Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneously Localize, Segment and Rank the Camouflaged Objects

Camouflage is a key defence mechanism across species that is critical to survival. Common strategies for camouflage include background matching, imitating the color and pattern of the environment, and disruptive coloration, disguising body outlines [37]. Camouflaged object detection (COD) aims to segment camouflaged objects hiding in their surroundings. Existing COD models are built upon binary ground truth to segment the camouflaged objects without illustrating the level of camouflage. In this paper, we revisit this task and argue that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage and evolution of animals, but also provide guidance to design more sophisticated camouflage techniques. Furthermore, we observe that it is some specific parts of the camouflaged objects that make them detectable by predators. With the above understanding about camouflaged objects, we present the first ranking based COD network (Rank-Net) to simultaneously localize, segment and rank camouflaged objects. The localization model is proposed to find the discriminative regions that make the camouflaged object obvious. The segmentation model segments the full scope of the camouflaged objects. Further, the ranking model infers the detectability of different camouflaged objects. Moreover, we contribute a large COD testing set to evaluate the generalization ability of COD models. Experimental results show that our model achieves new state-of-the-art, leading to a more interpretable COD network1.
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation

Reinforcement learning in cooperative multi-agent settings has recently advanced significantly in its scope, with applications in cooperative estimation for advertising, dynamic treatment regimes, distributed control, and federated learning. In this paper, we discuss the problem of cooperative multi-agent RL with function approximation, where a group of agents communicates with each other to jointly solve an episodic MDP. We demonstrate that via careful message-passing and cooperative value iteration, it is possible to achieve near-optimal no-regret learning even with a fixed constant communication budget. Next, we demonstrate that even in heterogeneous cooperative settings, it is possible to achieve Pareto-optimal no-regret learning with limited communication. Our work generalizes several ideas from the multi-agent contextual and multi-armed bandit literature to MDPs and reinforcement learning.
Novelty Detection in Sequential Data by Informed Clustering and Modeling

Novelty detection in discrete sequences is a challenging task, since deviations from the process generating the normal data are often small or intentionally hidden. Novelties can be detected by modeling normal sequences and measuring the deviations of a new sequence from the model predictions. However, in many applications data is generated by several distinct processes so that models trained on all the data tend to over-generalize and novelties remain undetected. We propose to approach this challenge through decomposition: by clustering the data we break down the problem, obtaining simpler modeling task in each cluster which can be modeled more accurately. However, this comes at a trade-off, since the amount of training data per cluster is reduced. This is a particular problem for discrete sequences where state-of-the-art models are data-hungry. The success of this approach thus depends on the quality of the clustering, i.e., whether the individual learning problems are sufficiently simpler than the joint problem. While clustering discrete sequences automatically is a challenging and domain-specific task, it is often easy for human domain experts, given the right tools. In this paper, we adapt a state-of-the-art visual analytics tool for discrete sequence clustering to obtain informed clusters from domain experts and use LSTMs to model each cluster individually. Our extensive empirical evaluation indicates that this informed clustering outperforms automatic ones and that our approach outperforms state-of-the-art novelty detection methods for discrete sequences in three real-world application scenarios. In particular, decomposition outperforms a global model despite less training data on each individual cluster.
Lead and zinc removal with storage period in porous asphalt pavement

Porous asphalt pavements have been used as an effective technique to overcome road runoff challenges, and to improve efficiency of rainwater utilisation in urban areas. Using porous asphalt pavements with reservoir storage and harvesting facilities is an important consideration for the future. This study monitored changes in water quality indicators, such as pH, conductivity, and concentrations of lead and zinc, for water stored in porous asphalt pavement models with basalt-, limestone- and ‘basalt+limestone’-filled reservoir structures. The research discusses findings over a 696-h storage period following artificial rainfall. Total lead and zinc concentrations were remarkably reduced throughout the initial flush, showing, on average, reductions of 90% and 80.5%, respectively. This pattern was consistent throughout the storage period, producing average reductions in lead and zinc of 99.98% and 79%, respectively, over 696 h. Conductivity and pH levels increased in all pavement models after the 696-h storage. The results obtained confirmed the potential of using porous asphalt pavements with reservoir structures to remove heavy metals from road runoff. This can be applied to future research on the removal mechanisms of porous asphalt pavements in relation to heavy metals in road runoff. Keywords : heavy metals, road runoff, porous asphalt pavement, retention
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding

We study the problem of learning conditional average treatment effects (CATE) from high-dimensional, observational data with unobserved confounders. Unobserved confounders introduce ignorance -- a level of unidentifiability -- about an individual's response to treatment by inducing bias in CATE estimates. We present a new parametric interval estimator suited for high-dimensional data, that estimates a range of possible CATE values when given a predefined bound on the level of hidden confounding. Further, previous interval estimators do not account for ignorance about the CATE associated with samples that may be underrepresented in the original study, or samples that violate the overlap assumption. Our interval estimator also incorporates model uncertainty so that practitioners can be made aware of out-of-distribution data. We prove that our estimator converges to tight bounds on CATE when there may be unobserved confounding, and assess it using semi-synthetic, high-dimensional datasets.
Comparative metrology of the evaporimeter and the DermaLab® TEWL probe

Transepidermal water loss (TEWL) measurements are one of the most frequently utilized of the noninvasive bioengineering techniques. Recently, Cortex Technology (Hadsund, Denmark) introduced the DermaLab® system, which can be equipped with a TEWL probe. It is based on the vapor pressure gradient estimation method of Nilsson and, thus, is quite similar to the Servo Med evaporimeter. In this paper, we compare these two commercial instruments under identical experimental conditions using both in vitro studies with physical standards as well as in vivo studies employing human volunteers.
Learning to Generate 3D Shapes with Generative Cellular Automata

We present a probabilistic 3D generative model, named Generative Cellular Automata, which is able to produce diverse and high quality shapes. We formulate the shape generation process as sampling from the transition kernel of a Markov chain, where the sampling chain eventually evolves to the full shape of the learned distribution. The transition kernel employs the local update rules of cellular automata, effectively reducing the search space in a high-resolution 3D grid space by exploiting the connectivity and sparsity of 3D shapes. Our progressive generation only focuses on the sparse set of occupied voxels and their neighborhood, thus enabling the utilization of an expressive sparse convolutional network. We propose an effective training scheme to obtain the local homogeneous rule of generative cellular automata with sequences that are slightly different from the sampling chain but converge to the full shapes in the training data. Extensive experiments on probabilistic shape completion and shape generation demonstrate that our method achieves competitive performance against recent methods.
Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models

The boom of DL technology leads to massive DL models built and shared, which facilitates the acquisition and reuse of DL models. For a given task, we encounter multiple DL models available with the same functionality, which are considered as candidates to achieve this task. Testers are expected to compare multiple DL models and select the more suitable ones w.r. t. the whole testing context. Due to the limitation of labeling effort, testers aim to select an efficient subset of samples to make an as precise rank estimation as possible for these models. To tackle this problem, we propose Sample Discrimination based Selection (SDS) to select efficient samples that could discriminate multiple models, i.e., the prediction behaviors (right/wrong) of these samples would be helpful to indicate the trend of model performance. To evaluate SDS, we conduct an extensive empirical study with three widely-used image datasets and 80 real world DL models. The experiment results show that, compared with state-of-the-art baseline methods, SDS is an effective and efficient sample selection method to rank multiple DL models.
The distance backbone of complex networks

Redundancy needs more precise characterization as it is a major factor in the evolution and robustness of networks of multivariate interactions. We investigate the complexity of such interactions by inferring a connection transitivity that includes all possible measures of path length for weighted graphs. The result, without breaking the graph into smaller components, is a distance backbone subgraph sufficient to compute all shortest paths. This is important for understanding the dynamics of spread and communication phenomena in real-world networks. The general methodology we formally derive yields a principled graph reduction technique and provides a finer characterization of the triangular geometry of all edges—those that contribute to shortest paths and those that do not but are involved in other network phenomena. We demonstrate that the distance backbone is very small in large networks across domains ranging from air traffic to the human brain connectome, revealing that network robustness to attacks and failures seems to stem from surprisingly vast amounts of redundancy.
Iterative Program Synthesis for Adaptable Social Navigation

Robot social navigation is influenced by human preferences and environment-specific scenarios such as elevators and doors, thus necessitating end-user adaptability. State-of-the-art approaches to social navigation fall into two categories: model-based social constraints and learning-based approaches. While effective, these approaches have fundamental limitations – model-based approaches require constraint and parameter tuning to adapt to preferences and new scenarios, while learning-based approaches require reward functions, significant training data, and are hard to adapt to new social scenarios or new domains with limited demonstrations.In this work, we propose Iterative Dimension Informed Program Synthesis (IDIPS) to address these limitations by learning and adapting social navigation in the form of human-readable symbolic programs. IDIPS works by combining pro-gram synthesis, parameter optimization, predicate repair, and iterative human demonstration to learn and adapt model-free action selection policies from orders of magnitude less data than learning-based approaches. We introduce a novel predicate repair technique that can accommodate previously unseen social scenarios or preferences by growing existing policies.We present experimental results showing that IDIPS: 1) synthesizes effective policies that model user preference, 2) can adapt existing policies to changing preferences, 3) can extend policies to handle novel social scenarios such as locked doors, and 4) generates policies that can be transferred from simulation to real-world robots with minimal effort.
Structure of the Nuttall partition for some class of four-sheeted Riemann surfaces

The structure of a Nuttall partition into sheets of some class of four-sheeted Riemann surfaces is studied. The corresponding class of multivalued analytic functions is a special class of algebraic functions of fourth order generated by the function inverse to the Zhukovskii function. We show that in this class of four-sheeted Riemann surfaces, the boundary between the second and third sheets of the Nuttall partition of the Riemann surface, is completely characterized in terms of an extremal problem posed on the two-sheeted Riemann surface of the function w defined by the equation w = z − 1. In particular, we show that in this class of functions the boundary between the second and third sheets does not intersect both the boundary between the first and second sheets and the boundary between the third and fourth sheets. Bibliography: [34] titles.
Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks

Attention based neural networks are state of the art in a large range of applications. However, their performance tends to degrade when the number of layers increases. In this work, we show that enforcing Lipschitz continuity by normalizing the attention scores can significantly improve the performance of deep attention models. First, we show that, for deep graph attention networks (GAT), gradient explosion appears during training, leading to poor performance of gradient-based training algorithms. To address this issue, we derive a theoretical analysis of the Lipschitz continuity of attention modules and introduce LipschitzNorm, a simple and parameter-free normalization for self-attention mechanisms that enforces the model to be Lipschitz continuous. We then apply LipschitzNorm to GAT and Graph Transformers and show that their performance is substantially improved in the deep setting (10 to 30 layers). More specifically, we show that a deep GAT model with LipschitzNorm achieves state of the art results for node label prediction tasks that exhibit long-range dependencies, while showing consistent improvements over their unnormalized counterparts in benchmark node classification tasks.
Composition and Bioactivities of the Leaf Essential Oils of Cinnamomum subavenium Miq. from Taiwan

Abstract Leaves of Cinnamomum subavenium Miq. were collected from two sites in Taiwan. Leaf essential oils of C. sub-avenium were isolated using hydrodistillation and headspace-GC methods to determine their composition and yield. Antioxidant and antimicrobial activities of various oils obtained were also evaluated. Forty-four and 88 compounds were identified in the leaf oils obtained. Whereas 26 and 65 compounds were identified by headspace-GC method, respectively. The main headspace components were comparable to those of the oil. The main components of one of the oils were methyl eugenol (75.9%), linalool (7.3%), and eugenol (6.6%); while those from the other oil were p-cymene (21.6%), 1,8-cineole (16.5%), and linalool (11.9%). Both leaf oils had excellent antioxidant and antimicrobial activities.
Experimental investigation of rotating instability in a contra-rotating axial flow compressor

Abstract The rotating instability in a contra-rotating axial flow compressor is investigated by experiments. Twenty-four pressure sensors were installed on the casing to capture the unsteady flow in the rotor tip region simultaneously. A double-phase-locking technique suitable for the contra-rotating compressor was proposed to characterise the static pressure contours of the rotor tip. The mean and root-mean-square pressure contours indicate that rotating instability occurs before the rotating stall happened, and the rotor tip clearance vortex is located upstream of the rear rotor leading edge plane before stall. Fourier spectrum shows that rotating instability and rotating stall both happened under the stall condition, and the frequency band of rotating instability does not change with the flow rate. In the front rotor, the frequency of rotating instability is half of the blade passing frequency. It is verified that the modal estimation method can be implemented by using the average azimuthal phase velocity, which significantly reduced the number of pressure sensors required. Modal estimation results show that each peak of the rotating instability frequency band corresponds to a unique dominant circumferential mode. By optimising average azimuthal phase velocity, an improved modal estimation method is obtained, which can further improve the reliability of the modal estimation results.
REMARK ON THE LOCAL NATURE OF METRIC MEAN DIMENSION

Metric mean dimension is a metric invariant of dynamical systems. It is a dynamical analogue of Minkowski dimension of metric spaces. We explain that old ideas of Bowen (1972) can be used for clarifying the local nature of metric mean dimension. We also explain the generalization to R-actions and an illustrating example.
CDLNet: Robust and Interpretable Denoising Through Deep Convolutional Dictionary Learning

Deep learning based methods hold state-of-the-art results in image denoising, but remain difficult to interpret due to their construction from poorly understood building blocks such as batch-normalization, residual learning, and feature domain processing. Unrolled optimization networks propose an interpretable alternative to constructing deep neural networks by deriving their architecture from classical iterative optimization methods, without use of tricks from the standard deep learning tool-box. So far, such methods have demonstrated performance close to that of state-of-the-art models while using their interpretable construction to achieve a comparably low learned parameter count. In this work, we propose an unrolled convolutional dictionary learning network (CDLNet) and demonstrate its competitive denoising performance in both low and high parameter count regimes. Specifically, we show that the proposed model outperforms the state-of-the-art denoising models when scaled to similar parameter count. In addition, we leverage the model's interpretable construction to propose an augmentation of the network's thresholds that enables state-of-the-art blind denoising performance and near-perfect generalization on noise-levels unseen during training.
Contracts for acquiring information

Information is costly for an agent to acquire and unobservable to a principal. We show that any Pareto optimal contract has a decomposition into a fraction of output, a state-dependent transfer, and an optimal distortion. Under this decomposition: 1) the fraction of output paid is increasing in the set of experiments available to the agent, 2) the statedependent transfer indexes contract payments to account for differences in output between states, 3) the optimal distortion exploits complementarities in the cost of information acquisition: experiment probabilities unalterable via contract payments stuck against liability limits are substituted for, the substitution occurring according to complementarities in the cost of information acquisition, and 4) if and only if the agent’s cost of experimentation is mutual information, the optimal distortion takes the form of a decision-dependent transfer.
Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization

The scarcity of labeled data often impedes the application of deep learning to the segmentation of medical images. Semi-supervised learning seeks to overcome this limitation by leveraging unlabeled examples in the learning process. In this paper, we present a novel semi-supervised segmentation method that leverages mutual information (MI) on categorical distributions to achieve both global representation invariance and local smoothness. In this method, we maximize the MI for intermediate feature embeddings that are taken from both the encoder and decoder of a segmentation network. We first propose a global MI loss constraining the encoder to learn an image representation that is invariant to geometric transformations. Instead of resorting to computationally-expensive techniques for estimating the MI on continuous feature embeddings, we use projection heads to map them to a discrete cluster assignment where MI can be computed efficiently. Our method also includes a local MI loss to promote spatial consistency in the feature maps of the decoder and provide a smoother segmentation. Since mutual information does not require a strict ordering of clusters in two different assignments, we incorporate a final consistency regularization loss on the output which helps align the cluster labels throughout the network. We evaluate the method on four challenging publicly-available datasets for medical image segmentation. Experimental results show our method to outperform recently-proposed approaches for semi-supervised segmentation and provide an accuracy near to full supervision while training with very few annotated images.
Voting in Transfer Learning System for Ground-Based Cloud Classification

Cloud classification is a great challenge in meteorological research. The different types of clouds, currently known and present in our skies, can produce radioactive effects that impact the variation of atmospheric conditions, with consequent strong dominance over the earth’s climate and weather. Therefore, identifying their main visual features becomes a crucial aspect. In this paper, the goal is to adopt pretrained deep neural networks-based architecture for clouds image description, and subsequently, classification. The approach is pyramidal. Proceeding from the bottom up, it partially extracts previous knowledge of deep neural networks related to original task and transfers it to the new task. The updated knowledge is integrated in a voting context to provide a classification prediction. The framework trains the neural models on unbalanced sets, a condition that makes the task even more complex, and combines the provided predictions through statistical measures. An experimental phase on different cloud image datasets is performed, and the results achieved show the effectiveness of the proposed approach with respect to state-of-the-art competitors.
BEhavioral Leakage and IntEr-cycle Variability Emulator model for ReRAMs (BELIEVER)

Emerging electronic devices are promising to drive the performance of computer systems to new heights, against the notable saturation in traditional transistor-based architectures. Among them, resistive RAM -- or ReRAM -- has attracted a lot of attention among scientists since its practical realization was reported in 2008 and numerous devices, circuits and systems, and also models have been described in the literature. However, behavioral models fail to reproduce device parameter variations and the drift of device state in the absence of a stimulus. This shortcoming substantially reduces the practical relevance of systems and circuits designed with existing models. The work at hand deals with the development of a behavioral model that integrates device parameter variation and state drift based on data collected from our measurements of real devices. As we show in this paper, BELIEVER model enables engineers to conduct more reliable and meaningful design and simulations of circuits and systems that use ReRAMs.