282 research outputs found
Enabling Cost-Effective Blockchain Applications via Workload-Adaptive Transaction Execution
As transaction fees skyrocket today, blockchains become increasingly
expensive, hurting their adoption in broader applications.
This work tackles the saving of transaction fees for economic blockchain
applications. The key insight is that other than the existing "default" mode to
execute application logic fully on-chain, i.e., in smart contracts, and in fine
granularity, i.e., user request per transaction, there are alternative
execution modes with advantages in cost-effectiveness.
On Ethereum, we propose a holistic middleware platform supporting flexible
and secure transaction executions, including off-chain states and batching of
user requests. Furthermore, we propose control-plane schemes to adapt the
execution mode to the current workload for optimal runtime cost.
We present a case study on the institutional accounts (e.g., coinbase.com)
intensively sending Ether on Ethereum blockchains. By collecting real-life
transactions, we construct workload benchmarks and show that our work saves 18%
~ 47% per invocation than the default baseline while introducing 1.81 ~ 16.59
blocks delay
On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond
Real-world data often exhibit imbalanced label distributions. Existing
studies on data imbalance focus on single-domain settings, i.e., samples are
from the same data distribution. However, natural data can originate from
distinct domains, where a minority class in one domain could have abundant
instances from other domains. We formalize the task of Multi-Domain Long-Tailed
Recognition (MDLT), which learns from multi-domain imbalanced data, addresses
label imbalance, domain shift, and divergent label distributions across
domains, and generalizes to all domain-class pairs. We first develop the
domain-class transferability graph, and show that such transferability governs
the success of learning in MDLT. We then propose BoDA, a theoretically grounded
learning strategy that tracks the upper bound of transferability statistics,
and ensures balanced alignment and calibration across imbalanced domain-class
distributions. We curate five MDLT benchmarks based on widely-used multi-domain
datasets, and compare BoDA to twenty algorithms that span different learning
strategies. Extensive and rigorous experiments verify the superior performance
of BoDA. Further, as a byproduct, BoDA establishes new state-of-the-art on
Domain Generalization benchmarks, highlighting the importance of addressing
data imbalance across domains, which can be crucial for improving
generalization to unseen domains. Code and data are available at:
https://github.com/YyzHarry/multi-domain-imbalance.Comment: ECCV 202
How to Rationally Select Your Delegatee in PoS
This paper centers around a simple yet crucial question for everyday users:
How should one choose their delegated validators within proof-of-stake (PoS)
protocols, particularly in the context of Ethereum 2.0? This has been a
long-overlooked gap, as existing studies have primarily focused on
inter-committee (validator set) behaviors and activities, while neglecting the
dynamic formation of committees, especially for individual stakeholders seeking
reliable validators. Our study bridges this gap by diving into the delegation
process (normal users delegate their small-value tokens to delegatees who later
act as validators) before entering an actual consensus phase.
We propose a Bayesian model to quantify normal users' trust in delegatees,
which we further incorporate into a game-theoretical model to simulate users'
reactions against a set of critical factors identified through extensive
research (including 10+ staking service provider as well as 30+ PoS
blockchains). Our results reveal that users tend to choose their delegatees and
utilize their tokens by carefully weighing the delegation cost, the behaviors
of other users, and the reputation of delegatees, ultimately reaching a Nash
equilibrium. Unfortunately, the collective trend significantly increases the
likelihood of token concentration on a small number of delegatees
Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations
We propose to learn to generate grasping motion for manipulation with a
dexterous hand using implicit functions. With continuous time inputs, the model
can generate a continuous and smooth grasping plan. We name the proposed model
Continuous Grasping Function (CGF). CGF is learned via generative modeling with
a Conditional Variational Autoencoder using 3D human demonstrations. We will
first convert the large-scale human-object interaction trajectories to robot
demonstrations via motion retargeting, and then use these demonstrations to
train CGF. During inference, we perform sampling with CGF to generate different
grasping plans in the simulator and select the successful ones to transfer to
the real robot. By training on diverse human data, our CGF allows
generalization to manipulate multiple objects. Compared to previous planning
algorithms, CGF is more efficient and achieves significant improvement on
success rate when transferred to grasping with the real Allegro Hand. Our
project page is at https://jianglongye.com/cgf .Comment: Project page: https://jianglongye.com/cg
The Perspectives of E-Commerce System and Innovation Economic Product Development: Mathematical and Computer Modeling Strategies Based on Virtual Reality Technology and Medical Diagnostics
Developing the e-commerce system as a product of health economics is the research team's research task. We performed computer modeling strategy analysis using virtual reality technology principles and clinical diagnostics. We tried to put forward a computer model for solving the problem of remote medical care and carried out the necessary feasibility analysis. I put forward a strategy in light of the key knowledge of cross-specialties, and carried out reports and expositions to address the technical difficulties of the present
Efficiently Hardening SGX Enclaves against Memory Access Pattern Attacks via Dynamic Program Partitioning
Intel SGX is known to be vulnerable to a class of practical attacks
exploiting memory access pattern side-channels, notably page-fault attacks and
cache timing attacks. A promising hardening scheme is to wrap applications in
hardware transactions, enabled by Intel TSX, that return control to the
software upon unexpected cache misses and interruptions so that the existing
side-channel attacks exploiting these micro-architectural events can be
detected and mitigated. However, existing hardening schemes scale only to
small-data computation, with a typical working set smaller than one or few
times (e.g., times) of a CPU data cache.
This work tackles the data scalability and performance efficiency of security
hardening schemes of Intel SGX enclaves against memory-access pattern side
channels. The key insight is that the size of TSX transactions in the target
computation is critical, both performance- and security-wise. Unlike the
existing designs, this work dynamically partitions target computations to
enlarge transactions while avoiding aborts, leading to lower performance
overhead and improved side-channel security. We materialize the dynamic
partitioning scheme and build a C++ library to monitor and model cache
utilization at runtime. We further build a data analytical system using the
library and implement various external oblivious algorithms. Performance
evaluation shows that our work can effectively increase transaction size and
reduce the execution time by up to two orders of magnitude compared with the
state-of-the-art solutions
Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems
Kalman Filter (KF) is widely used in various domains to perform sequential
learning or variable estimation. In the context of autonomous vehicles, KF
constitutes the core component of many Advanced Driver Assistance Systems
(ADAS), such as Forward Collision Warning (FCW). It tracks the states
(distance, velocity etc.) of relevant traffic objects based on sensor
measurements. The tracking output of KF is often fed into downstream logic to
produce alerts, which will then be used by human drivers to make driving
decisions in near-collision scenarios. In this paper, we study adversarial
attacks on KF as part of the more complex machine-human hybrid system of
Forward Collision Warning. Our attack goal is to negatively affect human
braking decisions by causing KF to output incorrect state estimations that lead
to false or delayed alerts. We accomplish this by sequentially manipulating
measure ments fed into the KF, and propose a novel Model Predictive Control
(MPC) approach to compute the optimal manipulation. Via experiments conducted
in a simulated driving environment, we show that the attacker is able to
successfully change FCW alert signals through planned manipulation over
measurements prior to the desired target time. These results demonstrate that
our attack can stealthily mislead a distracted human driver and cause vehicle
collisions.Comment: Accepted by AAAI2
A theoretical framework of immune cell phenotypic classification and discovery
Immune cells are highly heterogeneous and show diverse phenotypes, but the underlying mechanism remains to be elucidated. In this study, we proposed a theoretical framework for immune cell phenotypic classification based on gene plasticity, which herein refers to expressional change or variability in response to conditions. The system contains two core points. One is that the functional subsets of immune cells can be further divided into subdivisions based on their highly plastic genes, and the other is that loss of phenotype accompanies gain of phenotype during phenotypic conversion. The first point suggests phenotypic stratification or layerability according to gene plasticity, while the second point reveals expressional compatibility and mutual exclusion during the change in gene plasticity states. Abundant transcriptome data analysis in this study from both microarray and RNA sequencing in human CD4 and CD8 single-positive T cells, B cells, natural killer cells and monocytes supports the logical rationality and generality, as well as expansibility, across immune cells. A collection of thousands of known immunophenotypes reported in the literature further supports that highly plastic genes play an important role in maintaining immune cell phenotypes and reveals that the current classification model is compatible with the traditionally defined functional subsets. The system provides a new perspective to understand the characteristics of dynamic, diversified immune cell phenotypes and intrinsic regulation in the immune system. Moreover, the current substantial results based on plasticitomics analysis of bulk and single-cell sequencing data provide a useful resource for big-data–driven experimental studies and knowledge discoveries
Dynamic Handover: Throw and Catch with Bimanual Hands
Humans throw and catch objects all the time. However, such a seemingly common
skill introduces a lot of challenges for robots to achieve: The robots need to
operate such dynamic actions at high-speed, collaborate precisely, and interact
with diverse objects. In this paper, we design a system with two multi-finger
hands attached to robot arms to solve this problem. We train our system using
Multi-Agent Reinforcement Learning in simulation and perform Sim2Real transfer
to deploy on the real robots. To overcome the Sim2Real gap, we provide multiple
novel algorithm designs including learning a trajectory prediction model for
the object. Such a model can help the robot catcher has a real-time estimation
of where the object will be heading, and then react accordingly. We conduct our
experiments with multiple objects in the real-world system, and show
significant improvements over multiple baselines. Our project page is available
at \url{https://binghao-huang.github.io/dynamic_handover/}.Comment: Accepted at CoRL 2023.
https://binghao-huang.github.io/dynamic_handover
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