Miracle-dz/UniFAR-FTUs huggingface dataset:
OPAL-Net: A Generative Model for Part-based Object Layout Generation

We propose OPAL-Net, a novel hierarchical architecture for part-based layout generation of objects from multiple categories using a single unified model. We adopt a coarse-to-fine strategy involving semantically conditioned autoregressive generation of bounding box layouts and pixel-level part layouts for objects. We use Graph Convolutional Networks, Deep Recurrent Networks along with custom-designed Conditional Variational Autoencoders to enable flexible, diverse and category-aware generation of object layouts. We train OPAL-Net on PASCAL-Parts dataset. The generated samples and corresponding evaluation scores demonstrate the versatility of OPAL-Net compared to ablative variants and baselines.
Locally Differentially Private (Contextual) Bandits Learning

We study locally differentially private (LDP) bandits learning in this paper. First, we propose simple black-box reduction frameworks that can solve a large family of context-free bandits learning problems with LDP guarantee. Based on our frameworks, we can improve previous best results for private bandits learning with one-point feedback, such as private Bandits Convex Optimization etc, and obtain the first results for Bandits Convex Optimization (BCO) with multi-point feedback under LDP. LDP guarantee and black-box nature make our frameworks more attractive in real applications compared with previous specifically designed and relatively weaker differentially private (DP) context-free bandits algorithms. Further, we also extend our algorithm to Generalized Linear Bandits with regret bound $\tilde{\mathcal{O}}(T^{3/4}/\varepsilon)$ under $(\varepsilon, \delta)$-LDP which is conjectured to be optimal. Note given existing $\Omega(T)$ lower bound for DP contextual linear bandits (Shariff&Sheffe,NeurIPS2018), our result shows a fundamental difference between LDP and DP contextual bandits learning.
Squaring the circle: reconciling fishing and conservation of aquatic ecosystems

Size-at-entry regulations in fisheries cause major disruption to aquatic ecosystems, including truncation of age- and size-structures, destabilization of fish stocks, directional selection on phenotypic traits and a by-catch of unwanted species and sizes. Here, we use simple dynamic models of size-spectra to examine an alternative, so-called balanced harvesting. Balanced harvesting helps in retaining the approximate power-law size-structure of natural ecosystems, whereas size-at-entry regulations do not. Balanced harvesting is less likely to destabilize steady states than size-at-entry regulations set close to the size at maturation. Surprisingly, our numerical results suggest that steady-state biomass yield can be substantially increased by switching from size-at-entry to balanced harvesting. On the basis of these results, we argue that the goals of conservation and of greater yields seem less difficult to reconcile than have previously been thought. However, to work towards these goals require a change in our approach to fishing.
A General-Purpose Dehazing Algorithm based on Local Contrast Enhancement Approaches

Dehazing is in the image processing and computer vision communities, the task of enhancing the image taken in foggy conditions. To better understand this type of algorithm, we present in this document a dehazing method which is suitable for several local contrast adjustment algorithms. We base it on two filters. The first filter is built with a step of normalization with some other statistical tricks while the last represents the local contrast improvement algorithm. Thus, it can work on both CPU and GPU for real-time applications. We hope that our approach will open the door to new ideas in the community. Other advantages of our method are first that it does not need to be trained, then it does not need additional optimization processing. Furthermore, it can be used as a pre-treatment or post-processing step in many vision tasks. In addition, it does not need to convert the problem into a physical interpretation, and finally that it is very fast. This family of defogging algorithms is fairly simple, but it shows promising results compared to state-of-the-art algorithms based not only on a visual assessment but also on objective criteria.
TrueAdapt: Learning Smooth Online Trajectory Adaptation with Bounded Jerk, Acceleration and Velocity in Joint Space

We present TrueAdapt, a model-free method to learn online adaptations of robot trajectories based on their effects on the environment. Given sensory feedback and future waypoints of the original trajectory, a neural network is trained to predict joint accelerations at regular intervals. The adapted trajectory is generated by linear interpolation of the predicted accelerations, leading to continuously differentiable joint velocities and positions. Bounded jerks, accelerations and velocities are guaranteed by calculating the valid acceleration range at each decision step and clipping the network's output accordingly. A deviation penalty during the training process causes the adapted trajectory to follow the original one. Smooth movements are encouraged by penalizing high accelerations and jerks. We evaluate our approach by training a simulated KUKA iiwa robot to balance a ball on a plate while moving and demonstrate that the balancing policy can be directly transferred to a real robot with little impact on performance.
Estimation of the Number of Spiked Eigenvalues in a Covariance Matrix by Bulk Eigenvalue Matching Analysis

ABSTRACT The spiked covariance model has gained increasing popularity in high-dimensional data analysis. A fundamental problem is determination of the number of spiked eigenvalues, K. For estimation of K, most attention has focused on the use of top eigenvalues of sample covariance matrix, and there is little investigation into proper ways of using bulk eigenvalues to estimate K. We propose a principled approach to incorporating bulk eigenvalues in the estimation of K. Our method imposes a working model on the residual covariance matrix, which is assumed to be a diagonal matrix whose entries are drawn from a gamma distribution. Under this model, the bulk eigenvalues are asymptotically close to the quantiles of a fixed parametric distribution. This motivates us to propose a two-step method: the first step uses bulk eigenvalues to estimate parameters of this distribution, and the second step leverages these parameters to assist the estimation of K. The resulting estimator aggregates information in a large number of bulk eigenvalues. We show the consistency of under a standard spiked covariance model. We also propose a confidence interval estimate for K. Our extensive simulation studies show that the proposed method is robust and outperforms the existing methods in a range of scenarios. We apply the proposed method to analysis of a lung cancer microarray dataset and the 1000 Genomes dataset.
Transcription-Enriched Joint Embeddings for Spoken Descriptions of Images and Videos

In this work, we propose an effective approach for training unique embedding representations by combining three simultaneous modalities: image and spoken and textual narratives. The proposed methodology departs from a baseline system that spawns a embedding space trained with only spoken narratives and image cues. Our experiments on the EPIC-Kitchen and Places Audio Caption datasets show that introducing the human-generated textual transcriptions of the spoken narratives helps to the training procedure yielding to get better embedding representations. The triad speech, image and words allows for a better estimate of the point embedding and show an improving of the performance within tasks like image and speech retrieval, even when text third modality, text, is not present in the task.
Structure of Pelagic Food Chain and Relationship Between Plankton and Fish Production

Further observations on the standing stocks of pelagic organisms confirm the occurrence of approximately equal biomass over logarithmically equal size ranges. A simple theoretical framework is developed that shows that the structural elements of the pelagic ecosystem can be described in terms of the sizes of predator and prey and of the efficiencies of their interactions. In practice this means that if the standing stock at any size range is known, the standing stock at any other size can be estimated, and if the growth rate at this size is known, the production can be estimated. The theory is tested on three fisheries. For the Gulf of Maine and the North Sea, phytoplankton production is estimated from fishery production. For the area off Peru the fishery production is estimated from the plankton production. Key words: pelagic ecosystem, predator–prey relationships, plankton production, marine fisheries, Peru, North Sea, Gulf of Maine
Programmable In-Network Obfuscation of Traffic

Recent advances in programmable switch hardware offer a fresh opportunity to protect user privacy. This paper presents PINOT, a lightweight in-network anonymity solution that runs at line rate within the memory and processing constraints of hardware switches. PINOT encrypts a client's IPv4 address with an efficient encryption scheme to hide the address from downstream ASes and the destination server. PINOT is readily deployable, requiring no end-user software or cooperation from networks other than the trusted network where it runs. We implement a PINOT prototype on the Barefoot Tofino switch, deploy PINOT in a campus network, and present results on protecting user identity against public DNS, NTP, and WireGuard VPN services.
Motivating Good Practices for the Creation of Contiguous Area Cartograms

Cartograms are maps in which the areas of regions (e.g., countries or provinces) are proportional to a thematic mapping variable (e.g., population or gross domestic product). A cartogram is called contiguous if it keeps geographically adjacent regions connected. Over the past few years, several web tools have been developed for the creation of contiguous cartograms. However, most of these tools do not advise how to use cartograms correctly. To mitigate these shortcomings, we attempt to establish good practices through our recently developed web application this http URL: (1) use cartograms to show numeric data that add up to an interpretable total, (2) present a cartogram alongside a conventional map that uses the same color scheme, (3) indicate whether the data for a region are missing, (4) include a legend so that readers can infer the magnitude of the mapping variable, (5) if a cartogram is presented electronically, assist readers with interactive graphics.
Analog ensemble data assimilation and a method for constructing analogs with variational autoencoders

It is proposed to use analogs of the forecast mean to generate an ensemble of perturbations for use in ensemble optimal interpolation (EnOI) or ensemble variational (EnVar) methods. A new method of constructing analogs using variational autoencoders (VAEs; a machine learning method) is proposed. The resulting analog methods using analogs from a catalog (AnEnOI), and using constructed analogs (cAnEnOI), are tested in the context of a multiscale Lorenz‐‘96 model, with standard EnOI and an ensemble square root filter for comparison. The use of analogs from a modestly‐sized catalog is shown to improve the performance of EnOI, with limited marginal improvements resulting from increases in the catalog size. The method using constructed analogs (cAnEnOI) is found to perform as well as a full ensemble square root filter, and to be robust over a wide range of tuning parameters.
A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty

Accurate rotation estimation is at the heart of robot perception tasks such as visual odometry and object pose estimation. Deep neural networks have provided a new way to perform these tasks, and the choice of rotation representation is an important part of network design. In this work, we present a novel symmetric matrix representation of the 3D rotation group, SO(3), with two important properties that make it particularly suitable for learned models: (1) it satisfies a smoothness property that improves convergence and generalization when regressing large rotation targets, and (2) it encodes a symmetric Bingham belief over the space of unit quaternions, permitting the training of uncertainty-aware models. We empirically validate the benefits of our formulation by training deep neural rotation regressors on two data modalities. First, we use synthetic point-cloud data to show that our representation leads to superior predictive accuracy over existing representations for arbitrary rotation targets. Second, we use image data collected onboard ground and aerial vehicles to demonstrate that our representation is amenable to an effective out-of-distribution (OOD) rejection technique that significantly improves the robustness of rotation estimates to unseen environmental effects and corrupted input images, without requiring the use of an explicit likelihood loss, stochastic sampling, or an auxiliary classifier. This capability is key for safety-critical applications where detecting novel inputs can prevent catastrophic failure of learned models.
Cholinergic inhibition of muscle contraction in Hymenolepis diminuta (Cestoda)

Spontaneous contractions of the longitudinal musculature from the strobilae of the tapeworm Hymenolepis diminuta were measured by means of a force transducer. The addition of cholinergic agonists and antagonists to the bathing medium did not affect intact worms but strips of worm tissue were sensitive to these drugs. Acetylcholine and cholinergic agonists inhibited muscle contraction while cholinergic antagonists stimulated contraction. Both nicotine and muscarine were effective in suppressing muscle contraction and the receptor probably has mixed nicotinic and muscaric properties. Biochemical assays revealed the presence of choline and acetylcholine, and activities of choline acetyltransferase and acetylcholinesterase in worm tissues.
WattsApp: Power-Aware Container Scheduling

Containers are popular for deploying workloads. However, there are limited software-based methods (hardware- based methods are expensive) for obtaining the power consumed by containers to facilitate power-aware container scheduling. This paper presents WattsApp, a tool underpinned by a six step software-based method for power-aware container scheduling to minimize power cap violations on a server. The proposed method relies on a neural network-based power estimation model and a power capped container scheduling technique. Experimental studies are pursued in a lab-based environment on 10 benchmarks on Intel and ARM processors. The results highlight that power estimation has negligible overheads - nearly 90% of all data samples can be estimated with less than a 10% error, and the Mean Absolute Percentage Error (MAPE) is less than 6%. The power-aware scheduling of WattsApp is more effective than Intel’s Running Power Average Limit (RAPL) based power capping as it does not degrade the performance of all running containers.
How to Recognize Actionable Static Code Warnings (Using Linear SVMs)

Static code warning tools often generate warnings that programmers ignore. Such tools can be made more useful via data mining algorithms that select the "actionable" warnings; i.e. the warnings that are usually not ignored. But what is the best way to build those selection algorithms? To answer that question, we learn predictors for 5,675 actionable warnings seen in 31,058 static code warnings from FindBugs. Several data mining methods perform very well on this task. For example, linear Support Vector Machine achieved median recalls of 96%; median false alarms of 2%; and AUC(TNR, TPR) of over 99%. Other learners (tree-based methods and deep learning) achieved very similar results (usually, within 4% or less). On investigation, we found the reason for all these learners performing very well: the data was intrinsically very simple. Specifically, while our data sets have up to 58 raw features, those features can be approximated by less than two underlying dimensions. For such intrinsically simple data, many different kinds of learners can generate useful models with similar performance. Based on the above, we conclude that it is both simple and effective to use data mining algorithms for selecting "actionable" warnings from static code analysis tools. Also, we recommend using linear SVMs to implement that selecting process (since, at least in our sample, that learner ran relatively quickly and achieved the best all-around performance). Further, for any analytics task, it important to match the complexity of the inference to the complexity of the data. For example, we would not recommend deep learning for finding actionable static code warnings since our data is intrinsically very simple.
Production and properties of Aspergillus niger inulinase

An inulinase producing Aspergillus niger strain was isolated from Compositae rhizosphere soil samples. High inulinase levels were produced on a corn steep liquor (CSL)-maltose medium in the absence of inulin at 28°C within 110 h of fermentation. Media based on CSL-sucrose yielded high cell-bound inulinase activity; on inulin-based media the enzyme was mainly extracellularly produced. Both crude extra-and intracellular inulinase preparations displayed identical pH and temperature optima with maximal activity at pH 4.3–4.4 and at 55–56°C. These properties are favourable in view of large scale inulinase application for pure fructose production. High operation temperatures would avoid microbial contamination of reactors and would allow the use of high inulin-substrate concentrations, a limiting factor in obtaining high conversion ratios. The remarkably low pH optimum prevents colour formation and undesirable chemical side reactions. An advantageous low ratio of invertase to inulinase activity (S/I value) of 0.85 was found for the crude extracellular enzyme preparation. Crude inulin (chicory) extracts are hydrolysed faster than pure inulin. Apart from inulin (100% hydrolysis), sucrose (45%) and raffinose (20%) are also hydrolysed, and no liberation of oligomers or of sucrose from inulin was observed. These facts indicate that the A. niger enzyme is an exo-acting inulinase. The above characteristics make this A. niger inulinase an industrially attractive enzyme for the preparation of pure fructose from inulin-containing agricultural crops.
SSumM: Sparse Summarization of Massive Graphs

Given a graph G and the desired size k in bits, how can we summarize G within k bits, while minimizing the information loss? Large-scale graphs have become omnipresent, posing considerable computational challenges. Analyzing such large graphs can be fast and easy if they are compressed sufficiently to fit in main memory or even cache. Graph summarization, which yields a coarse-grained summary graph with merged nodes, stands out with several advantages among graph compression techniques. Thus, a number of algorithms have been developed for obtaining a concise summary graph with little information loss or equivalently small reconstruction error. However, the existing methods focus solely on reducing the number of nodes, and they often yield dense summary graphs, failing to achieve better compression rates. Moreover, due to their limited scalability, they can be applied only to moderate-size graphs. In this work, we propose SSumM, a scalable and effective graph-summarization algorithm that yields a sparse summary graph. SSumM not only merges nodes together but also sparsifies the summary graph, and the two strategies are carefully balanced based on the minimum description length principle. Compared with state-of-the-art competitors, SSumM is (a) Concise: yields up to 11.2X smaller summary graphs with similar reconstruction error, (b) Accurate: achieves up to 4.2X smaller reconstruction error with similarly concise outputs, and (c) Scalable: summarizes 26X larger graphs while exhibiting linear scalability. We validate these advantages through extensive experiments on 10 real-world graphs.
An Efficient Planar Bundle Adjustment Algorithm

This paper presents an efficient algorithm for the least-squares problem using the point-to-plane cost, which aims to jointly optimize depth sensor poses and plane parameters for 3D reconstruction. We call this least-squares problem Planar Bundle Adjustment (PBA), due to the similarity between this problem and the original Bundle Adjustment (BA) in visual reconstruction. As planes ubiquitously exist in the man-made environment, they are generally used as landmarks in SLAM algorithms for various depth sensors. PBA is important to reduce drift and improve the quality of the map. However, directly adopting the well-established BA framework in visual reconstruction will result in a very inefficient solution for PBA. This is because a 3D point only has one observation at a camera pose. In contrast, a depth sensor can record hundreds of points in a plane at a time, which results in a very large nonlinear least-squares problem even for a small-scale space. The main contribution of this paper is an efficient solution for the PBA problem using the point-to-plane cost. We introduce a reduced Jacobian matrix and a reduced residual vector, and prove that they can replace the original Jacobian matrix and residual vector in the generally adopted Levenberg-Marquardt (LM) algorithm. This significantly reduces the computational cost. Besides, when planes are combined with other features for 3D reconstruction, the reduced Jacobian matrix and residual vector can also replace the corresponding parts derived from planes. Our experimental results show that our algorithm can significantly reduce the computational time compared to the solution using the traditional BA framework. In addition, our algorithm is faster, more accurate, and more robust to initialization errors compared to the start-of-the-art solution using the plane-to-plane cost [3].
The Binding Ability of α-Lactalbumin and β-Lactoglobulin to Mutagenic Heterocyclic Amines

Abstract The binding ability of bovine milk proteins with mutagenic heterocyclic amines was investigated. Binding was determined with 2mg of β-lactoglobulin and 20µg of heterocyclic amine in . 4ml of pH 7.4, 50m M phosphate buffer, at 37°C, in a shaker for 10min. The unbound heterocyclic amine in protein-free ultrafiltrate was analyzed by HPLC method. The binding of α-lactalbumin, β-lactoglobulin A and β-lactoglobulin B were 90.44, 81.38 and 89.18%, respectively, with 3-amino-l, 4-dimethyl- 5H -pyrido[4,3- b ]indole; 37.85, 34.04, and 43.90%, respectively, with 3-amino-l-methyl- 5H -pyrido[4,3- b ]indole; and 49.11, 43.25, and 57.44%. respectively, with 2-amino-6-methyldipyrido[1,2- a :3',2'- d ] imidazole. Binding of β-lactoglobulin and α-lactalbumin to 3-amino-1,4-dimethyl- 5H -pyrido[4,3- b ]indole and 3-amino-1-methyl- 5H -pyrido [4,3- b ]-indole was higher at pH conditions above 7.4, and binding was lost at pH less than 5.5. Maximum binding of both proteins to 2-amino-6-methyl-dipyrido[1,2- a 3',2'- d ]imidazole was at pH 7.4, and binding was inhibited at pH conditions above 8.5 and less than 6.5.