Long Cycles through a Linear Forest

AbstractFor a graph G and an integer k⩾1, let S(G)={x∈V(G):dG(x)=0} and σk(G)=min{∑ki=1dG(vi):{v1, v2, …, vk} is an independent set of G}. The main result of this paper is as follows. Let k⩾3, m⩾0, and 0⩽s⩽k−3. Let G be a (m+k−1)-connected graph and let F be a subgraph of G with |E(F)|=m and |S(F)|=s. If every component of F is a path, then G has a cycle of length ⩾min{|V(G)|, 2kσk(G)−m} passing through E(F)∪V(F). This generalizes three related results known previously

Exploiting Behavioral Consistence for Universal User Representation

User modeling is critical for developing personalized services in industry. A common way for user modeling is to learn user representations that can be distinguished by their interests or preferences. In this work, we focus on developing universal user representation model. The obtained universal representations are expected to contain rich information, and be applicable to various downstream applications without further modifications (e.g., user preference prediction and user profiling). Accordingly, we can be free from the heavy work of training task-specific models for every downstream task as in previous works. In specific, we propose Self-supervised User Modeling Network (SUMN) to encode behavior data into the universal representation. It includes two key components. The first one is a new learning objective, which guides the model to fully identify and preserve valuable user information under a self-supervised learning framework. The other one is a multi-hop aggregation layer, which benefits the model capacity in aggregating diverse behaviors. Extensive experiments on benchmark datasets show that our approach can outperform state-of-the-art unsupervised representation methods, and even compete with supervised ones.Comment: Preprint of accepted AAAI2021 pape

Single-Sample Finger Vein Recognition via Competitive and Progressive Sparse Representation

As an emerging biometric technology, finger vein recognition has attracted much attention in recent years. However, single-sample recognition is a practical and longstanding challenge in this field, referring to only one finger vein image per class in the training set. In single-sample finger vein recognition, the illumination variations under low contrast and the lack of information of intra-class variations severely affect the recognition performance. Despite of its high robustness against noise and illumination variations, sparse representation has rarely been explored for single-sample finger vein recognition. Therefore, in this paper, we focus on developing a new approach called Progressive Sparse Representation Classification (PSRC) to address the challenging issue of single-sample finger vein recognition. Firstly, as residual may become too large under the scenario of single-sample finger vein recognition, we propose a progressive strategy for representation refinement of SRC. Secondly, to adaptively optimize progressions, a progressive index called Max Energy Residual Index (MERI) is defined as the guidance. Furthermore, we extend PSRC to bimodal biometrics and propose a Competitive PSRC (C-PSRC) fusion approach. The C-PSRC creates more discriminative fused sample and fusion dictionary by comparing residual errors of different modalities. By comparing with several state-of-the-art methods on three finger vein benchmarks, the superiority of the proposed PSRC and C-PSRC is clearly demonstrated

Expressive and Secure Searchable Encryption in the Public Key Setting (Full Version)

Searchable encryption allows an untrusted server to search on encrypted data without knowing the underlying data contents. Traditional searchable encryption schemes focus only on single keyword or conjunctive keyword search. Several solutions have been recently proposed to design more expressive search criteria, but most of them are in the setting of symmetric key encryption. In this paper, based on the composite-order groups, we present an expressive and secure asymmetric searchable encryption (ESASE) scheme, which is the first that simultaneously supports conjunctive, disjunctive and negation search operations. We analyze the efficiency of ESASE and prove it is secure under the standard model. In addition, we show that how ESASE could be extended to support the range search and the multi-user setting

Improved Differential Cryptanalysis on SPECK Using Plaintext Structures

Plaintext structures are a commonly-used technique for improving differential cryptanalysis. Generally, there are two types of plaintext structures: multiple-differential structures and truncated-differential structures. Both types have been widely used in cryptanalysis of S-box-based ciphers while for SPECK, an Addition-Rotation-XOR (ARX) cipher, the truncated-differential structure has not been used so far. In this paper, we investigate the properties of modular addition and propose a method to construct truncated-differential structures for SPECK. Moreover, we show that a combination of both types of structures is also possible for SPECK. For recovering the key of SPECK, we propose dedicated algorithms and apply them to various differential distinguishers, which helps to obtain a series of improved attacks on all variants of SPECK. Notably, on SPECK128, the time complexity of the attack can be reduced by a factor up to 2^15. The results show that the combination of both structures helps to improve the data and time complexity at the same time, as in the cryptanalysis of S-box-based ciphers

Large Language Models are Parallel Multilingual Learners

In this study, we reveal an in-context learning (ICL) capability of multilingual large language models (LLMs): by translating the input to several languages, we provide Parallel Input in Multiple Languages (PiM) to LLMs, which significantly enhances their comprehension abilities. To test this capability, we design extensive experiments encompassing 8 typical datasets, 7 languages and 8 state-of-the-art multilingual LLMs. Experimental results show that (1) incorporating more languages help PiM surpass the conventional ICL further; (2) even combining with the translations that are inferior to baseline performance can also help. Moreover, by examining the activated neurons in LLMs, we discover a counterintuitive but interesting phenomenon. Contrary to the common thought that PiM would activate more neurons than monolingual input to leverage knowledge learned from diverse languages, PiM actually inhibits neurons and promotes more precise neuron activation especially when more languages are added. This phenomenon aligns with the neuroscience insight about synaptic pruning, which removes less used neural connections, strengthens remainders, and then enhances brain intelligence.Comment: Working in proces

High-power 1560 nm single-frequency erbium fiber amplifier core-pumped at 1480 nm

High-power continuous-wave single-frequency Er-doped fiber amplifiers at 1560 nm by in-band and core pumping of a 1480 nm Raman fiber laser are investigated in detail. Both co- and counter-pumping configurations are studied experimentally. Up to 59.1 W output and 90% efficiency were obtained in the fundamental mode and linear polarization in the co-pumped case, while less power and efficiency were achieved in the counter-pumped setup for additional loss. The amplifier performs indistinguishably in terms of laser linewidth and relative intensity noise in the frequency range up to 10 MHz for both configurations. However, the spectral pedestal is raised in co-pumping, caused by cross-phase modulation between the pump and signal laser, which is observed and analyzed for the first time. Nevertheless, the spectral pedestal is 34.9 dB below the peak, which has a negligible effect for most applications

Single-frequency upconverted laser generation by phase summation

The phase summation effect in sum-frequency mixing process is utilized to avoid a nonlinearity obstacle in the power scaling of single-frequency visible or ultraviolet lasers. Two single-frequency fundamental lasers are spectrally broadened by phase modulation to suppress stimulated Brillouin scattering in fiber amplifier and achieve higher power. After sum-frequency mixing in a nonlinear optical crystal, the upconverted laser returns to single frequency due to phase summation, when the phase modulations on two fundamental lasers have a similar amplitude but opposite sign. The method was experimentally proved in a Raman fiber amplifier-based laser system, which generated a power-scalable sideband-free single-frequency 590 nm laser. The proposal manifests the importance of phase operation in wave-mixing processes for precision laser technology