Prognostic Outcomes and Risk Factors for Patients with Renal Cell Carcinoma and Venous Tumor Thrombus after Radical Nephrectomy and Thrombectomy: The Prognostic Significance of Venous Tumor Thrombus Level.

IntroductionTo evaluate the prognostic outcomes and risk factors for renal cell carcinoma (RCC) patients with venous tumor thrombus in China.Materials and methodsWe reviewed the clinical information of 169 patients who underwent radical nephrectomy and thrombectomy. Overall and cancer-specific survival rates were analyzed. Univariate and multivariate analyses were used to investigate the potential prognostic factors.ResultsThe median survival time was 63 months. The five-year overall survival and cancer-specific survival rate were 53.6% and 54.4% for all patients. For all patients, significant survival difference was only observed between early (below hepatic vein) and advanced (above hepatic vein) tumor thrombus. However, significant differences existed between both RV/IVC and early/advanced tumor thrombus groups in N0M0 patients. Multivariate analysis demonstrated that higher tumor thrombus level (p = 0.016, RR = 1.58), N (p = 0.013, RR = 2.60), and M (p < 0.001, RR = 4.14) stages and adrenal gland invasion (p = 0.001, RR = 4.91) were the most significant negative prognostic predictors.ConclusionsIn this study, we reported most cases of RCC patients with venous extension in China. We proved that patients with RCC and venous tumor thrombus may have relative promising long-term survival rate, especially those with early tumor thrombus

A Blockchain-based Long-term Time-Stamping Scheme

Traditional time-stamping services confirm the existence time of data items by using a time-stamping authority. In order to eliminate trust requirements on this authority, decentralized Blockchain-based Time-Stamping (BTS) services have been proposed. In these services, a hash digest of users’ data is written into a blockchain transaction. The security of such services relies on the security of hash functions used to hash the data, and of the cryptographic algorithms used to build the blockchain. It is well-known that any single cryptographic algorithm has a limited lifespan due to the increasing computational power of attackers. This directly impacts the security of the BTS services from a long-term perspective. However, the topic of long-term security has not been discussed in the existing BTS proposals. In this paper, we propose the first formal definition and security model of a Blockchainbased Long-Term Time-Stamping (BLTTS) scheme. To develop a BLTTS scheme, we first consider an intuitive solution that directly combines the BTS services and a long-term secure blockchain, but we prove that this solution is vulnerable to attacks in the long term. With this insight, we propose the first BLTTS scheme supporting cryptographic algorithm renewal. We show that the security of our scheme over the long term is not limited by the lifespan of any underlying cryptographic algorithm, and we successfully implement the proposed scheme under existing BTS services

Analysis of Client-side Security for Long-term Time-stamping Services

Time-stamping services produce time-stamp tokens as evidence to prove that digital data existed at given points in time. Time-stamp tokens contain verifiable cryptographic bindings between data and time, which are produced using cryptographic algorithms. In the ANSI, ISO/IEC and IETF standards for time-stamping services, cryptographic algorithms are addressed in two aspects: (i) Client-side hash functions used to hash data into digests for nondisclosure. (ii) Server-side algorithms used to bind the time and digests of data. These algorithms are associated with limited lifespans due to their operational life cycles and increasing computational powers of attackers. After the algorithms are compromised, time-stamp tokens using the algorithms are no longer trusted. The ANSI and ISO/IEC standards provide renewal mechanisms for time-stamp tokens. However, the renewal mechanisms for client-side hash functions are specified ambiguously, that may lead to the failure of implementations. Besides, in existing papers, the security analyses of long-term time-stamping schemes only cover the server-side renewal, and the client-side renewal is missing. In this paper, we analyse the necessity of client-side renewal, and propose a comprehensive long-term time-stamping scheme that addresses both client-side renewal and server-side renewal mechanisms. After that, we formally analyse and evaluate the client-side security of our proposed scheme

Melt compounding with graphene to develop functional, high-performance elastomers

Rather than using graphene oxide, which is limited by a high defect concentration and cost due to oxidation and reduction, we adopted cost-effective, 3.56 nm thick graphene platelets (GnPs) of high structural integrity to melt compound with an elastomer—ethylene–propylene–diene monomer rubber (EPDM)—using an industrial facility. An elastomer is an amorphous, chemically crosslinked polymer generally having rather low modulus and fracture strength but high fracture strain in comparison with other materials; and upon removal of loading, it is able to return to its original geometry, immediately and completely. It was found that most GnPs dispersed uniformly in the elastomer matrix, although some did form clusters. A percolation threshold of electrical conductivity at 18 vol% GnPs was observed and the elastomer thermal conductivity increased by 417% at 45 vol% GnPs. The modulus and tensile strength increased by 710% and 404% at 26.7 vol% GnPs, respectively. The modulus improvement agrees well with the Guth and Halpin-Tsai models. The reinforcing effect of GnPs was compared with silicate layers and carbon nanotube. Our simple fabrication would prolong the service life of elastomeric products used in dynamic loading, thus reducing thermosetting waste in the environment

Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling

In many industrial applications like online advertising and recommendation systems, diverse and accurate user profiles can greatly help improve personalization. For building user profiles, deep learning is widely used to mine expressive tags to describe users' preferences from their historical actions. For example, tags mined from users' click-action history can represent the categories of ads that users are interested in, and they are likely to continue being clicked in the future. Traditional solutions usually introduce multiple independent Two-Tower models to mine tags from different actions, e.g., click, conversion. However, the models cannot learn complementarily and support effective training for data-sparse actions. Besides, limited by the lack of information fusion between the two towers, the model learning is insufficient to represent users' preferences on various topics well. This paper introduces a novel multi-task model called Mixture of Virtual-Kernel Experts (MVKE) to learn multiple topic-related user preferences based on different actions unitedly. In MVKE, we propose a concept of Virtual-Kernel Expert, which focuses on modeling one particular facet of the user's preference, and all of them learn coordinately. Besides, the gate-based structure used in MVKE builds an information fusion bridge between two towers, improving the model's capability much and maintaining high efficiency. We apply the model in Tencent Advertising System, where both online and offline evaluations show that our method has a significant improvement compared with the existing ones and brings about an obvious lift to actual advertising revenue.Comment: 10 pages, under revie

Melt compounding with graphene to develop functional, high-performance elastomers

Rather than using graphene oxide, which is limited by a high defect concentration and cost due to oxidation and reduction, we adopted cost-effective, 3.56 nm thick graphene platelets (GnPs) of high structural integrity to melt compound with an elastomer—ethylene–propylene–diene monomer rubber (EPDM)—using an industrial facility. An elastomer is an amorphous, chemically crosslinked polymer generally having rather low modulus and fracture strength but high fracture strain in comparison with other materials; and upon removal of loading, it is able to return to its original geometry, immediately and completely. It was found that most GnPs dispersed uniformly in the elastomer matrix, although some did form clusters. A percolation threshold of electrical conductivity at 18 vol% GnPs was observed and the elastomer thermal conductivity increased by 417% at 45 vol% GnPs. The modulus and tensile strength increased by 710% and 404% at 26.7 vol% GnPs, respectively. The modulus improvement agrees well with the Guth and Halpin-Tsai models. The reinforcing effect of GnPs was compared with silicate layers and carbon nanotube. Our simple fabrication would prolong the service life of elastomeric products used in dynamic loading, thus reducing thermosetting waste in the environment

FEASE: Fast and Expressive Asymmetric Searchable Encryption

Asymmetric Searchable Encryption (ASE) is a promising cryptographic mechanism that enables a semi-trusted cloud server to perform keyword searches over encrypted data for users. To be useful, an ASE scheme must support expressive search queries, which are expressed as conjunction, disjunction, or any Boolean formulas. In this paper, we propose a fast and expressive ASE scheme that is adaptively secure, called FEASE. It requires only 3 pairing operations for searching any conjunctive set of keywords independent of the set size and has linear complexity for encryption and trapdoor algorithms in the number of keywords. FEASE is based on a new fast Anonymous Key-Policy Attribute-Based Encryption (A-KP-ABE) scheme as our first proposal, which is of independent interest. To address optional protection against keyword guessing attacks, we extend FEASE into the first expressive Public-Key Authenticated Encryption with Keyword Search (PAEKS) scheme. We provide implementations and evaluate the performance of all three schemes, while also comparing them with the state of the art. We observe that FEASE outperforms all existing expressive ASE constructions and that our A-KP-ABE scheme offers anonymity with efficiency comparable to the currently fastest yet non-anonymous KP-ABE schemes FAME (ACM CCS 2017) and FABEO (ACM CCS 2022)

USED: Universal Speaker Extraction and Diarization

Speaker extraction and diarization are two crucial enabling techniques for speech applications. Speaker extraction aims to extract a target speaker's voice from a multi-talk mixture, while speaker diarization demarcates speech segments by speaker, identifying `who spoke when'. The previous studies have typically treated the two tasks independently. However, the two tasks share a similar objective, that is to disentangle the speakers in the spectral domain for the former but in the temporal domain for the latter. It is logical to believe that the speaker turns obtained from speaker diarization can benefit speaker extraction, while the extracted speech offers more accurate speaker turns than the mixture speech. In this paper, we propose a unified framework called Universal Speaker Extraction and Diarization (USED). We extend the existing speaker extraction model to simultaneously extract the waveforms of all speakers. We also employ a scenario-aware differentiated loss function to address the problem of sparsely overlapped speech in real-world conversations. We show that the USED model significantly outperforms the baselines for both speaker extraction and diarization tasks, in both highly overlapped and sparsely overlapped scenarios. Audio samples are available at https://ajyy.github.io/demo/USED/.Comment: Submitted to ICASSP 202