Building K-Anonymous User Cohorts with\\ Consecutive Consistent Weighted Sampling (CCWS)

To retrieve personalized campaigns and creatives while protecting user privacy, digital advertising is shifting from member-based identity to cohort-based identity. Under such identity regime, an accurate and efficient cohort building algorithm is desired to group users with similar characteristics. In this paper, we propose a scalable $K$-anonymous cohort building algorithm called {\em consecutive consistent weighted sampling} (CCWS). The proposed method combines the spirit of the ($p$-powered) consistent weighted sampling and hierarchical clustering, so that the $K$-anonymity is ensured by enforcing a lower bound on the size of cohorts. Evaluations on a LinkedIn dataset consisting of $>70$M users and ads campaigns demonstrate that CCWS achieves substantial improvements over several hashing-based methods including sign random projections (SignRP), minwise hashing (MinHash), as well as the vanilla CWS

Surface-SOS:Self-Supervised Object Segmentation via Neural Surface Representation

Self-supervised Object Segmentation (SOS) aims to segment objects without any annotations. Under conditions of multi-camera inputs, the structural, textural and geometrical consistency among each view can be leveraged to achieve fine-grained object segmentation. To make better use of the above information, we propose Surface representation based Self-supervised Object Segmentation (Surface-SOS), a new framework to segment objects for each view by 3D surface representation from multi-view images of a scene. To model high-quality geometry surfaces for complex scenes, we design a novel scene representation scheme, which decomposes the scene into two complementary neural representation modules respectively with a Signed Distance Function (SDF). Moreover, Surface-SOS is able to refine single-view segmentation with multi-view unlabeled images, by introducing coarse segmentation masks as additional input. To the best of our knowledge, Surface-SOS is the first self-supervised approach that leverages neural surface representation to break the dependence on large amounts of annotated data and strong constraints. These constraints typically involve observing target objects against a static background or relying on temporal supervision in videos. Extensive experiments on standard benchmarks including LLFF, CO3D, BlendedMVS, TUM and several real-world scenes show that Surface-SOS always yields finer object masks than its NeRF-based counterparts and surpasses supervised single-view baselines remarkably.</p

Diverse Consequences in Liver Injury in Mice with Different Autophagy Functional Status Treated with Alcohol

Alcoholic fatty liver disease is often complicated by other pathologic insults, such as viral infection or high-fat diet. Autophagy plays a homeostatic role in the liver but can be compromised by alcohol, high-fat diet, or viral infection, which in turn affects the disease process caused by these etiologies. To understand the full impact of autophagy modulation on alcohol-induced liver injury, several genetic models of autophagy deficiency, which have different levels of functional alterations, were examined after acute binge or chronic-plus-binge treatment. Mice given alcohol with either mode and induced with deficiency in liver-specific autophagy-related protein (Atg)-7 shortly after the induction of Atg7 deletion had elevated liver injury, indicating the protective role of autophagy. Constitutive hepatic Atg7–deficient mice, in which Atg7 was deleted in embryos, were more susceptible with chronic-plus-binge but not with acute alcohol treatment. Constitutive hepatic Atg5–deficient mice, in which Atg5 was deleted in embryos, were more susceptible with acute alcohol treatment, but liver injury was unexpectedly improved with the chronic-plus-binge regimen. A prolonged Atg deficiency may complicate the hepatic response to alcohol treatment, likely in part due to endogenous liver injury. The complexity of the relationship between autophagy deficiency and alcohol-induced liver injury can thus be affected by the timing of autophagy dysfunction, the exact autophagy gene being affected, and the alcohol treatment regimen

Systematic review on the treatment of pentoxifylline in patients with non-alcoholic fatty liver disease

<p>Abstract</p> <p>Background</p> <p>As an anti-TNF agent that targets inflammatory process directly, Pentoxifylline has been investigated for treatment of NASH in individual studies and pilot trials for years. We summarized the available information and generating hypotheses for future research.</p> <p>Data Sources</p> <p>Google, Cochrane, MEDLINE, and EMBASE and the <it>Chinese Biomedical </it>data bases for studies restricted to pentoxifylline treatment in humans with NAFLD in all languages until June 2010. Six studies (2 randomized, double-blind, placebo-controlled trials; 4 prospective cohort studies) extracted from 11604 references.</p> <p>Results</p> <p>Pentoxifylline-treated patients showed a significant decrease AST (n = 37, <it>P </it>= 0.01) and ALT (n = 50, <it>P </it>= 0.03), but no significant effect on IL-6 (n = 36, <it>P </it>= 0.33) and TNF-α (n = 68, <it>P </it>= 0.26) compared with Placebo or UDCA-controlled groups. Improvement in one or more histological variables was reported in two trails, only 1 study showed a reduction in of one or two points in fibrosis stage.</p> <p>Limitations</p> <p>The trails did not consistently report all of the outcomes of interest. Sample sizes (117 patients totally) were small and only 2 out of 6 studies had a randomized, controlled design.</p> <p>Conclusion</p> <p>Pentoxifylline reduce AST and ALT levels and may improve liver histological scores in patients with NALFD/NASH, but did not appear to affect cytokines. Large, prospective, and well-designed randomized, controlled studies are needed to address this issue. Novel therapeutic targets for activation of inflammatory signaling pathways by fat also merit investigation.</p

Quantum cryptanalysis on some Generalized Feistel Schemes

Post-quantum cryptography has attracted much attention from worldwide cryptologists. In ISIT 2010, Kuwakado and Morii gave a quantum distinguisher with polynomial time against 3-round Feistel networks. However, generalized Feistel schemes (GFS) have not been systematically investigated against quantum attacks. In this paper, we study the quantum distinguishers about some generalized Feistel schemes. For $d$-branch Type-1 GFS (CAST256-like Feistel structure), we introduce ($2d-1$)-round quantum distinguishers with polynomial time. For $2d$-branch Type-2 GFS (RC6/CLEFIA-like Feistel structure), we give ($2d+1$)-round quantum distinguishers with polynomial time. Classically, Moriai and Vaudenay proved that a 7-round $4$-branch Type-1 GFS and 5-round $4$-branch Type-2 GFS are secure pseudo-random permutations. Obviously, they are no longer secure in quantum setting. Using the above quantum distinguishers, we introduce generic quantum key-recovery attacks by applying the combination of Simon\u27s and Grover\u27s algorithms recently proposed by Leander and May. We denote $n$ as the bit length of a branch. For $(d^2-d+2)$-round Type-1 GFS with $d$ branches, the time complexity is $2^{(\frac{1}{2}d^2-\frac{3}{2}d+2)\cdot \frac{n}{2}}$, which is better than the quantum brute force search (Grover search) by a factor $2^{(\frac{1}{4}d^2+\frac{1}{4}d)n}$. For $4d$-round Type-2 GFS with $2d$ branches, the time complexity is $2^{{\frac{d^2 n}{2}}}$, which is better than the quantum brute force search by a factor $2^{{\frac{3d^2 n}{2}}}$