OpenRaiser/Intern-Atlas huggingface dataset:
A unified theory of learning
Venue: arXiv cs.LG 2022
Year: 2022

Recently machine learning using neural networks (NN) has been developed, and many new methods have been suggested. These methods are optimized for the type of input data and work very effectively, but they cannot be used with any kind of input data universally. On the other hand, the human brain is universal for any kind of problem, and we will be able to construct artificial general intelligence if we can mimic the system of how the human brain works. We consider how the human brain learns things uniformly, and find that the essence of learning is the compression of information. We suggest a toy NN model which mimics the system of the human brain, and we show that the NN can compress the input information without ad hoc treatment, only by setting the loss function properly. The loss function is expressed as the sum of the self-information to remember and the loss of the information along with the compression, and its minimum corresponds to the self-information of the original data. To evaluate the self-information to remember, we provided the concept of memory. The memory expresses the compressed information, and the learning proceeds by referring to previous memories. There are many similarities between this NN and the human brain, and this NN is a realization of the free-energy principle which is considered to be a unified theory of the human brain. This work can be applied to any kind of data analysis and cognitive science.
Multi-Agent Spatial Predictive Control with Application to Drone Flocking (Extended Version)
Venue: arXiv cs.MA 2022
Year: 2022

We introduce the novel concept of Spatial Predictive Control (SPC) to solve the following problem: given a collection of agents (e.g., drones) with positional low-level controllers (LLCs) and a mission-specific distributed cost function, how can a distributed controller achieve and maintain cost-function minimization without a plant model and only positional observations of the environment? Our fully distributed SPC controller is based strictly on the position of the agent itself and on those of its neighboring agents. This information is used in every time-step to compute the gradient of the cost function and to perform a spatial look-ahead to predict the best next target position for the LLC. Using a high-fidelity simulation environment, we show that SPC outperforms the most closely related class of controllers, Potential Field Controllers, on the drone flocking problem. We also show that SPC is able to cope with a potential sim-to-real transfer gap by demonstrating its performance on real hardware, namely our implementation of flocking using nine Crazyflie 2.1 drones.
A Novel Probabilistic V2X Data Fusion Framework for Cooperative Perception
Venue: arXiv cs.RO 2022
Year: 2022

The paper addresses the vehicle-to-X (V2X) data fusion for cooperative or collective perception (CP). This emerging and promising intelligent transportation systems (ITS) technology has enormous potential for improving efficiency and safety of road transportation. Recent advances in V2X communication primarily address the definition of V2X messages and data dissemination amongst ITS stations (ITS-Ss) in a traffic environment. Yet, a largely unsolved problem is how a connected vehicle (CV) can efficiently and consistently fuse its local perception information with the data received from other ITS-Ss. In this paper, we present a novel data fusion framework to fuse the local and V2X perception data for CP that considers the presence of cross-correlation. The proposed approach is validated through comprehensive results obtained from numerical simulation, CARLA simulation, and real-world experimentation that incorporates V2X-enabled intelligent platforms. The real-world experiment includes a CV, a connected and automated vehicle (CAV), and an intelligent roadside unit (IRSU) retrofitted with vision and lidar sensors. We also demonstrate how the fused CP information can improve the awareness of vulnerable road users (VRU) for CV/CAV, and how this information can be considered in path planning/decision making within the CAV to facilitate safe interactions.
Message Passing Neural Networks for Hypergraphs
Venue: arXiv cs.LG 2022
Year: 2022

Hypergraph representations are both more efficient and better suited to describe data characterized by relations between two or more objects. In this work, we present a new graph neural network based on message passing capable of processing hypergraph-structured data. We show that the proposed model defines a design space for neural network models for hypergraphs, thus generalizing existing models for hypergraphs. We report experiments on a benchmark dataset for node classification, highlighting the effectiveness of the proposed model with respect to other state-of-the-art methods for graphs and hypergraphs. We also discuss the benefits of using hypergraph representations and, at the same time, highlight the limitation of using equivalent graph representations when the underlying problem has relations among more than two objects.
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks
Venue: arXiv cs.CV 2022
Year: 2022

New classes arise frequently in our ever-changing world, e.g., emerging topics in social media and new types of products in e-commerce. A model should recognize new classes and meanwhile maintain discriminability over old classes. Under severe circumstances, only limited novel instances are available to incrementally update the model. The task of recognizing few-shot new classes without forgetting old classes is called few-shot class-incremental learning (FSCIL). In this work, we propose a new paradigm for FSCIL based on meta-learning by LearnIng Multi-phase Incremental Tasks (LIMIT), which synthesizes fake FSCIL tasks from the base dataset. The data format of fake tasks is consistent with the `real' incremental tasks, and we can build a generalizable feature space for the unseen tasks through meta-learning. Besides, LIMIT also constructs a calibration module based on transformer, which calibrates the old class classifiers and new class prototypes into the same scale and fills in the semantic gap. The calibration module also adaptively contextualizes the instance-specific embedding with a set-to-set function. LIMIT efficiently adapts to new classes and meanwhile resists forgetting over old classes. Experiments on three benchmark datasets (CIFAR100, miniImageNet, and CUB200) and large-scale dataset, i.e., ImageNet ILSVRC2012 validate that LIMIT achieves state-of-the-art performance.
On the Population Monotonicity of Independent Set Games
Venue: arXiv cs.GT 2022
Year: 2022

An independent set game is a cooperative game defined on graphs and dealing with profit-sharing in maximum independent set problems. A population monotonic allocation scheme is a rule specifying how to share the profit of each coalition among its participants such that every participant is better off when the coalition expands. In this paper, we provide a necessary and sufficient characterization for population monotonic allocation schemes in independent set games. Moreover, our characterization can be verified efficiently.
Interpretation of Black Box NLP Models: A Survey
Venue: arXiv cs.LG 2022
Year: 2022

An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare. Despite their superior performances, many models are black boxes in nature which are hard to explain. There are growing efforts for researchers to develop methods to interpret these black-box models. Post hoc explanations based on perturbations, such as LIME, are widely used approaches to interpret a machine learning model after it has been built. This class of methods has been shown to exhibit large instability, posing serious challenges to the effectiveness of the method itself and harming user trust. In this paper, we propose S-LIME, which utilizes a hypothesis testing framework based on central limit theorem for determining the number of perturbation points needed to guarantee stability of the resulting explanation. Experiments on both simulated and real world data sets are provided to demonstrate the effectiveness of our method.
Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$
Venue: arXiv cs.LG 2022
Year: 2022

Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Scaling can be complicated due to various factors including the need to distribute computation on supercomputer clusters (e.g., TPUs), prevent bottlenecks when infeeding data, and ensure reproducible results. In this work, we present two software libraries that ease these issues: $\texttt{t5x}$ simplifies the process of building and training large language models at scale while maintaining ease of use, and $\texttt{seqio}$ provides a task-based API for simple creation of fast and reproducible training data and evaluation pipelines. These open-source libraries have been used to train models with hundreds of billions of parameters on datasets with multiple terabytes of training data. Along with the libraries, we release configurations and instructions for T5-like encoder-decoder models as well as GPT-like decoder-only architectures. $\texttt{t5x}$ and $\texttt{seqio}$ are open source and available at https://github.com/google-research/t5x and https://github.com/google/seqio, respectively.
MyStyle: A Personalized Generative Prior
Venue: arXiv cs.CV 2022
Year: 2022

We introduce MyStyle, a personalized deep generative prior trained with a few shots of an individual. MyStyle allows to reconstruct, enhance and edit images of a specific person, such that the output is faithful to the person's key facial characteristics. Given a small reference set of portrait images of a person (~100), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space. We show that this manifold constitutes a personalized region that spans latent codes associated with diverse portrait images of the individual. Moreover, we demonstrate that we obtain a personalized generative prior, and propose a unified approach to apply it to various ill-posed image enhancement problems, such as inpainting and super-resolution, as well as semantic editing. Using the personalized generative prior we obtain outputs that exhibit high-fidelity to the input images and are also faithful to the key facial characteristics of the individual in the reference set. We demonstrate our method with fair-use images of numerous widely recognizable individuals for whom we have the prior knowledge for a qualitative evaluation of the expected outcome. We evaluate our approach against few-shots baselines and show that our personalized prior, quantitatively and qualitatively, outperforms state-of-the-art alternatives.
Leveraging Privacy Profiles to Empower Users in the Digital Society
Venue: arXiv cs.CR 2022
Year: 2022

Privacy and ethics of citizens are at the core of the concerns raised by our increasingly digital society. Profiling users is standard practice for software applications triggering the need for users, also enforced by laws, to properly manage privacy settings. Users need to manage software privacy settings properly to protect personally identifiable information and express personal ethical preferences. AI technologies that empower users to interact with the digital world by reflecting their personal ethical preferences can be key enablers of a trustworthy digital society. We focus on the privacy dimension and contribute a step in the above direction through an empirical study on an existing dataset collected from the fitness domain. We find out which set of questions is appropriate to differentiate users according to their preferences. The results reveal that a compact set of semantic-driven questions (about domain-independent privacy preferences) helps distinguish users better than a complex domain-dependent one. This confirms the study's hypothesis that moral attitudes are the relevant piece of information to collect. Based on the outcome, we implement a recommender system to provide users with suitable recommendations related to privacy choices. We then show that the proposed recommender system provides relevant settings to users, obtaining high accuracy.
Curiosity Driven Self-supervised Tactile Exploration of Unknown Objects
Venue: arXiv cs.RO 2022
Year: 2022

Intricate behaviors an organism can exhibit is predicated on its ability to sense and effectively interpret complexities of its surroundings. Relevant information is often distributed between multiple modalities, and requires the organism to exhibit information assimilation capabilities in addition to information seeking behaviors. While biological beings leverage multiple sensing modalities for decision making, current robots are overly reliant on visual inputs. In this work, we want to augment our robots with the ability to leverage the (relatively under-explored) modality of touch. To focus our investigation, we study the problem of scene reconstruction where touch is the only available sensing modality. We present Tactile Slam (tSLAM) -- which prepares an agent to acquire information seeking behavior and use implicit understanding of common household items to reconstruct the geometric details of the object under exploration. Using the anthropomorphic `ADROIT' hand, we demonstrate that tSLAM is highly effective in reconstructing objects of varying complexities within 6 seconds of interactions. We also established the generality of tSLAM by training only on 3D Warehouse objects and testing on ContactDB objects.
Distributed Robust Geocast Multicast Routing for Inter-Vehicle Communication
Venue: arXiv cs.NI 2022
Year: 2022

Numerous protocols for geocast have been proposed in literature. It has been shown that explicit route setup approaches perform poorly with VANETs due to limited route lifetime and frequent network fragmentation. The broadcast based approaches have considerable redundancy and add significantly to the overhead of the protocol. A completely distributed and robust geocast approach is presented in this paper, that is resilient to frequent topology changes and network fragmentation. A distance-based backoff algorithm is used to reduce the number of hops and a novel mechanism to reduce redundant broadcasts is introduced. The performance of the proposed protocol is evaluated for various scenarios and compared with simple flooding and a protocol based on explicit route setup.
ElectAnon: A Blockchain-Based, Anonymous, Robust and Scalable Ranked-Choice Voting Protocol
Venue: arXiv cs.CR 2022
Year: 2022

Remote voting has become more critical in recent years, especially after the Covid-19 outbreak. Blockchain technology and its benefits like decentralization, security, and transparency have encouraged remote voting systems to use blockchains. Analysis of existing solutions reveals that anonymity, robustness, and scalability are common problems in blockchain-based election systems. In this work, we propose ElectAnon, a blockchain-based, ranked-choice election protocol focusing on anonymity, robustness, and scalability. ElectAnon achieves anonymity by enabling voters to cast their votes via zero-knowledge proofs anonymously. Robustness is realized by removing the direct control of the authorities in the voting process by using timed-state machines. Results show that ElectAnon is scalable amongst existing works as it reduces the gas consumption up to 89% compared to previous works. The proposed protocol includes a candidate proposal system and swappable tallying libraries. An extension is also proposed to minimize the trust assumption on election authorities. Our code is available on https://github.com/ceyonur/electanon.
A Survey of Robust 3D Object Detection Methods in Point Clouds
Venue: arXiv cs.CV 2022
Year: 2022

The purpose of this work is to review the state-of-the-art LiDAR-based 3D object detection methods, datasets, and challenges. We describe novel data augmentation methods, sampling strategies, activation functions, attention mechanisms, and regularization methods. Furthermore, we list recently introduced normalization methods, learning rate schedules and loss functions. Moreover, we also cover advantages and limitations of 10 novel autonomous driving datasets. We evaluate novel 3D object detectors on the KITTI, nuScenes, and Waymo dataset and show their accuracy, speed, and robustness. Finally, we mention the current challenges in 3D object detection in LiDAR point clouds and list some open issues.
QUIP: Query-driven Missing Value Imputation
Venue: arXiv cs.DB 2022
Year: 2022

Missing values widely exist in real-world data sets, and failure to clean the missing data may result in the poor quality of answers to queries. \yiming{Traditionally, missing value imputation has been studied as an offline process as part of preparing data for analysis.} This paper studies query-time missing value imputation and proposes QUIP, which only imputes minimal missing values to answer the query. Specifically, by taking a reasonable good query plan as input, QUIP tries to minimize the missing value imputation cost and query processing overhead. QUIP proposes a new implementation of outer join to preserve missing values in query processing and a bloom filter based index structure to optimize the space and runtime overhead. QUIP also designs a cost-based decision function to automatically guide each operator to impute missing values now or delay imputations. Efficient optimizations are proposed to speed-up aggregate operations in QUIP, such as MAX/MIN operator. Extensive experiments on both real and synthetic data sets demonstrates the effectiveness and efficiency of QUIP, which outperforms the state-of-the-art ImputeDB by 2 to 10 times on different query sets and data sets, and achieves the order-of-magnitudes improvement over the offline approach.
Designing for emotion regulation interventions: an agenda for HCI theory and research
Venue: arXiv cs.HC 2022
Year: 2022

There is a growing interest in HCI to envision, design, and evaluate technology-enabled interventions that support users' emotion regulation. This interest stems in part from increased recognition that the ability to regulate emotions is critical to mental health, and that a lack of effective emotion regulation is a transdiagnostic factor for mental illness. However, the potential to combine innovative HCI designs with the theoretical grounding and state-of-art interventions from psychology has yet to be fully realised. In this paper, we synthesise HCI work on emotion regulation interventions and propose a three-part framework to guide technology designers in making: (i) theory-informed decisions about intervention targets; (ii) strategic decisions regarding the technology-enabled intervention mechanisms to be included in the system; and (iii) practical decisions around previous implementations of the selected intervention components. We show how this framework can both systematise HCI work to date and suggest a research agenda for future work.
Joint Power and Subcarrier Allocation in Multi-Cell Multi-Carrier NOMA
Venue: arXiv cs.IT 2022
Year: 2022

Non-orthogonal multiple access (NOMA) is a technology proposed for next generation cellular networks because of its high spectral efficiency and enhanced user connectivity. However, in the literature the optimal joint power and sub-carrier allocation for NOMA has been proposed for single cell only. Consequently, a global optimal algorithm for the joint power and sub-carrier allocation for NOMA system in multi-cell scenario is still an open problem. In this work, we propose a polyblock optimization based algorithm for obtaining a global optimal solution. It has reduced complexity due to a necessary and sufficient condition for feasible successive interference cancellation (SIC). Besides, we can adjust its optimization approximation parameter to serve as benchmark solution or to offer suitable practical solution for multi-cell multi-carrier NOMA systems. Numerical studies have shown its effectiveness.
An MPSoC-based on-line Edge Infrastructure for Embedded Neuromorphic Robotic Controllers
Venue: arXiv cs.RO 2022
Year: 2022

In this work, an all-in-one neuromorphic controller system with reduced latency and power consumption for a robotic arm is presented. Biological muscle movement consists of stretching and shrinking fibres via spike-commanded signals that come from motor neurons, which in turn are connected to a central pattern generator neural structure. In addition, biological systems are able to respond to diverse stimuli rather fast and efficiently, and this is based on the way information is coded within neural processes. As opposed to human-created encoding systems, neural ones use neurons and spikes to process the information and make weighted decisions based on a continuous learning process. The Event-Driven Scorbot platform (ED-Scorbot) consists of a 6 Degrees of Freedom (DoF) robotic arm whose controller implements a Spiking Proportional-Integrative- Derivative algorithm, mimicking in this way the previously commented biological systems. In this paper, we present an infrastructure upgrade to the ED-Scorbot platform, replacing the controller hardware, which was comprised of two Spartan Field Programmable Gate Arrays (FPGAs) and a barebone computer, with an edge device, the Xilinx Zynq-7000 SoC (System on Chip) which reduces the response time, power consumption and overall complexity.
MOF: A Modular Framework for Rapid Application of Optimization Methodologies to General Engineering Design Problems
Venue: arXiv cs.NE 2022
Year: 2022

A variety of optimization algorithms have been developed to solve engineering design problems in which the solution space is too large to manually determine the optimal solution. The Modular Optimization Framework (MOF) was developed to facilitate the development and application of these optimization algorithms. MOF is written in Python 3, and it used object-oriented programming to create a modular design that allows users to easily incorporate new optimization algorithms, methods, or engineering design problems into the framework. Additionally, a common input file allows users to easily specify design problems, update the optimization parameters, and perform comparisons between various optimization methods and algorithms. In the current MOF version, genetic algorithm (GA) and simulated annealing (SA) approaches are implemented. Applications in different nuclear engineering optimization problems are included as examples. The effectiveness of the GA and SA optimization algorithms within MOF are demonstrated through an unconstrained nuclear fuel assembly pin lattice optimization, a first cycle fuel loading constrained optimization of a three-loop pressurized water reactor (PWR), and a third cycle constrained optimization of a four-loop PWR. In all cases, the algorithms efficiently searched the solution spaces and found optimized solutions to the given problems that satisfied the imposed constraints. These results demonstrate the capabilities of the existing optimization tools within MOF, and they also provide a set of benchmark cases that can be used to evaluate the progress of future optimization methodologies with MOF.