Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis to Deployment: Cyber-Physical Control Architectures

We consider the problem of how to deploy a controller to a (networked) cyber-physical system (CPS). Controlling a CPS is an involved task, and synthesizing a controller to respect sensing, actuation, and communication constraints is only part of the challenge. In addition to controller synthesis, one should also consider how the controller will be incorporated within the CPS. Put another way, the cyber layer and its interaction with the physical layer need to be taken into account. In this work, we aim to bridge the gap between theoretical controller synthesis and practical CPS deployment. We adopt the system level synthesis (SLS) framework to synthesize a controller, either state-feedback or output-feedback, and provide deployment architectures for the standard SLS controllers. Furthermore, we derive new controller realizations for open-loop stable systems and introduce different state-feedback and output-feedback architectures for deployment, ranging from fully centralized to fully distributed. Finally, we compare the trade-offs among them in terms of robustness, memory, computation, and communication overhead.
Large-scale magnetic field structure of NGC 3627 based on a magnetic vector map

We analyzed the data of Stokes I, Q, and U in the C and X bands and investigated the large-scale magnetic field structure of NGC 3627. The polarization intensity and angle in each band were derived using Stokes Q and U maps. The rotation measure was calculated using polarization angle maps. Moreover, the magnetic field strength was calculated by assuming energy equipartition with cosmic ray electrons. The structure of the magnetic field was well aligned with the spiral arms, which were consistent with those in the former studies. We applied the magnetic vector reconstruction method to NGC 3627 to derive a magnetic vector map, which showed that the northern and southern disks were dominant with inward and outward magnetic vectors, respectively. Furthermore, we considered the large-scale structure of the magnetic field in NGC 3627 and observed that the structure is bi-symmetric spiral in nature, and that the number of magnetic field modes is mB = 1 in the outer region of galaxy. In addition, NGC 3627 has a mode of two spiral arms that were clearly visible in an optical image. The ratio of the mode of the spiral arms to that of the magnetic field is 2 : 1. In terms of NGC 3627, the large-scale magnetic field may be generated via the parametric resonance induced by the gravitational potential of the spiral arms.
Model-agnostic Fits for Understanding Information Seeking Patterns in Humans

In decision making tasks under uncertainty, humans display characteristic biases in seeking, integrating, and acting upon information relevant to the task. Here, we reexamine data from previous carefully designed experiments, collected at scale, that measured and catalogued these biases in aggregate form. We design deep learning models that replicate these biases in aggregate, while also capturing individual variation in behavior. A key finding of our work is that paucity of data collected from each individual subject can be overcome by sampling large numbers of subjects from the population, while still capturing individual differences. We predict human behavior with high accuracy without making any assumptions about task goals, reward structure, or individual biases, thus providing a model-agnostic fit to human behavior in the task. Such an approach can sidestep potential limitations in modeler-specified inductive biases, and has implications for computational modeling of human cognitive function in general, and of human-AI interfaces in particular.
Long Term Motion Prediction Using Keyposes

Long term human motion prediction is essential in safety-critical applications such as human-robot interaction and autonomous driving. In this paper we show that to achieve long term forecasting, predicting human pose at every time instant is unnecessary. Instead, it is more effective to predict a few keyposes and approximate intermediate ones by interpolating the keyposes. We demonstrate that our approach enables us to predict realistic motions for up to 5 seconds in the future, which is far longer than the typical 1 second encountered in the literature. Furthermore, because we model future keyposes probabilistically, we can generate multiple plausible future motions by sampling at inference time. Over this extended time period, our predictions are more realistic, more diverse and better preserve the motion dynamics than those state-of-the-art methods yield.
Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering

In this paper, we present an approach to tactile pose estimation from the first touch for known objects. First, we create an object-agnostic map from real tactile observations to contact shapes. Next, for a new object with known geometry, we learn a tailored perception model completely in simulation. To do so, we simulate the contact shapes that a dense set of object poses would produce on the sensor. Then, given a new contact shape obtained from the sensor output, we match it against the pre-computed set using the object-specific embedding learned purely in simulation using contrastive learning. This results in a perception model that can localize objects from a single tactile observation. It also allows reasoning over pose distributions and including additional pose constraints coming from other perception systems or multiple contacts. We provide quantitative results for four objects. Our approach provides high accuracy pose estimations from distinctive tactile observations while regressing pose distributions to account for those contact shapes that could result from different object poses. We further extend and test our approach in multi-contact scenarios where several tactile sensors are simultaneously in contact with the object. Website: this http URL
Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments

Localizing the camera in a known indoor environment is a key building block for scene mapping, robot navigation, AR, etc. Recent advances estimate the camera pose via optimization over the 2D/3D-3D correspondences established between the coordinates in 2D/3D camera space and 3D world space. Such a mapping is estimated with either a convolution neural network or a decision tree using only the static input image sequence, which makes these approaches vulnerable to dynamic indoor environments that are quite common yet challenging in the real world. To address the aforementioned issues, in this paper, we propose a novel outlier-aware neural tree which bridges the two worlds, deep learning and decision tree approaches. It builds on three important blocks: (a) a hierarchical space partition over the indoor scene to construct the decision tree; (b) a neural routing function, implemented as a deep classification network, employed for better 3D scene understanding; and (c) an outlier rejection module used to filter out dynamic points during the hierarchical routing process. Our proposed algorithm is evaluated on the RIO-10 benchmark developed for camera relocalization in dynamic indoor environments. It achieves robust neural routing through space partitions and outperforms the state-of-the-art approaches by around 30% on camera pose accuracy, while running comparably fast for evaluation.
A multi-period multi-product stochastic inventory problem with order-based loan

This paper investigates a multi-product stochastic inventory problem in which a cash-constrained online retailer can adopt order-based loan provided by some Chinese e-commerce platforms to speed up its cash recovery for deferred revenue. We first build deterministic models for the problem and then develop the corresponding stochastic programming models to maximize the retailers' expected profit over the planning horizon. The uncertainty of customer demand is represented by scenario trees, and a scenario reduction technique is used to solve the problem when the scenario trees are too large. We conduct numerical tests based on real data crawling from an online store. The results show that the stochastic model outperforms the deterministic model, especially when the retailer is less cash-constrained. Moreover, the retailer tends to choose using order-based loan when its initial available cash is small or facing long receipt delay length.
Page Tables: Keeping them Flat and Hot (Cached)

As memory capacity has outstripped TLB coverage, applications that use large data sets suffer from frequent page table walks. The underlying problem is that traditional tree-based page tables are designed to save memory capacity through the use of fine-grained indirection, which is a poor match for today's systems which have significant memory capacity but high latencies for indirections. In this work we reduce the penalty of page table walks by both reducing the number of indirections required for page walks and by reducing the number of main memory accesses required for the indirections. We achieve this by first flattening the page table by allocating page table nodes in large pages. This results in fewer levels in the page table, which reduces the number of indirections needed to walk it. While the use of large pages in the page table does waste memory capacity for unused entries, this cost is small compared to using large pages for data as the page table itself is far smaller. Second we prioritize caching page table entries during phases of high TLB misses. This slightly increases data misses, but does so during phases of low-locality, and the resulting decrease in page walk latency outweighs this loss. By flattening the page table we are able to reduce the number of indirections needed for a page walk from 4 to 2 in a non-virtualized system and from 24 to 8 under virtualization. However, the actual effect is much smaller as the Page Walker/Paging Structure Cache already captures most locality in the first two levels of the page table. Overall we are able to reduce the number of main memory accesses per page walk from 1.6 to 1.0 for non-virtualized systems and from 4.6 to 3.0 for virtualized systems.
TaskTracker-tool: A Toolkit for Tracking of Code Snapshots and Activity Data During Solution of Programming Tasks

The process of writing code and use of features in an integrated development environment (IDE) is a fruitful source of data in computing education research. Existing studies use records of students' actions in the IDE, consecutive code snapshots, compilation events, and others, to gain deep insight into the process of student programming. In this paper, we present a set of tools for collecting and processing data of student activity during problem-solving. The first tool is a plugin for IntelliJ-based IDEs (PyCharm, IntelliJ IDEA, CLion). By capturing snapshots of code and IDE interaction data, it allows to analyze the process of writing code in different languages --- Python, Java, Kotlin, and C++. The second tool is designed for the post-processing of data collected by the plugin and is capable of basic analysis and visualization. To validate and showcase the toolkit, we present a dataset collected by our tools. It consists of records of activity and IDE interaction events during solution of programming tasks by 148 participants of different ages and levels of programming experience. We propose several directions for further exploration of the dataset.
Pyxel: the collaborative detection simulation framework

Pyxel is a novel python tool for end-to-end detection chain simulation i.e. from detector optical effects to readout electronics effects. It is an easy-to-use framework to host and pipeline any detector effect model. It is suited for simulating both Charge-Coupled Devices, CMOS Image Sensors and Mercury Cadmium Telluride hybridized arrays. It is conceived as a collaborative tool to promote reusability, knowledge transfer, and reliability in the instrumentation community. We will provide a demonstration of Pyxel’s basic principles, describe newly added capabilities and the main models already implemented, and give examples of more advanced applications.
Implicit Regularization in ReLU Networks with the Square Loss

Understanding the implicit regularization (or implicit bias) of gradient descent has recently been a very active research area. However, the implicit regularization in nonlinear neural networks is still poorly understood, especially for regression losses such as the square loss. Perhaps surprisingly, we prove that even for a single ReLU neuron, it is impossible to characterize the implicit regularization with the square loss by any explicit function of the model parameters (although on the positive side, we show it can be characterized approximately). For one hidden-layer networks, we prove a similar result, where in general it is impossible to characterize implicit regularization properties in this manner, except for the "balancedness" property identified in Du et al. [2018]. Our results suggest that a more general framework than the one considered so far may be needed to understand implicit regularization for nonlinear predictors, and provides some clues on what this framework should be.
Robust Facial Landmark Detection by Multiorder Multiconstraint Deep Networks

Recently, heatmap regression has been widely explored in facial landmark detection and obtained remarkable performance. However, most of the existing heatmap regression-based facial landmark detection methods neglect to explore the high-order feature correlations, which is very important to learn more representative features and enhance shape constraints. Moreover, no explicit global shape constraints have been added to the final predicted landmarks, which leads to a reduction in accuracy. To address these issues, in this article, we propose a multiorder multiconstraint deep network (MMDN) for more powerful feature correlations and shape constraints’ learning. Especially, an implicit multiorder correlating geometry-aware (IMCG) model is proposed to introduce the multiorder spatial correlations and multiorder channel correlations for more discriminative representations. Furthermore, an explicit probability-based boundary-adaptive regression (EPBR) method is developed to enhance the global shape constraints and further search the semantically consistent landmarks in the predicted boundary for robust facial landmark detection. It is interesting to show that the proposed MMDN can generate more accurate boundary-adaptive landmark heatmaps and effectively enhance shape constraints to the predicted landmarks for faces with large pose variations and heavy occlusions. Experimental results on challenging benchmark data sets demonstrate the superiority of our MMDN over state-of-the-art facial landmark detection methods.
Edited Media Understanding: Reasoning About Implications of Manipulated Images

Multimodal disinformation, from `deepfakes' to simple edits that deceive, is an important societal problem. Yet at the same time, the vast majority of media edits are harmless -- such as a filtered vacation photo. The difference between this example, and harmful edits that spread disinformation, is one of intent. Recognizing and describing this intent is a major challenge for today's AI systems. We present the task of Edited Media Understanding, requiring models to answer open-ended questions that capture the intent and implications of an image edit. We introduce a dataset for our task, EMU, with 48k question-answer pairs written in rich natural language. We evaluate a wide variety of vision-and-language models for our task, and introduce a new model PELICAN, which builds upon recent progress in pretrained multimodal representations. Our model obtains promising results on our dataset, with humans rating its answers as accurate 40.35% of the time. At the same time, there is still much work to be done -- humans prefer human-annotated captions 93.56% of the time -- and we provide analysis that highlights areas for further progress.
Modular Analysis of Tree-Topology Models

We investigate networks of automata that synchronise over common action labels. A graph synchronisation topology between the automata is defined in such a way that two automata are connected iff they can synchronise over an action. We show a very effective reduction of networks of automata with tree-like synchronisation topologies. The reduction preserves a certain form of reachability, but not safety. The procedure is implemented in an open-source tool.
The hierarchical structure of galactic haloes: classification and characterization with halo-optics

We build upon Ordering Points To Identify the Clustering Structure (optics ), a hierarchical clustering algorithm well known to be a robust data miner, in order to produce halo-optics , an algorithm designed for the automatic detection and extraction of all meaningful clusters between any two arbitrary sizes. We then apply halo-optics to the 3D spatial positions of halo particles within four separate synthetic Milky Way-type galaxies, classifying the stellar and dark matter structural hierarchies. Through visualization of the halo-optics output, we compare its structure identification to the state-of-the-art galaxy/(sub)halo finder VELOCIraptor , finding excellent agreement even though halo-optics does not consider kinematic information in this current implementation. We conclude that halo-optics is a robust hierarchical halo finder, although its determination of lower spatial-density features such as the tails of streams could be improved with the inclusion of extra localized information such as particle kinematics and stellar metallicity into its distance metric.
An improved spectral clustering method for mixed membership community detection

Community detection has been well studied recent years, but the more realistic case of mixed membership community detection remains a challenge. Here, we develop an efficient spectral algorithm Mixed-ISC based on applying more than K eigenvectors for clustering given K communities for estimating the community memberships under the degree-corrected mixed membership (DCMM) model. We show that the algorithm is asymptotically consistent. Numerical experiments on both simulated networks and many empirical networks demonstrate that Mixed-ISC performs well compared to a number of benchmark methods for mixed membership community detection. Especially, Mixed-ISC provides satisfactory performances on weak signal networks.
Freezing-induced removal of water from phospholipid head groups in biomembranes

The aim of this study was to investigate the driving forces causing freezing-induced gel phase formation in cellular membranes. Fourier transform infrared spectroscopy (FTIR) was used to study subzero membrane phase behavior of mouse embryonic fibroblast (3T3) cells under dehydrating and non-dehydrating freezing conditions, in the absence and presence of dimethylsulfoxide (DMSO). In addition, suprazero membrane phase behavior and the effects of salt stress and air-drying on lipid disorder were studied. FTIR studies showed that freezing induces a fluid-to-gel membrane phase transition, both in the absence and presence of DMSO, which is reversible upon thawing. With DMSO the phase transition occurs more gradual over a greater temperature range. At suprazero temperatures, membrane conformational disorder was found to be decreased when cells were exposed to high concentrations of NaCl. This effect, however, is minor compared to the effects of freezing on membrane phase state. Membrane conformational disorder at −30 ◦ C in the case of freezing-induced dehydration was lower compared to that of air-dried cells. When cells are air-dried in the presence of 3 M NaCl, however, membrane conformational disorder drastically decreases. This indicates that the freezing-induced removal of water from the phospholipid head groups results from osmotic forces.
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification

In the time-series analysis, the time series motifs and the order patterns in time series can reveal general temporal patterns and dynamic features. Triadic Motif Field (TMF) is a simple and effective time-series image encoding method based on triadic time series motifs. Electrocardiography (ECG) signals are time-series data widely used to diagnose various cardiac anomalies. The TMF images contain the features characterizing the normal and Atrial Fibrillation (AF) ECG signals. Considering the quasi-periodic characteristics of ECG signals, the dynamic features can be extracted from the TMF images with the transfer learning pre-trained convolutional neural network (CNN) models. With the extracted features, the simple classifiers, such as the Multi-Layer Perceptron (MLP), the logistic regression, and the random forest, can be applied for accurate anomaly detection. With the test dataset of the PhysioNet Challenge 2017 database, the TMF classification model with the VGG16 transfer learning model and MLP classifier demonstrates the best performance with the 95.50% ROC-AUC and 88.43% F1 score in the AF classification. Besides, the TMF classification model can identify AF patients in the test dataset with high precision. The feature vectors extracted from the TMF images show clear patient-wise clustering with the t-distributed Stochastic Neighbor Embedding technique. Above all, the TMF classification model has very good clinical interpretability. The patterns revealed by symmetrized Gradient-weighted Class Activation Mapping have a clear clinical interpretation at the beat and rhythm levels.
Shrinkage of Decision Lists and DNF Formulas

We establish nearly tight bounds on the expected shrinkage of decision lists and DNF formulas under the $p$-random restriction $\mathbf R_p$ for all values of $p \in [0,1]$. For a function $f$ with domain $\{0,1\}^n$, let $\mathrm{DL}(f)$ denote the minimum size of a decision list that computes $f$. We show that \[ \mathbb E[\ \mathrm{DL}(f{\upharpoonright}\mathbf R_p)\ ] \le \mathrm{DL}(f)^{\log_{2/(1-p)}(\frac{1+p}{1-p})}. \] For example, this bound is $\sqrt{\mathrm{DL}(f)}$ when $p = \sqrt{5}-2 \approx 0.24$. For Boolean functions $f$, we obtain the same shrinkage bound with respect to DNF formula size plus $1$ (i.e., replacing $\mathrm{DL}(\cdot)$ with $\mathrm{DNF}(\cdot)+1$ on both sides of the inequality).
On Distributed Algorithms for Minimum Dominating Set problem, from theory to application

In this paper, we propose a local distributed algorithm for the minimum dominating set problem. For some especial networks, we prove theoretically that the achieved answer by our proposed algorithm is a constant approximation factor of the exact answer. This problem arises naturally in social networks, for example in news spreading, avoiding rumor spreading and recommendation spreading. So we implement our algorithm on massive social networks and compare our results with the state of the art algorithms. Also, we extend our algorithm to solve the $k$-dominating set problem and experimentally study the efficiency of the proposed algorithm. Our proposed algorithm is fast and easy to implement and can be used in dynamic model where the network in changing constantly. More importantly, based on the experimental results the proposed algorithm has reasonable solutions and running time which enables us to use it in distributed model practically.