Several Improvements on BKZ Algorithm

Lattice problem such as NTRU problem and LWE problem is widely used as the security base of post-quantum cryptosystems. And currently doing lattice reduction by BKZ algorithm is the most efficient way to solve it. In this paper, we give several further improvements on BKZ algorithm, which can be used for different SVP subroutines base on both enumeration and sieving. These improvements in combination provide a speed up of $2^{3\sim 4}$ in total. It is significant in concrete attacks. Using these new techniques, we solved the 656 dimensional ideal lattice challenge in only 380 thread hours (also with a enumeration based SVP subroutine), much less than the previous records (which costs 4600 thread hours in total). With these improvements enabled, we can still simulate the new BKZ algorithm easily. One can also use this simulator to find the blocksize strategy (and the corresponding cost) to make $\mathrm{Pot}$ of the basis (defined in section 5.2) decrease as fast as possible, which means the length of the first basis vector decrease the fastest if we accept the GSA assumption. It is useful for analyzing concrete attacks on lattice-based cryptography

Novel Quasi‐Liquid K‐Na Alloy as a Promising Dendrite‐Free Anode for Rechargeable Potassium Metal Batteries

Rechargeable potassium metal batteries are promising energy storage devices with potentially high energy density and markedly low cost. However, eliminating dendrite growth and achieving a stable electrode/electrolyte interface are the key challenges to tackle. Herein, a novel "quasi-liquid" potassium-sodium alloy (KNA) anode comprising only 3.5 wt% sodium (KNA-3.5) is reported, which exhibits outstanding electrochemical performance able to be reversibly cycled at 4 mA cm-2 for 2000 h. Moreover, it is demonstrated that adding a small amount of sodium hexafluorophosphate (NaPF6 ) into the potassium bis(fluorosulfonyl)imide electrolyte allows for the formation of the "quasi-liquid" KNA on electrode surface. Comprehensive experimental studies reveal the formation of an unusual metastable KNa2 phase during plating, which is believed to facilitate simultaneous nucleation and suppress the growth of dendrites, thereby improving the electrode's cycle lifetime. The "quasi-liquid" KNA-3.5 anode demonstrates markedly enhanced electrochemical performance in a full cell when pairing with Prussian blue analogs or sodium rhodizonate dibasic as the cathode material, compared to the pristine potassium anode. Importantly, unlike the liquid KNA reported before, the "quasi-liquid" KNA-3.5 exhibits good processability and can be readily shaped into sheet electrodes, showing substantial promise as a dendrite-free anode in rechargeable potassium metal batteries.Z.T. acknowledges the financial support of Maria Curie COFUND fellowship (Grant No. 713640). Z.L. thanks the financial support of China Scholarship Council (Grant No. 201 806 400 066). This project was partly funded by the “Baterias 2030” project through the Mobilizadore Programme by the National Innovation Agency of Portugal (Grant No. POCI-01-0247- FEDER-046109). G.Y. acknowledges the financial support from the Welch Foundation Award F-1861. The authors thank Dr. Artur Martins for his assistance in mechanical property measurement.info:eu-repo/semantics/publishedVersio

SPA-GPT: General Pulse Tailor for Simple Power Analysis Based on Reinforcement Learning

Power analysis of public-key algorithms is a well-known approach in the community of side-channel analysis. We usually classify operations based on the differences in power traces produced by different basic operations (such as modular exponentiation) to recover secret information like private keys. The more accurate the segmentation of power traces, the higher the efficiency of their classification. There exist two commonly used methods: one is equidistant segmentation, which requires a fixed number of basic operations and similar trace lengths for each type of operation, leading to limited application scenarios; the other is peak-based segmentation, which relies on personal experience to configure parameters, resulting in insufficient flexibility and poor universality. In this paper, we propose an automated power trace segmentation method based on reinforcement learning algorithms, which is applicable to a wide range of common implementation of public-key algorithms. Reinforcement learning is an unsupervised machine learning technique that eliminates the need for manual label collection. For the first time, this technique is introduced into the field of side-channel analysis for power trace processing. By using prioritized experience replay optimized Deep Q-Network algorithm, we reduce the number of parameters required to achieve accurate segmentation of power traces to only one, i.e. the key length. We also employ various techniques to improve the segmentation effectiveness, such as clustering algorithm, enveloped-based feature enhancement and fine-tuning method. We validate the effectiveness of the new method in nine scenarios involving hardware and software implementations of different public-key algorithms executed on diverse platforms such as microcontrollers, SAKURA-G, and smart cards. Specifically, one of these implementations is protected by time randomization countermeasures. Experimental results show that our method has good robustness on the traces with varying segment lengths and differing peak heights. After employ the clustering algorithm, our method achieves an accuracy of over 99.6% in operations recovery. Besides, power traces collected from these devices have been uploaded as databases, which are available for researchers engaged in public-key algorithms to conduct related experiments or verify our method

Constraints on the spacetime metric around seven "bare" AGNs using X-ray reflection spectroscopy

We present the study of a sample of seven "bare" active galactic nuclei (AGN) observed with Suzaku. We interpret the spectrum of these sources with a relativistic reflection component and we employ our model RELXILL_NK to test the Kerr nature of their supermassive black holes. We constrain the Johannsen deformation parameters $\alpha_{13}$ and $\alpha_{22}$, in which the Kerr metric is recovered when $\alpha_{13} = \alpha_{22} = 0$. All our measurements are consistent with the hypothesis that the spacetime geometry around these supermassive objects is described by the Kerr solution. For some sources, we obtain quite strong constraints on $\alpha_{13}$ and $\alpha_{22}$ when compared to those found in our previous studies. We discuss the systematic uncertainties in our tests and the implications of our results.Comment: 15 pages, 12 figures. v2: refereed versio

TfR1 binding with H-ferritin nanocarrier achieves prognostic diagnosis and enhances the therapeutic efficacy in clinical gastric cancer

H-ferritin (HFn) nanocarrier is emerging as a promising theranostic platform for tumor diagnosis and therapy, which can specifically target tumor cells via binding transferrin receptor 1 (TfR1). This led us to investigate the therapeutic function of TfR1 in GC. The clinical significance of TfR1 was assessed in 178 GC tissues by using a magneto-HFn nanoparticle-based immunohistochemistry method. The therapeutic effects of doxorubicin-loaded HFn nanocarriers (HFn-Dox) were evaluated on TfR1-positive GC patient-derived xenograft (GC-PDX) models. The biological function of TfR1 was investigated through in vitro and in vivo assays. TfR1 was upregulated (73.03%) in GC tissues, and reversely correlated with patient outcome. TfR1-negative sorted cells exhibited tumor-initiating features, which enhanced tumor formation and migration/invasion, whereas TfR1-positive sorted cells showed significant proliferation ability. Knockout of TfR1 in GC cells also enhanced cell invasion. TfR1-deficient cells displayed immune escape by upregulating PD-L1, CXCL9, and CXCL10, when disposed with IFN-γ. Western blot results demonstrated that TfR1-knockout GC cells upregulated Akt and STAT3 signaling. Moreover, in TfR1-positive GC-PDX models, the HFn-Dox group significantly inhibited tumor growth, and increased mouse survival, compared with that of free-Dox group. TfR1 could be a potential prognostic and therapeutic biomarker for GC: (i) TfR1 reversely correlated with patient outcome, and its negative cells possessed tumor-aggressive features; (ii) TfR1-positive cells can be killed by HFn drug nanocarrier. Given the heterogeneity of GC, HFn drug nanocarrier combined with other therapies toward TfR1-negative cells (such as small molecules or immunotherapy) will be a new option for GC treatment

How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?

Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for long-tailed, multi-label datasets commonly found in clinical settings. This knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient well-being. To fill this gap, we perform the first analysis of pruning's effect on neural networks trained to diagnose thorax diseases from chest X-rays (CXRs). On two large CXR datasets, we examine which diseases are most affected by pruning and characterize class "forgettability" based on disease frequency and co-occurrence behavior. Further, we identify individual CXRs where uncompressed and heavily pruned models disagree, known as pruning-identified exemplars (PIEs), and conduct a human reader study to evaluate their unifying qualities. We find that radiologists perceive PIEs as having more label noise, lower image quality, and higher diagnosis difficulty. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification. All code, model weights, and data access instructions can be found at https://github.com/VITA-Group/PruneCXR.Comment: Early accepted to MICCAI 202

High-risk genotypes for type 1 diabetes are associated with the imbalance of gut microbiome and serum metabolites

BackgroundThe profile of gut microbiota, serum metabolites, and lipids of type 1 diabetes (T1D) patients with different human leukocyte antigen (HLA) genotypes remains unknown. We aimed to explore gut microbiota, serum metabolites, and lipids signatures in individuals with T1D typed by HLA genotypes.MethodsWe did a cross-sectional study that included 73 T1D adult patients. Patients were categorized into two groups according to the HLA haplotypes they carried: those with any two of three susceptibility haplotypes (DR3, DR4, DR9) and without any of the protective haplotypes (DR8, DR11, DR12, DR15, DR16) were defined as high-risk HLA genotypes group (HR, n=30); those with just one or without susceptibility haplotypes as the non-high-risk HLA genotypes group (NHR, n=43). We characterized the gut microbiome profile with 16S rRNA gene amplicon sequencing and analyzed serum metabolites with liquid chromatography-mass spectrometry.ResultsStudy individuals were 32.5 (8.18) years old, and 60.3% were female. Compared to NHR, the gut microbiota of HR patients were characterized by elevated abundances of Prevotella copri and lowered abundances of Parabacteroides distasonis. Differential serum metabolites (hypoxanthine, inosine, and guanine) which increased in HR were involved in purine metabolism. Different lipids, phosphatidylcholines and phosphatidylethanolamines, decreased in HR group. Notably, Parabacteroides distasonis was negatively associated (p ≤ 0.01) with hypoxanthine involved in purine metabolic pathways.ConclusionsThe present findings enabled a better understanding of the changes in gut microbiome and serum metabolome in T1D patients with HLA risk genotypes. Alterations of the gut microbiota and serum metabolites may provide some information for distinguishing T1D patients with different HLA risk genotypes