pwc-archive/papers-with-abstracts huggingface dataset:
Technology and Consciousness
Authors: John Rushby, Daniel Sanchez
Date: 2022-07-17 00:00:00

We report on a series of eight workshops held in the summer of 2017 on the topic "technology and consciousness." The workshops covered many subjects but the overall goal was to assess the possibility of machine consciousness, and its potential implications. In the body of the report, we summarize most of the basic themes that were discussed: the structure and function of the brain, theories of consciousness, explicit attempts to construct conscious machines, detection and measurement of consciousness, possible emergence of a conscious technology, methods for control of such a technology and ethical considerations that might be owed to it. An appendix outlines the topics of each workshop and provides abstracts of the talks delivered. Update: Although this report was published in 2018 and the workshops it is based on were held in 2017, recent events suggest that it is worth bringing forward. In particular, in the Spring of 2022, a Google engineer claimed that LaMDA, one of their "large language models" is sentient or even conscious. This provoked a flurry of commentary in both the scientific and popular press, some of it interesting and insightful, but almost all of it ignorant of the prior consideration given to these topics and the history of research into machine consciousness. Thus, we are making a lightly refreshed version of this report available in the hope that it will provide useful background to the current debate and will enable more informed commentary. Although this material is five years old, its technical points remain valid and up to date, but we have "refreshed" it by adding a few footnotes highlighting recent developments.
Critical Slowing Down along the Separatrix of Lotka-Volterra Model of Competition
Authors: Sauvik Chatterjee, Muktish Acharyya
Date: 2022-09-09 00:00:00

The Lotka-Volterra model of competition has been studied by numerical simulations using the Runge-Kutta-Fehlberg algorithm. The stable fixed points, unstable fixed point, saddle node, basins of attraction, and the separatices are found. The transient behaviours associated with reaching the stable fixed point are studied systematically. It is observed that the time of reaching the stable fixed point in any one of the basins of attraction, depends strongly on the initial distance from the separatrix. As the initial point approached the separatrix, this time was found to diverge logarithmically. The divergence of the time, required to reach the stable fixed point, indicates the critical slowing down near the critical point in equilibrium phase transition. A metastable behaviour was also observed near the saddle fixed point before reaching the stable fixed point.
Cryptocurrency bubbles, the wealth effect, and non-fungible token prices: Evidence from metaverse LAND
Authors: Kanis Saengchote
Date: 2022-09-09 00:00:00

The rapid rise of cryptocurrency prices led to concerns (e.g. the Financial Stability Board) that this wealth accumulation could detrimentally spill over into other parts of the economy, but evidence is limited. We exploit the tendency for metaverses to issue their own cryptocurrencies along with non-fungible tokens (NFTs) representing virtual real estate ownership (LAND) to provide evidence of the wealth effect. Cryptocurrency prices and their corresponding real estate prices are highly correlated (more than 0.96), and cryptocurrency prices Granger cause LAND prices. This metaverse bubble reminisces the 1920s American real estate bubble that preceded the 1929 stock market crash.
Improving plant disease classification by adaptive minimal ensembling
Authors: Antonio Bruno, Davide Moroni, Riccardo Dainelli, Leandro Rocchi, Silvia Morelli, Emilio Ferrari, Piero Toscano, Massimo Martinelli
Date: 2022-09-08 00:00:00
Proceeding: Frontiers in Artificial Intelligence 2022 9
Tasks: Benchmarking, Classification, Diagnostic, Image Classification, Transfer Learning

A novel method for improving plant disease classification, a challenging and time-consuming process, is proposed. First, using as baseline EfficientNet, a recent and advanced family of architectures having an excellent accuracy/complexity trade-off, we have introduced, devised, and applied refined techniques based on transfer learning, regularization, stratification, weighted metrics, and advanced optimizers in order to achieve improved performance. Then, we go further by introducing adaptive minimal ensembling, which is a unique input to the knowledge base of the proposed solution. This represents a leap forward since it allows improving the accuracy with limited complexity using only two EfficientNet-b0 weak models, performing ensembling on feature vectors by a trainable layer instead of classic aggregation on outputs. To the best of our knowledge, such an approach to ensembling has never been used before in literature. Our method was tested on PlantVillage, a public reference dataset used for benchmarking models' performances for crop disease diagnostic, considering both its original and augmented versions. We noticeably improved the state of the art by achieving 100% accuracy in both the original and augmented datasets. Results were obtained using PyTorch to train, test, and validate the models; reproducibility is granted by providing exhaustive details, including hyperparameters used in the experimentation. A Web interface is also made publicly available to test the proposed methods.
Accelerated Primal-Dual Methods for Convex-Strongly-Concave Saddle Point Problems
Authors: Mohammad Khalafi, Digvijay Boob
Date: 2022-09-10 00:00:00

We investigate a primal-dual (PD) method for the saddle point problem (SPP) that uses a linear approximation of the primal function instead of the standard proximal step, resulting in a linearized PD (LPD) method. For convex-strongly concave SPP, we observe that the LPD method has a suboptimal dependence on the Lipschitz constant of the primal function. To fix this issue, we combine features of Accelerated Gradient Descent with the LPD method resulting in a single-loop Accelerated Linearized Primal-Dual (ALPD) method. ALPD method achieves the optimal gradient complexity when the SPP has a semi-linear coupling function. We also present an inexact ALPD method for SPPs with a general nonlinear coupling function that maintains the optimal gradient evaluations of the primal parts and significantly improves the gradient evaluations of the coupling term compared to the ALPD method. We verify our findings with numerical experiments.
Unified State Representation Learning under Data Augmentation
Authors: Taylor Hearn, Sravan Jayanthi, Sehoon Ha
Date: 2022-09-12 00:00:00
Tasks: Data Augmentation, Domain Adaptation, Reinforcement Learning (RL), Representation Learning

The capacity for rapid domain adaptation is important to increasing the applicability of reinforcement learning (RL) to real world problems. Generalization of RL agents is critical to success in the real world, yet zero-shot policy transfer is a challenging problem since even minor visual changes could make the trained agent completely fail in the new task. We propose USRA: Unified State Representation Learning under Data Augmentation, a representation learning framework that learns a latent unified state representation by performing data augmentations on its observations to improve its ability to generalize to unseen target domains. We showcase the success of our approach on the DeepMind Control Generalization Benchmark for the Walker environment and find that USRA achieves higher sample efficiency and 14.3% better domain adaptation performance compared to the best baseline results.
OpenMixup: Open Mixup Toolbox and Benchmark for Visual Representation Learning
Authors: Siyuan Li, Zedong Wang, Zicheng Liu, Juanxi Tian, Di wu, Cheng Tan, Weiyang Jin, Stan Z. Li
Date: 2022-09-11 00:00:00
Tasks: Benchmarking, Classification, Image Classification, Representation Learning, Self-Supervised Learning, Semi-Supervised Image Classification

Mixup augmentation has emerged as a widely used technique for improving the generalization ability of deep neural networks (DNNs). However, the lack of standardized implementations and benchmarks has impeded recent progress, resulting in poor reproducibility, unfair comparisons, and conflicting insights. In this paper, we introduce OpenMixup, the first mixup augmentation codebase, and benchmark for visual representation learning. Specifically, we train 18 representative mixup baselines from scratch and rigorously evaluate them across 11 image datasets of varying scales and granularity, ranging from fine-grained scenarios to complex non-iconic scenes. We also open-source our modular codebase, including a collection of popular vision backbones, optimization strategies, and analysis toolkits, which not only supports the benchmarking but enables broader mixup applications beyond classification, such as self-supervised learning and regression tasks. Through experiments and empirical analysis, we gain observations and insights on mixup performance-efficiency trade-offs, generalization, and optimization behaviors, and thereby identify preferred choices for different needs. To the best of our knowledge, OpenMixup has facilitated several recent studies. We believe this work can further advance reproducible mixup augmentation research and thereby lay a solid ground for future progress in the community. The source code and user documents are available at \url{https://github.com/Westlake-AI/openmixup}.
Switchable Online Knowledge Distillation
Authors: Biao Qian, Yang Wang, Hongzhi Yin, Richang Hong, Meng Wang
Date: 2022-09-12 00:00:00
Tasks: Knowledge Distillation

Online Knowledge Distillation (OKD) improves the involved models by reciprocally exploiting the difference between teacher and student. Several crucial bottlenecks over the gap between them -- e.g., Why and when does a large gap harm the performance, especially for student? How to quantify the gap between teacher and student? -- have received limited formal study. In this paper, we propose Switchable Online Knowledge Distillation (SwitOKD), to answer these questions. Instead of focusing on the accuracy gap at test phase by the existing arts, the core idea of SwitOKD is to adaptively calibrate the gap at training phase, namely distillation gap, via a switching strategy between two modes -- expert mode (pause the teacher while keep the student learning) and learning mode (restart the teacher). To possess an appropriate distillation gap, we further devise an adaptive switching threshold, which provides a formal criterion as to when to switch to learning mode or expert mode, and thus improves the student's performance. Meanwhile, the teacher benefits from our adaptive switching threshold and keeps basically on a par with other online arts. We further extend SwitOKD to multiple networks with two basis topologies. Finally, extensive experiments and analysis validate the merits of SwitOKD for classification over the state-of-the-arts. Our code is available at https://github.com/hfutqian/SwitOKD.
Peer Recommendation Interventions for Health-related Social Support: a Feasibility Assessment
Authors: Zachary Levonian, Matthew Zent, Ngan Nguyen, Matthew McNamara, Loren Terveen, Svetlana Yarosh
Date: 2022-09-12 00:00:00
Tasks: Recommendation Systems

Online health communities (OHCs) offer the promise of connecting with supportive peers. Forming these connections first requires finding relevant peers - a process that can be time-consuming. Peer recommendation systems are a computational approach to make finding peers easier during a health journey. By encouraging OHC users to alter their online social networks, peer recommendations could increase available support. But these benefits are hypothetical and based on mixed, observational evidence. To experimentally evaluate the effect of peer recommendations, we conceptualize these systems as health interventions designed to increase specific beneficial connection behaviors. In this paper, we designed a peer recommendation intervention to increase two behaviors: reading about peer experiences and interacting with peers. We conducted an initial feasibility assessment of this intervention by conducting a 12-week field study in which 79 users of CaringBridge received weekly peer recommendations via email. Our results support the usefulness and demand for peer recommendation and suggest benefits to evaluating larger peer recommendation interventions. Our contributions include practical guidance on the development and evaluation of peer recommendation interventions for OHCs.
If Influence Functions are the Answer, Then What is the Question?
Authors: Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, Roger Grosse
Date: 2022-09-12 00:00:00

Influence functions efficiently estimate the effect of removing a single training data point on a model's learned parameters. While influence estimates align well with leave-one-out retraining for linear models, recent works have shown this alignment is often poor in neural networks. In this work, we investigate the specific factors that cause this discrepancy by decomposing it into five separate terms. We study the contributions of each term on a variety of architectures and datasets and how they vary with factors such as network width and training time. While practical influence function estimates may be a poor match to leave-one-out retraining for nonlinear networks, we show they are often a good approximation to a different object we term the proximal Bregman response function (PBRF). Since the PBRF can still be used to answer many of the questions motivating influence functions, such as identifying influential or mislabeled examples, our results suggest that current algorithms for influence function estimation give more informative results than previous error analyses would suggest.
Testing Pre-trained Language Models' Understanding of Distributivity via Causal Mediation Analysis
Authors: Pangbo Ban, Yifan Jiang, Tianran Liu, Shane Steinert-Threlkeld
Date: 2022-09-11 00:00:00
Tasks: Diagnostic, Natural Language Inference

To what extent do pre-trained language models grasp semantic knowledge regarding the phenomenon of distributivity? In this paper, we introduce DistNLI, a new diagnostic dataset for natural language inference that targets the semantic difference arising from distributivity, and employ the causal mediation analysis framework to quantify the model behavior and explore the underlying mechanism in this semantically-related task. We find that the extent of models' understanding is associated with model size and vocabulary size. We also provide insights into how models encode such high-level semantic knowledge.
CSL: A Large-scale Chinese Scientific Literature Dataset
Authors: Yudong Li, Yuqing Zhang, Zhe Zhao, Linlin Shen, Weijie Liu, Weiquan Mao, HUI ZHANG
Date: 2022-09-12 00:00:00
Proceeding: COLING 2022 10
Tasks: text-classification, Text Classification

Scientific literature serves as a high-quality corpus, supporting a lot of Natural Language Processing (NLP) research. However, existing datasets are centered around the English language, which restricts the development of Chinese scientific NLP. In this work, we present CSL, a large-scale Chinese Scientific Literature dataset, which contains the titles, abstracts, keywords and academic fields of 396k papers. To our knowledge, CSL is the first scientific document dataset in Chinese. The CSL can serve as a Chinese corpus. Also, this semi-structured data is a natural annotation that can constitute many supervised NLP tasks. Based on CSL, we present a benchmark to evaluate the performance of models across scientific domain tasks, i.e., summarization, keyword generation and text classification. We analyze the behavior of existing text-to-text models on the evaluation tasks and reveal the challenges for Chinese scientific NLP tasks, which provides a valuable reference for future research. Data and code are available at https://github.com/ydli-ai/CSL
Novel and Secure Blockchain Framework for Health Applications in IoT
Authors: MARAH R. BATAINEH, WAIL MARDINI, YASER M. KHAMAYSEH, MUNEER MASADEH BANI YASSEIN
Date: 2022-01-31 00:00:00
Proceeding: IEEE Access 2022 1
Tasks: Management

nternet of Things (IoT) has grown increasingly in the past decade. This growth brings up several challenging issues for a successful continuous operation of IoT applications. Some of these challenges that need to be taken care of are resource constraints, central server overload, and the risk of illegal use of private data. On the other side, Blockchain technology is increasingly popular and has gained huge success in cryptocurrencies. It offers numerous vital qualities such as a technique for consensus, peer communications, confidence-building without a trustworthy third party, and a transaction controlled by conditions and functions using the intelligent contract technique. Blockchain is an excellent candidate to establish a decentralized, autonomous IoT system addressing the above issues. This study proposes an IoT-Blockchain integration architecture using an Ethereum Blockchain infrastructure within a rich-thin client IoT approach to address the challenges created by the limited IoT resources while implementing the Blockchain mining technique in IoT systems. The architecture depends on load distribution between the resources. Limited resource devices are the thin-clients, while the higher resource devices are assigned as rich-clients. Both clients can access the blockchain and collect the data, but rich-client can only execute the mining process. In addition, we implement a healthcare system based on the proposed architecture in which surgical process management is carried out. We also prove our solution’s efficiency by testing and comparing the architecture against other well-known IoT-based blockchain architectures. The obtained results show that proposed blockchain-IoT architecture is suited for many IoT applications while avoiding difficulties created by IoT devices’ limitations.
Vi-Fi: Associating Moving Subjects across Vision and Wireless Sensors
Authors: Hansi Liu, Abrar Alali, Mohamed Ibrahim, Bryan Bo Cao, Nicholas Meegan, Hongyu Li, Marco Gruteser, Shubham Jain, Kristin Dana, Ashwin Ashok, Bin Cheng, HongSheng Lu
Date: 2022-07-18 00:00:00
Proceeding: ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) 2022 7
Tasks: Graph Matching, Multimodal Association

In this paper, we present Vi-Fi, a multi-modal system that leverages a user’s smartphone WiFi Fine Timing Measurements (FTM) and inertial measurement unit (IMU) sensor data to associate the user detected on a camera footage with their corresponding smartphone identifier (e.g. WiFi MAC address). Our approach uses a recurrent multi-modal deep neural network that exploits FTM and IMU measurements along with distance between user and camera (depth information) to learn affinity matrices. As a baseline method for comparison, we also present a traditional non deep learning approach that uses bipartite graph matching. To facilitate evaluation, we collected a multi-modal dataset that comprises camera videos with depth information (RGB-D), WiFi FTM and IMU measurements for multiple participants at diverse real-world settings. Using association accuracy as the key metric for evaluating the fidelity of 𝑉𝑖𝑠𝑢𝑎𝑙4 𝑉𝑖𝑠𝑢𝑎𝑙5 Figure 1: Motivation: Successfully associating vision-wireless Vi-Fi in associating human users on camera feed with their phone IDs, we show that Vi-Fi achieves between 81% (real-time) to 91% (offline) association accuracy.
ViTag: Online WiFi Fine Time Measurements Aided Vision-Motion Identity Association in Multi-person Environments
Authors: Bryan Bo Cao, Abrar Alali, Hansi Liu, Nicholas Meegan, Marco Gruteser, Kristin Dana, Ashwin Ashok, Shubham Jain
Date: 2022-09-20 00:00:00
Proceeding: IEEE International Conference on Sensing, Communication, and Networking 2022 9
Tasks: Decoder, Multimodal Association, Translation

In this paper, we present ViTag to associate user identities across multimodal data, particularly those obtained from cameras and smartphones. ViTag associates a sequence of vision tracker generated bounding boxes with Inertial Measurement Unit (IMU) data and Wi-Fi Fine Time Measurements (FTM) from smartphones. We formulate the problem as association by sequence to sequence (seq2seq) translation. In this two-step process, our system first performs cross-modal translation using a multimodal LSTM encoder-decoder network (X-Translator) that translates one modality to another, e.g. reconstructing IMU and FTM readings purely from camera bounding boxes. Second, an association module finds identity matches between camera and phone domains, where the translated modality is then matched with the observed data from the same modality. In contrast to existing works, our proposed approach can associate identities in multi-person scenarios where all users may be performing the same activity. Extensive experiments in real-world indoor and outdoor environments demonstrate that online association on camera and phone data (IMU and FTM) achieves an average Identity Precision Accuracy (IDP) of 88.39% on a 1 to 3 seconds window, outperforming the state-of-the-art Vi-Fi (82.93%). Further study on modalities within the phone domain shows the FTM can improve association performance by 12.56% on average. Finally, results from our sensitivity experiments demonstrate the robustness of ViTag under different noise and environment variations.
Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation
Authors: Mohit Shridhar, Lucas Manuelli, Dieter Fox
Date: 2022-09-12 00:00:00
Tasks: Robot Manipulation, Robot Manipulation Generalization

Transformers have revolutionized vision and natural language processing with their ability to scale with large datasets. But in robotic manipulation, data is both limited and expensive. Can manipulation still benefit from Transformers with the right problem formulation? We investigate this question with PerAct, a language-conditioned behavior-cloning agent for multi-task 6-DoF manipulation. PerAct encodes language goals and RGB-D voxel observations with a Perceiver Transformer, and outputs discretized actions by ``detecting the next best voxel action''. Unlike frameworks that operate on 2D images, the voxelized 3D observation and action space provides a strong structural prior for efficiently learning 6-DoF actions. With this formulation, we train a single multi-task Transformer for 18 RLBench tasks (with 249 variations) and 7 real-world tasks (with 18 variations) from just a few demonstrations per task. Our results show that PerAct significantly outperforms unstructured image-to-action agents and 3D ConvNet baselines for a wide range of tabletop tasks.
Soft Diffusion: Score Matching for General Corruptions
Authors: Giannis Daras, Mauricio Delbracio, Hossein Talebi, Alexandros G. Dimakis, Peyman Milanfar
Date: 2022-09-12 00:00:00
Tasks: Denoising, Image Generation

We define a broader family of corruption processes that generalizes previously known diffusion models. To reverse these general diffusions, we propose a new objective called Soft Score Matching that provably learns the score function for any linear corruption process and yields state of the art results for CelebA. Soft Score Matching incorporates the degradation process in the network. Our new loss trains the model to predict a clean image, \textit{that after corruption}, matches the diffused observation. We show that our objective learns the gradient of the likelihood under suitable regularity conditions for a family of corruption processes. We further develop a principled way to select the corruption levels for general diffusion processes and a novel sampling method that we call Momentum Sampler. We show experimentally that our framework works for general linear corruption processes, such as Gaussian blur and masking. We achieve state-of-the-art FID score $1.85$ on CelebA-64, outperforming all previous linear diffusion models. We also show significant computational benefits compared to vanilla denoising diffusion.