OpenRaiser/Intern-Atlas huggingface dataset:
Analysis of three dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis
Venue: arXiv cs.LG 2022
Year: 2022

In this work, we present a deep collocation method for three dimensional potential problems in nonhomogeneous media. This approach utilizes a physics informed neural network with material transfer learning reducing the solution of the nonhomogeneous partial differential equations to an optimization problem. We tested different cofigurations of the physics informed neural network including smooth activation functions, sampling methods for collocation points generation and combined optimizers. A material transfer learning technique is utilised for nonhomogeneous media with different material gradations and parameters, which enhance the generality and robustness of the proposed method. In order to identify the most influential parameters of the network configuration, we carried out a global sensitivity analysis. Finally, we provide a convergence proof of our DCM. The approach is validated through several benchmark problems, also testing different material variations.
Learning Patterns in Imaginary Vowels for an Intelligent Brain Computer Interface (BCI) Design
Venue: arXiv cs.LG 2022
Year: 2022

Technology advancements made it easy to measure non-invasive and high-quality electroencephalograph (EEG) signals from human's brain. Hence, development of robust and high-performance AI algorithms becomes crucial to properly process the EEG signals and recognize the patterns, which lead to an appropriate control signal. Despite the advancements in processing the motor imagery EEG signals, the healthcare applications, such as emotion detection, are still in the early stages of AI design. In this paper, we propose a modular framework for the recognition of vowels as the AI part of a brain computer interface system. We carefully designed the modules to discriminate the English vowels given the raw EEG signals, and meanwhile avoid the typical issued with the data-poor environments like most of the healthcare applications. The proposed framework consists of appropriate signal segmentation, filtering, extraction of spectral features, reducing the dimensions by means of principle component analysis, and finally a multi-class classification by decision-tree-based support vector machine (DT-SVM). The performance of our framework was evaluated by a combination of test-set and resubstitution (also known as apparent) error rates. We provide the algorithms of the proposed framework to make it easy for future researchers and developers who want to follow the same workflow.
Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face Recognition
Venue: arXiv cs.CV 2022
Year: 2022

State-of-the-art deep neural network models have reached near perfect face recognition accuracy rates on controlled high-resolution face images. However, their performance is drastically degraded when they are tested with very low-resolution face images. This is particularly critical in surveillance systems, where a low-resolution probe image is to be matched with high-resolution gallery images. super-resolution techniques aim at producing high-resolution face images from low-resolution counterparts. While they are capable of reconstructing images that are visually appealing, the identity-related information is not preserved. Here, we propose an identity-preserving end-to-end image-to-image translation deep neural network which is capable of super-resolving very low-resolution faces to their high-resolution counterparts while preserving identity-related information. We achieved this by training a very deep convolutional encoder-decoder network with a symmetric contracting path between corresponding layers. This network was trained with a combination of a reconstruction and an identity-preserving loss, on multi-scale low-resolution conditions. Extensive quantitative evaluations of our proposed model demonstrated that it outperforms competing super-resolution and low-resolution face recognition methods on natural and artificial low-resolution face data sets and even unseen identities.
Structure-Grounded Pretraining for Text-to-SQL
Venue: arXiv cs.CL 2022
Year: 2022

Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns and values and to ground them in the given database schema. In this paper, we present a novel weakly supervised Structure-Grounded pretraining framework (StruG) for text-to-SQL that can effectively learn to capture text-table alignment based on a parallel text-table corpus. We identify a set of novel prediction tasks: column grounding, value grounding and column-value mapping, and leverage them to pretrain a text-table encoder. Additionally, to evaluate different methods under more realistic text-table alignment settings, we create a new evaluation set Spider-Realistic based on Spider dev set with explicit mentions of column names removed, and adopt eight existing text-to-SQL datasets for cross-database evaluation. STRUG brings significant improvement over BERT-LARGE in all settings. Compared with existing pretraining methods such as GRAPPA, STRUG achieves similar performance on Spider, and outperforms all baselines on more realistic sets. The Spider-Realistic dataset is available at https://doi.org/10.5281/zenodo.5205322.
Pooling for First and Last Mile: Integrating Carpooling and Transit
Venue: arXiv cs.MA 2022
Year: 2022

While carpooling is widely adopted for long travels, it is by construction inefficient for daily commuting, where it is difficult to match drivers and riders, sharing similar origin, destination and time. To overcome this limitation, we present an Integrated system, which integrates carpooling into transit, in the line of the philosophy of Mobility as a Service. Carpooling acts as feeder to transit and transit stations act as consolidation points, where trips of riders and drivers meet, increasing potential matching. We present algorithms to construct multimodal rider trips (including transit and carpooling legs) and driver detours. Simulation shows that our Integrated system increases transit ridership and reduces auto-dependency, with respect to current practice, in which carpooling and transit are operated separately. Indeed, the Integrated system decreases the number of riders who are left with no feasible travel option and would thus be forced to use private cars. The simulation code is available as open source.
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills
Venue: arXiv cs.RO 2022
Year: 2022

Parameterized movement primitives have been extensively used for imitation learning of robotic tasks. However, the high-dimensionality of the parameter space hinders the improvement of such primitives in the reinforcement learning (RL) setting, especially for learning with physical robots. In this paper we propose a novel view on handling the demonstrated trajectories for acquiring low-dimensional, non-linear latent dynamics, using mixtures of probabilistic principal component analyzers (MPPCA) on the movements' parameter space. Moreover, we introduce a new contextual off-policy RL algorithm, named LAtent-Movements Policy Optimization (LAMPO). LAMPO can provide gradient estimates from previous experience using self-normalized importance sampling, hence, making full use of samples collected in previous learning iterations. These advantages combined provide a complete framework for sample-efficient off-policy optimization of movement primitives for robot learning of high-dimensional manipulation skills. Our experimental results conducted both in simulation and on a real robot show that LAMPO provides sample-efficient policies against common approaches in literature.
Transporter Networks: Rearranging the Visual World for Robotic Manipulation
Venue: arXiv cs.RO 2022
Year: 2022

Robotic manipulation can be formulated as inducing a sequence of spatial displacements: where the space being moved can encompass an object, part of an object, or end effector. In this work, we propose the Transporter Network, a simple model architecture that rearranges deep features to infer spatial displacements from visual input - which can parameterize robot actions. It makes no assumptions of objectness (e.g. canonical poses, models, or keypoints), it exploits spatial symmetries, and is orders of magnitude more sample efficient than our benchmarked alternatives in learning vision-based manipulation tasks: from stacking a pyramid of blocks, to assembling kits with unseen objects; from manipulating deformable ropes, to pushing piles of small objects with closed-loop feedback. Our method can represent complex multi-modal policy distributions and generalizes to multi-step sequential tasks, as well as 6DoF pick-and-place. Experiments on 10 simulated tasks show that it learns faster and generalizes better than a variety of end-to-end baselines, including policies that use ground-truth object poses. We validate our methods with hardware in the real world. Experiment videos and code are available at https://transporternets.github.io
Vertex nomination between graphs via spectral embedding and quadratic programming
Venue: arXiv cs.SI 2022
Year: 2022

Given a network and a subset of interesting vertices whose identities are only partially known, the vertex nomination problem seeks to rank the remaining vertices in such a way that the interesting vertices are ranked at the top of the list. An important variant of this problem is vertex nomination in the multi-graphs setting. Given two graphs $G_1, G_2$ with common vertices and a vertex of interest $x \in G_1$, we wish to rank the vertices of $G_2$ such that the vertices most similar to $x$ are ranked at the top of the list. The current paper addresses this problem and proposes a method that first applies adjacency spectral graph embedding to embed the graphs into a common Euclidean space, and then solves a penalized linear assignment problem to obtain the nomination lists. Since the spectral embedding of the graphs are only unique up to orthogonal transformations, we present two approaches to eliminate this potential non-identifiability. One approach is based on orthogonal Procrustes and is applicable when there are enough vertices with known correspondence between the two graphs. Another approach uses adaptive point set registration and is applicable when there are few or no vertices with known correspondence. We show that our nomination scheme leads to accurate nomination under a generative model for pairs of random graphs that are approximately low-rank and possibly with pairwise edge correlations. We illustrate our algorithm's performance through simulation studies on synthetic data as well as analysis of a high-school friendship network and analysis of transition rates between web pages on the Bing search engine.
How Does the Task Landscape Affect MAML Performance?
Venue: arXiv cs.LG 2022
Year: 2022

Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps. However, the MAML objective is significantly more difficult to optimize compared to standard non-adaptive learning (NAL), and little is understood about how much MAML improves over NAL in terms of the fast adaptability of their solutions in various scenarios. We analytically address this issue in a linear regression setting consisting of a mixture of easy and hard tasks, where hardness is related to the rate that gradient descent converges on the task. Specifically, we prove that in order for MAML to achieve substantial gain over NAL, (i) there must be some discrepancy in hardness among the tasks, and (ii) the optimal solutions of the hard tasks must be closely packed with the center far from the center of the easy tasks optimal solutions. We also give numerical and analytical results suggesting that these insights apply to two-layer neural networks. Finally, we provide few-shot image classification experiments that support our insights for when MAML should be used and emphasize the importance of training MAML on hard tasks in practice.
Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data
Venue: arXiv cs.RO 2022
Year: 2022

As more and more autonomous vehicles (AVs) are being deployed on public roads, designing socially compatible behaviors for them is becoming increasingly important. In order to generate safe and efficient actions, AVs need to not only predict the future behaviors of other traffic participants, but also be aware of the uncertainties associated with such behavior prediction. In this paper, we propose an uncertain-aware integrated prediction and planning (UAPP) framework. It allows the AVs to infer the characteristics of other road users online and generate behaviors optimizing not only their own rewards, but also their courtesy to others, and their confidence regarding the prediction uncertainties. We first propose the definitions for courtesy and confidence. Based on that, their influences on the behaviors of AVs in interactive driving scenarios are explored. Moreover, we evaluate the proposed algorithm on naturalistic human driving data by comparing the generated behavior against ground truth. Results show that the online inference can significantly improve the human-likeness of the generated behaviors. Furthermore, we find that human drivers show great courtesy to others, even for those without right-of-way. We also find that such driving preferences vary significantly in different cultures.
Toyota Smarthome Untrimmed: Real-World Untrimmed Videos for Activity Detection
Venue: arXiv cs.CV 2022
Year: 2022

Designing activity detection systems that can be successfully deployed in daily-living environments requires datasets that pose the challenges typical of real-world scenarios. In this paper, we introduce a new untrimmed daily-living dataset that features several real-world challenges: Toyota Smarthome Untrimmed (TSU). TSU contains a wide variety of activities performed in a spontaneous manner. The dataset contains dense annotations including elementary, composite activities and activities involving interactions with objects. We provide an analysis of the real-world challenges featured by our dataset, highlighting the open issues for detection algorithms. We show that current state-of-the-art methods fail to achieve satisfactory performance on the TSU dataset. Therefore, we propose a new baseline method for activity detection to tackle the novel challenges provided by our dataset. This method leverages one modality (i.e. optic flow) to generate the attention weights to guide another modality (i.e RGB) to better detect the activity boundaries. This is particularly beneficial to detect activities characterized by high temporal variance. We show that the method we propose outperforms state-of-the-art methods on TSU and on another popular challenging dataset, Charades.
Actris 2.0: Asynchronous Session-Type Based Reasoning in Separation Logic
Venue: arXiv cs.LO 2022
Year: 2022

Message passing is a useful abstraction for implementing concurrent programs. For real-world systems, however, it is often combined with other programming and concurrency paradigms, such as higher-order functions, mutable state, shared-memory concurrency, and locks. We present Actris: a logic for proving functional correctness of programs that use a combination of the aforementioned features. Actris combines the power of modern concurrent separation logics with a first-class protocol mechanism -- based on session types -- for reasoning about message passing in the presence of other concurrency paradigms. We show that Actris provides a suitable level of abstraction by proving functional correctness of a variety of examples, including a channel-based merge sort, a channel-based load-balancing mapper, and a variant of the map-reduce model, using concise specifications. While Actris was already presented in a conference paper (POPL'20), this paper expands the prior presentation significantly. Moreover, it extends Actris to Actris 2.0 with a notion of subprotocols -- based on session-type subtyping -- that permits additional flexibility when composing channel endpoints, and that takes full advantage of the asynchronous semantics of message passing in Actris. Soundness of Actris 2.0 is proven using a model of its protocol mechanism in the Iris framework. We have mechanised the theory of Actris, together with custom tactics, as well as all examples in the paper, in the Coq proof assistant.
A configurable computer simulation model for reducing patient waiting time in oncology departments
Venue: arXiv cs.CY 2022
Year: 2022

Nowadays, the increase in patient demand and the decline in resources are lengthening patient waiting times in many chemotherapy oncology departments. Therefore, enhancing healthcare services is necessary to reduce patient complaints. Reducing the patient waiting times in the oncology departments represents one of the main goals of healthcare manager. Simulation models are considered an effective tool for identifying potential ways to improve patient flow in oncology departments. This paper presents a new agent-based simulation model designed to be configurable and adaptable to the needs of oncology departments which have to interact with an external pharmacy. When external pharmacies are utilized, a courier service is needed to deliver the individual therapies from the pharmacy to the oncology department. An oncology department located in southern Italy was studied through the simulation model and different scenarios were compared with the aim of selecting the department configuration capable of reducing the patient waiting times.
Two-stage coding over the Z-channel
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we discuss two-stage encoding algorithms capable of correcting a fraction of asymmetric errors. Suppose that the encoder transmits $n$ binary symbols $(x_1,\ldots,x_n)$ one-by-one over the Z-channel, in which a 1 is received only if a 1 is transmitted. At some designated moment, say $n_1$, the encoder uses noiseless feedback and adjusts further encoding strategy based on the partial output of the channel $(y_1,\ldots,y_{n_1})$. The goal is to transmit error-free as much information as possible under the assumption that the total number of errors inflicted by the Z-channel is limited by $τn$, $0<τ<1$. We propose an encoding strategy that uses a list-decodable code at the first stage and a high-error low-rate code at the second stage. This strategy and our converse result yield that there is a sharp transition at $τ=\max\limits_{0<w<1}\frac{w + w^3}{1+4w^3}\approx 0.44$ from positive rate to zero rate for two-stage encoding strategies. As side results, we derive bounds on the size of list-decodable codes for the Z-channel and prove that for a fraction $1/4+ε$ of asymmetric errors, an error-correcting code contains at most $O(ε^{-3/2})$ codewords.
A survey and analysis of TLS interception mechanisms and motivations
Venue: arXiv cs.CR 2022
Year: 2022

TLS is an end-to-end protocol designed to provide confidentiality and integrity guarantees that improve end-user security and privacy. While TLS helps defend against pervasive surveillance of intercepted unencrypted traffic, it also hinders several common beneficial operations typically performed by middleboxes on the network traffic. Consequently, various methods have been proposed that "bypass" the confidentiality goals of TLS by playing with keys and certificates essentially in a man-in-the-middle solution, as well as new proposals that extend the protocol to accommodate third parties, delegation schemes to trusted middleboxes, and fine-grained control and verification mechanisms. We first review the use cases expecting plain HTTP traffic and discuss the extent to which TLS hinders these operations. We retain 19 scenarios where access to unencrypted traffic is still relevant and evaluate the incentives of the stakeholders involved. Second, we survey 30 schemes by which TLS no longer delivers end-to-end security, and by which the notion of an "end" changes, including caching middleboxes such as Content Delivery Networks. Finally, we compare each scheme based on deployability and security characteristics, and evaluate their compatibility with the stakeholders' incentives. Our analysis leads to a number of key findings, observations, and research questions that we believe will be of interest to practitioners, policy makers and researchers.
Monitoring the edges of a graph using distances
Venue: arXiv cs.DS 2022
Year: 2022

We introduce a new graph-theoretic concept in the area of network monitoring. A set $M$ of vertices of a graph $G$ is a \emph{distance-edge-monitoring set} if for every edge $e$ of $G$, there is a vertex $x$ of $M$ and a vertex $y$ of $G$ such that $e$ belongs to all shortest paths between $x$ and $y$. We denote by $dem(G)$ the smallest size of such a set in $G$. The vertices of $M$ represent distance probes in a network modeled by $G$; when the edge $e$ fails, the distance from $x$ to $y$ increases, and thus we are able to detect the failure. It turns out that not only we can detect it, but we can even correctly locate the failing edge. In this paper, we initiate the study of this new concept. We show that for a nontrivial connected graph $G$ of order $n$, $1\leq dem(G)\leq n-1$ with $dem(G)=1$ if and only if $G$ is a tree, and $dem(G)=n-1$ if and only if it is a complete graph. We compute the exact value of $dem$ for grids, hypercubes, and complete bipartite graphs. Then, we relate $dem$ to other standard graph parameters. We show that $demG)$ is lower-bounded by the arboricity of the graph, and upper-bounded by its vertex cover number. It is also upper-bounded by twice its feedback edge set number. Moreover, we characterize connected graphs $G$ with $dem(G)=2$. Then, we show that determining $dem(G)$ for an input graph $G$ is an NP-complete problem, even for apex graphs. There exists a polynomial-time logarithmic-factor approximation algorithm, however it is NP-hard to compute an asymptotically better approximation, even for bipartite graphs of small diameter and for bipartite subcubic graphs. For such instances, the problem is also unlikey to be fixed parameter tractable when parameterized by the solution size.
Opinion Dynamics of Online Social Network Users: A Micro-Level Analysis
Venue: arXiv cs.SI 2022
Year: 2022

In this paper, we present an empirical study of the opinion dynamics of a large-scale sample of online social network users. We estimate opinions of users as continuous scalars based on their subscriptions to information sources and analyze how friendship connections affect the dynamics of these estimations. Distinguishing between positive (toward opinions of friends) and negative (away from opinions of friends) opinion shifts, we find that the existence and magnitude of opinion shifts are positively related (largely through a linear form or an inverted U-shaped form) to the degree of divergence in opinions between user and their friends. Additionally, we moderate the balance between positive and negative shifts using opinion divergence: if the opinions of the focal user and their friends are too similar or dissimilar, there is a relatively low chance of a positive shift.