A sharp bound for the resurgence of sums of ideals

We prove a sharp upper bound for the resurgence of sums of ideals involving disjoint sets of variables, strengthening work of Bisui--H\`a--Jayanthan--Thomas. Complete solutions are delivered for two conjectures proposed by these authors. For given real numbers $a$ and $b$, we consider the set Res$(a,b)$ of possible values of the resurgence of $I+J$ where $I$ and $J$ are ideals in disjoint sets of variables having resurgence $a$ and $b$, respectively. Some questions and partial results about Res$(a,b)$ are discussed.Comment: 14 pages, 01 figur

Optimization for continuous overflow proteolytic hydrolysis of spent brewer’s yeast by using proteases

A large amount of spent yeast as by-product is annually generated from brewing industry and it contains about 50-55% protein with good balance of amino acids. The hydrolysate produced from spent brewer’s yeast may be used in food application. The yield of proteolylic hydrolysis for spent brewer’s yeast and amino acid contents of hydrolysates depend on factors such as temperature, pH value, type of used enzyme and ratio enzyme/substrate, time. Besides, applied hydrolysing methods (batch-, or continuous method) has effected on degree of hydrolysis. With the purpose of how proteolytic hydrolysis having effects on the spent brewer’s yeast for food application in industrial scale, continuous overflow method was used in this study. Bitterness of hydrolysate and the yield of continuous overflow proteolytic hydrolysis process are the two interested factors for protein hydrolysis. In this report, it is dealt with determination for optimal conditions to obtain the highest yield of hydrolysis process and the lowest bitterness of hydrolysate. Response surface methodology (RSM) was used to determine optimal condition for continuous overflow proteolytic hydrolysis of spent brewer’s yeast. The optimal conditions for obtaining high degree of hydrolysis and low bitterness are determined as followings: ratio of enzyme mixture (alcalase 7.5 U/g and flavourzyme 10 U/g), pH at 7.5, hydrolysis temperature at 51oC and hydrolysis time of 9 hours. Under the optimal conditions, the yield of hydrolysis was 59.62% ± 0.027 and the bitterness equivalently with concentration of quinine was 7.86 ± 0.033 μmol /ml

One-pot microwave-assisted green synthesis of amine-functionalized graphene quantum dots for high visible light photocatalytic application

Nowadays, graphene quantum dots (GQDs) have gained a huge interest in the field of visible-range photocatalysts because of their tunable band gap and stable photochemical properties. In this work, amine-functionalized GQDs (AGQDs) were successfully prepared by one-step microwave-assisted conversion of glucose, H2O2, and NH3 solution. The obtained quantum dots possess the high quality of graphene structure with the average size of 3.78 nm as well as exhibit a strong green fluorescence with a high quantum yield. Interestingly, the amine-functionalized dots perform outstanding visible-light absorption. To further investigate photocatalytic properties, a composite of AGQDs and TiO2 was then prepared by a simple mixing route. The hybrid material showed high catalytic activity of dye degradation under visible light irradiation, which indicates the key role of AGQDs in enhancing light absorption and induced electron–hole separation. The current study may open a new way for construction of effective visible light photocatalytic systems with a cost-effective, simple approach.Scopu

XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection

With the advancement of deep learning (DL) in various fields, there are many attempts to reveal software vulnerabilities by data-driven approach. Nonetheless, such existing works lack the effective representation that can retain the non-sequential semantic characteristics and contextual relationship of source code attributes. Hence, in this work, we propose XGV-BERT, a framework that combines the pre-trained CodeBERT model and Graph Neural Network (GCN) to detect software vulnerabilities. By jointly training the CodeBERT and GCN modules within XGV-BERT, the proposed model leverages the advantages of large-scale pre-training, harnessing vast raw data, and transfer learning by learning representations for training data through graph convolution. The research results demonstrate that the XGV-BERT method significantly improves vulnerability detection accuracy compared to two existing methods such as VulDeePecker and SySeVR. For the VulDeePecker dataset, XGV-BERT achieves an impressive F1-score of 97.5%, significantly outperforming VulDeePecker, which achieved an F1-score of 78.3%. Again, with the SySeVR dataset, XGV-BERT achieves an F1-score of 95.5%, surpassing the results of SySeVR with an F1-score of 83.5%

Anti-DreamBooth: Protecting users from personalized text-to-image synthesis

Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs. With powerful personalization tools like DreamBooth, they can generate images of a specific person just by learning from his/her few reference images. However, when misused, such a powerful and convenient tool can produce fake news or disturbing content targeting any individual victim, posing a severe negative social impact. In this paper, we explore a defense system called Anti-DreamBooth against such malicious use of DreamBooth. The system aims to add subtle noise perturbation to each user's image before publishing in order to disrupt the generation quality of any DreamBooth model trained on these perturbed images. We investigate a wide range of algorithms for perturbation optimization and extensively evaluate them on two facial datasets over various text-to-image model versions. Despite the complicated formulation of DreamBooth and Diffusion-based text-to-image models, our methods effectively defend users from the malicious use of those models. Their effectiveness withstands even adverse conditions, such as model or prompt/term mismatching between training and testing. Our code will be available at \href{https://github.com/VinAIResearch/Anti-DreamBooth.git}{https://github.com/VinAIResearch/Anti-DreamBooth.git}.Comment: Project page: https://anti-dreambooth.github.io

The Spitzer View of Low-Metallicity Star Formation: III. Fine Structure Lines, Aromatic Features, and Molecules

We present low- and high-resolution Spitzer/IRS spectra, supplemented by IRAC and MIPS measurements, of 22 blue compact dwarf (BCD) galaxies. The BCD sample spans a wide range in oxygen abundance [12+Log(O/H) between 7.4 and 8.3], and hardness of the interstellar radiation field (ISRF). The IRS spectra provide us with a rich set of diagnostics to probe the physics of star and dust formation in very low-metallicity environments. We find that metal-poor BCDs have harder ionizing radiation than metal-rich galaxies: [OIV] emission is roughly 4 times as common as [FeII] emission. They also have a more intense ISRF, as indicated by the 71 to 160micron luminosity ratio. Two-thirds of the sample (15 BCDs) show PAH features, although the fraction of PAH emission normalized to the total infrared (IR) luminosity is considerably smaller in metal-poor BCDs (~0.5%) than in metal-rich star-forming galaxies (~10%). We find several lines of evidence for a deficit of small PAH carriers at low metallicity, and attribute this to destruction by a hard, intense ISRF, only indirectly linked to metal abundance. Our IRS spectra reveal a variety of H2 rotational lines, and more than a third of the objects in our sample (8 BCDs) have >=3sigma detections in one or more of the four lowest-order transitions. The warm gas masses in the BCDs range from 10^3 to 10^8 Msun, and can be comparable to the neutral hydrogen gas mass; relative to their total IR luminosities, some BCDs contain more H2 than SINGS galaxies.Comment: Accepted by ApJ: 70 pages in draft form, 6 tables, 22 figure

Exploiting Secrecy Performance of Uplink NOMA in Cellular Networks

Funding Information: This work was supported in part by the Air Force Office of Scientific Research under Award FA9550-20-1-0090, and in part by the National Science Foundation under Grant CNS-2034218.Peer reviewedPublisher PD