Regularized Conditional Alignment for Multi-Domain Text Classification

The most successful multi-domain text classification (MDTC) approaches employ the shared-private paradigm to facilitate the enhancement of domain-invariant features through domain-specific attributes. Additionally, they employ adversarial training to align marginal feature distributions. Nevertheless, these methodologies encounter two primary challenges: (1) Neglecting class-aware information during adversarial alignment poses a risk of misalignment; (2) The limited availability of labeled data across multiple domains fails to ensure adequate discriminative capacity for the model. To tackle these issues, we propose a method called Regularized Conditional Alignment (RCA) to align the joint distributions of domains and classes, thus matching features within the same category and amplifying the discriminative qualities of acquired features. Moreover, we employ entropy minimization and virtual adversarial training to constrain the uncertainty of predictions pertaining to unlabeled data and enhance the model's robustness. Empirical results on two benchmark datasets demonstrate that our RCA approach outperforms state-of-the-art MDTC techniques.Comment: This paper has been accepted by ICASSP 202

Decoding flat bands from compact localized states

The flat band system is an ideal quantum platform to investigate the kaleidoscope created by the electron-electron correlation effects. The central ingredient of realizing a flat band is to find its compact localized states. In this work, we develop a systematic way to generate the compact localized states by designing destructive interference pattern from 1-dimensional chains. A variety of 2-dimensional new flat band systems are constructed with this method. Furthermore, we show that the method can be extended to generate the compact localized states in multi-orbital systems by carefully designing the block hopping scheme, as well as in quasicrystal and disorder systems

Analysis on Hydrophobic Membrane-Based Air Pre-Dehumidification and Capillary Radiation Air Conditioning System

Temperature and humidity are two important factors for human thermal comfort and air conditioning system. Traditional air conditioning system adjust temperature and humidity together in evaporator of refrigeration system, and the evaporation temperature is lower than the dew point temperature of air, accordingly, the thermal comfort is not easy satisfied and the whole energy efficiency ratio of refrigeration system is relative low. Temperature and humidity independent control of air conditioning system is an effective mode that can supply the needed indoor parameters with high energy efficiency ratio, simultaneously. Capillary network radiation system is a comfortable and energy-saving terminal energy supplements mode with no blowing feeling; however, it only adjust temperature and the condensation water often occurs in the capillary tube surface. Therefore, air pre-dehumidification process should handle first to improve the dew point temperature of air to ensure the higher temperature working fluid in capillary tube. In this study, a hydrophobic membrane-based air pre-dehumidification and capillary radiation air conditioning system is built. The hollow fibre membrane is acted as the dehumidifier when the concentrated lithium bromide solution with low temperature flows into the membrane while the wet air pass through the outside of membrane. The water vapour will pass across the membrane pores from wet air to lithium bromide solution and is absorbed, and then the diluted lithium bromide solution is regenerated by solar energy independently, furthermore, the temperature is controlled by capillary network radiation system. Finally, the influence factors of indoor parameters and the whole energy efficiency ratio are analysed

Key-Controlled Order-Preserving Encryption

In this paper we study order-preserving encryption (OPE), a primitive in the database community for allowing efficient range queries on ecrypted data. OPE was suggested by Agrawal et al [1], and was throughly studied by Boldyreva et al [2]. In this paper we present a practical OPE scheme, which is a key-controlled algorithm, based on simple computation. A primary analysis shows that our algorithm is secure enoug

AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand Pose

How human interact with objects depends on the functional roles of the target objects, which introduces the problem of affordance-aware hand-object interaction. It requires a large number of human demonstrations for the learning and understanding of plausible and appropriate hand-object interactions. In this work, we present AffordPose, a large-scale dataset of hand-object interactions with affordance-driven hand pose. We first annotate the specific part-level affordance labels for each object, e.g. twist, pull, handle-grasp, etc, instead of the general intents such as use or handover, to indicate the purpose and guide the localization of the hand-object interactions. The fine-grained hand-object interactions reveal the influence of hand-centered affordances on the detailed arrangement of the hand poses, yet also exhibit a certain degree of diversity. We collect a total of 26.7K hand-object interactions, each including the 3D object shape, the part-level affordance label, and the manually adjusted hand poses. The comprehensive data analysis shows the common characteristics and diversity of hand-object interactions per affordance via the parameter statistics and contacting computation. We also conduct experiments on the tasks of hand-object affordance understanding and affordance-oriented hand-object interaction generation, to validate the effectiveness of our dataset in learning the fine-grained hand-object interactions. Project page: https://github.com/GentlesJan/AffordPose.Comment: Accepted by ICCV 202

SAP: An IoT Application Module Placement Strategy Based on Simulated Annealing Algorithm in Edge-Cloud Computing

The Internet of Things (IoT) is rapidly growing and provides the foundation for the development of smart cities, smart home, and health care. With more and more devices connecting to the Internet, huge amounts of data are produced, creating a great challenge for data processing. Traditional cloud computing has the problems of long delays. Edge computing is an extension of cloud computing, processing data at the edge of the network can reduce the long processing delay of cloud computing. Due to the limited computing resources of edge servers, resource management of edge servers has become a critical research problem. However, the structural characteristics of the subtask chain between each pair of sensors and actuators are not considered to address the task scheduling problem in most existing research. To reduce processing latency and energy consumption of the edge-cloud system, we propose a multilayer edge computing system. The application deployed in the system is based on directed digraph. To fully use the edge servers, we proposed an application module placement strategy using Simulated Annealing module Placement (SAP) algorithm. The modules in an application are bounded to each sensor. The SAP algorithm is designed to find a module placement scheme for each sensor and to generate a module chain including the mapping of the module and servers for each sensor. Thus, the edge servers can transmit the tuples in the network with the module chain. To evaluate the efficacy of our algorithm, we simulate the strategy in iFogSim. Results show the scheme is able to achieve significant reductions in latency and energy consumption

Floating Fault analysis of Trivium under Weaker Assumptions

Trivium is a hardware-oriented stream cipher, and one of the finally chosen ciphers by eSTREAM project. Michal Hojsik and Bohuslav Rudolf presented an effective attack to Trivium, named floating fault analysis, at INDOCRYPT 2008. Their attack makes use of the fault injection and the fault float. In this paper, we present an improvement of this attack. Our attack is under following weaker and more practical assumptions.The fault injection can be made for the state at a random time.The positions of the fault bits are from random one of 3 NFSRs, and from a random area within 8 neighboring bits.We present a checking method, by which either the injecting time and fault positions can be determined, or the state differential at a known time can be determined. Each of these two determinations is enough for floating attack. After the determination, the attacker can averagely obtain 67.167 additional linear equations from 82 original quadratic equations, and obtain 66 additional quadratic equations from 66 original cubic equations

The Lower Bounds on the Second Order Nonlinearity of Cubic Boolean Functions

It is a difficult task to compute the $r$-th order nonlinearity of a given function with algebraic degree strictly greater than $r>1$. Even the lower bounds on the second order nonlinearity is known only for a few particular functions. We investigate the lower bounds on the second order nonlinearity of cubic Boolean functions $F_u(x)=Tr(\sum_{l=1}^{m}\mu_{l}x^{d_{l}})$, where $u_{l} \in F_{2^n}^{*}$, $d_{l}=2^{i_{l}}+2^{j_{l}}+1$, $i_{l}$ and $j_{l}$ are positive integers, $n>i_{l}> j_{l}$. Especially, for a class of Boolean functions $G_u(x)=Tr(\sum_{l=1}^{m}\mu_{l}x^{d_{l}})$, we deduce a tighter lower bound on the second order nonlinearity of the functions, where $u_{l} \in F_{2^n}^{*}$, $d_{l}=2^{i_{l}\gamma}+2^{j_{l}\gamma}+1$, $i_{l}> j_{l}$ and $\gamma\neq 1$ is a positive integer such that $gcd(n,\gamma)=1$. \\The lower bounds on the second order nonlinearity of cubic monomial Boolean functions, represented by $f_\mu(x)=Tr(\mu x^{2^i+2^j+1})$, $\mu\in F_{2^n}^*$, $i$ and $j$ are positive integers such that $i>j$, have recently (2009) been obtained by Gode and Gangopadhvay. Our results have the advantages over those of Gode and Gangopadhvay as follows. We first extend the results from monomial Boolean functions to Boolean functions with more trace terms. We further generalize and improve the results to a wider range of $n$. Also, our bounds are better than those of Gode and Gangopadhvay for monomial functions $f_\mu(x)$

Ignition and Flame Stabilization of a Strut-Jet RBCC Combustor with Small Rocket Exhaust

A Rocket Based Combined Cycle combustor model is tested at a ground direct connected rig to investigate the flame holding characteristics with a small rocket exhaust using liquid kerosene. The total temperature and the Mach number of the vitiated air flow, at exit of the nozzle are 1505 K and 2.6, respectively. The rocket base is embedded in a fuel injecting strut and mounted in the center of the combustor. The wall of the combustor is flush, without any reward step or cavity, so the strut-jet is used to make sure of the flame stabilization of the second combustion. Mass flow rate of the kerosene and oxygen injected into the rocket is set to be a small value, below 10% of the total fuel when the equivalence ratio of the second combustion is 1. The experiment has generated two different kinds of rocket exhaust: fuel rich and pure oxygen. Experiment result has shown that, with a relative small total mass flow rate of the rocket, the fuel rich rocket plume is not suitable for ignition and flame stabilization, while an oxygen plume condition is suitable. Then the paper conducts a series of experiments to investigate the combustion characteristics under this oxygen pilot method and found that the flame stabilization characteristics are different at different combustion modes

CDMPP: A Device-Model Agnostic Framework for Latency Prediction of Tensor Programs

Deep Neural Networks (DNNs) have shown excellent performance in a wide range of machine learning applications. Knowing the latency of running a DNN model or tensor program on a specific device is useful in various tasks, such as DNN graph- or tensor-level optimization and device selection. Considering the large space of DNN models and devices that impede direct profiling of all combinations, recent efforts focus on building a predictor to model the performance of DNN models on different devices. However, none of the existing attempts have achieved a cost model that can accurately predict the performance of various tensor programs while supporting both training and inference accelerators. We propose CDMPP, an efficient tensor program latency prediction framework for both cross-model and cross-device prediction. We design an informative but efficient representation of tensor programs, called compact ASTs, and a pre-order-based positional encoding method, to capture the internal structure of tensor programs. We develop a domain-adaption-inspired method to learn domain-invariant representations and devise a KMeans-based sampling algorithm, for the predictor to learn from different domains (i.e., different DNN operators and devices). Our extensive experiments on a diverse range of DNN models and devices demonstrate that CDMPP significantly outperforms state-of-the-art baselines with 14.03% and 10.85% prediction error for cross-model and cross-device prediction, respectively, and one order of magnitude higher training efficiency. The implementation and the expanded dataset are available at https://github.com/joapolarbear/cdmpp.Comment: Accepted by EuroSys 202