds ratio (OR) of 0.76 while the variants in PARK16 had OR values between 0.71 and 0.86. An association with GRN was not detectable in our Malaysian cohort but did show a significant risk association when a meta-analysis was performed with other ethnically matched PD cohorts. Five LRRK2 mutations (G2385R, R1628P, A419V, N551K and R1398H) were screened. The G2385R and R1628P mutations were found to be risk factors. We excluded A419V as a risk factor, and determined it as a rare variant in the Malaysian population. The N551K and R1398H mutations showed significant protective effects with OR values of 0.623 and 0.699 respectively. As the postulated protective role of N551K and R1398H has not been fully characterised functionally, we sought to further investigate this effect. Constructs of LRRK2 mutations (G2019S, N551K and R1398H) were transfected into human neuroblastoma (SH-SY5Y) cells. In cell viability assays, cells carrying the N551K and R1398H mutations were found to confer greater protection when exposed to cellular stress under hydrogen peroxide treatment compared to G2019S. As a kinase, LRRK2 is known to activate pathways that are triggered by cellular stress, initiating a cascade of cell death. In keeping with this, cells carrying the risk factor G2019S mutation showed higher kinase activity and lower cell viability, while the protective factor R1398H had the lowest kinase activity and iv higher cell viability. In addition, although R1398H is located on a domain that is responsible for GTPase activity, our data suggests that there is no significant effect on the GTP binding ability. Although cells expressing protective factor N551K showed high cell viability, the kinase and GTP binding activity was unaltered suggesting that it may use alternative pathway to confer protection. Collectively, this thesis represents the first investigation into genetic loci for PD in Malaysia and has revealed some insight into how selected variants within LRRK2 may influence protective mechanisms within neurons when exposed to cellular stress. Furthermore, while we are not yet at the stage of being able to provide diagnostic testing for late onset PD, the establishment of a Malaysian PD DNA bank achieved as part of this project will provide an invaluable resource for further genetic studies on the contribution of newly identified loci in our cohort.
EFFECT OF TEMPERATURE ON STERILITY IN CMS LINES OF PIGEONPEA [Cajanus cajan(L.) Millsp.]

Present study includes five CMS lines of Pigeonpea with A4 cytoplasm at two locations viz. ICRISAT Patancheru (Telangana) and BAU Sabour (Bihar). Observations on each plant for pollen sterility were recorded at the onset of flowering in September. The second round of sterility evaluation was done in December. The final observations were recorded in February. Result of study revealed that higher temperature of ICRISAT Patancheru (Telangana) have positive effect on the sterility level and higher sterility was reported in all CMS lines(> 90%) whereas at BAU Sabour temperature respectively low so sterility level was also low (< 90%).