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By Cartan’s version of Nevanlinna’s theory, we prove the following result: let m and n be two positive integers satisfying $$n\ge 2+m,$$ n ≥ 2 + m , let $$p\not \equiv 0$$ p ≢ 0 be a polynomial, let $$\eta \ne 0$$ η ≠ 0 be a finite complex number, let $$\omega _{1}, \omega _{2}, \ldots , \omega _{m}$$ ω 1 , ω 2 , … , ω m be m distinct finite nonzero complex numbers, and let $$H_{j}$$ H j be either exponential polynomials of degree less than q, or an ordinary polynomial in z for $$0\le j\le m$$ 0 ≤ j ≤ m , such that $$H_{j}\not \equiv 0$$ H j ≢ 0 for $$1\le j\le m.$$ 1 ≤ j ≤ m . Suppose that $$f\not \equiv \infty $$ f ≢ ∞ is a meromorphic solution of the difference equation: $$\begin{aligned} f^n(z)+p(z)f(z+\eta )&=H_0(z)+H_1(z)e^{\omega _{1}z^{q}}+H_2(z)e^{\omega _{2}z^{q}}\\&\quad +\cdots +H_m(z)e^{\omega _{m}z^{q}}, \end{aligned}$$ f n ( z ) + p ( z ) f ( z + η ) = H 0 ( z ) + H 1 ( z ) e ω 1 z q + H 2 ( z ) e ω 2 z q + ⋯ + H m ( z ) e ω m z q , such that the hyper-order of f satisfies $$\rho _2(f) ρ 2 ( f ) < 1 . Then, f reduces to a transcendental entire function, such that either $$n=m+2$$ n = m + 2 with $$H_0\not \equiv 0$$ H 0 ≢ 0 and $$\lambda (f)=\rho (f)=q,$$ λ ( f ) = ρ ( f ) = q , or $$m=2,$$ m = 2 , $$H_0=0$$ H 0 = 0 and: $$\begin{aligned} f(z)=\frac{H_1(z-\eta )e^{\omega _{1}(z-\eta )^{q}}}{p(z-\eta )} \end{aligned}$$ f ( z ) = H 1 ( z - η ) e ω 1 ( z - η ) q p ( z - η ) with $$\begin{aligned} H^n_1(z)=p^n(z)H_2(z+\eta )e^{\omega _2P_{q-1}(z)}\quad \text {and}\quad P_{q-1}(z)=\sum \limits _{k=1}^q\left( {\begin{array}{c}q\\ k\end{array}}\right) \eta ^kz^{q-k}. \end{aligned}$$ H 1 n ( z ) = p n ( z ) H 2 ( z + η ) e ω 2 P q - 1 ( z ) and P q - 1 ( z ) = ∑ k = 1 q q k η k z q - k . This result improves Theorems 1.1 and 1.3 from [19] by removing some assumptions of theirs. An example is provided to show that some results obtained in this paper, in a sense, are the best possible.

As the world’s leading software development platform, GitHub has become a social networking site for programmers and recruiters who leverage its social features, such as star and fork, for career and business development. However, in this paper, we found a group of GitHub accounts that conducted promotion services in GitHub, called “promoters”, by performing paid star and fork operations on specified repositories. We also uncovered a stealthy way of tampering with historical commits, through which these promoters are able to fake commits retroactively. By exploiting such a promotion service, any GitHub user can pretend to be a skillful developer with high influence. To understand promotion services in GitHub, we first investigated the underground promotion market of GitHub and identified 1,023 suspected promotion accounts from the market. Then, we developed an SVM (Support Vector Machine) classifier to detect promotion accounts from all active users extracted from GH Archive ranging from 2015 to 2019. In total, we detected 63,872 suspected promotion accounts. We further analyzed these suspected promotion accounts, showing that (1) a hidden functionality in GitHub is abused to boost the reputation of an account by forging historical commits and (2) a group of small businesses exploit GitHub promotion services to promote their products. We estimated that suspicious promoters could have made a profit of $3.41 million and $4.37 million in 2018 and 2019, respectively.

Fake base station (FBS) has been exploited by criminals to attack mobile users by spamming fraudulent messages for over a decade. Despite that prior work has proposed several techniques to mitigate this issue, FBS spam is still a long-standing challenging issue in some countries, such as China, and causes billions of dollars of financial loss every year. Therefore, understanding and exploring the thematic strategies in the FBS spam ecosystem at a large scale would improve the defense mechanisms. In this paper, we present the first large-scale characterization of FBS spam ecosystem by collecting three-month real-world FBS detection results. First, at "macro-level'', we uncover the characteristics of FBS spammers, including their business categories, temporal patterns and spatial patterns. Second, at "micro-level'', we investigate how FBS ecosystem is organized and how fraudulent messages are constructed by campaigns to trap users and evade detection. Collectively, the results expand our understanding of the FBS spam ecosystem and provide new insights into improved mitigation mechanisms for the security community.

HTTPS is principally designed for secure end-to-end communication, which adds confidentiality and integrity to sensitive data transmission. While several man-in-the-middle attacks (e.g., SSL Stripping) are available to break the secured connections, state-of-the-art security policies (e.g., HSTS) have significantly increased the cost of successful attacks. However, the TLS certificates shared by multiple domains make HTTPS hijacking attacks possible again. In this paper, we term the HTTPS MITM attacks based on the shared TLS certificates as HTTPS Context Confusion Attack (SCC Attack). Despite a known threat, it has not yet been studied thoroughly. We aim to fill this gap with an in-depth empirical assessment of SCC Attack. We find the attack can succeed even for servers that have deployed current best practice of security policies. By rerouting encrypted traffic to another flawed server that shares the TLS certificate, attackers can bypass the security practices, hijack the ongoing HTTPS connections, and subsequently launch additional attacks including phishing and payment hijacking. Particularly, vulnerable HTTP headers from a third-party server are exploitable for this attack, and it is possible to hijack an already-established secure connection. Through tests on popular websites, we find vulnerable subdomains under 126 apex domains in Alexa top 500 sites, including large vendors like Alibaba, JD, and Microsoft. Meanwhile, through a large-scale measurement, we find that TLS certificate sharing is prominent, which uncovers the high potential of such attacks, and we summarize the security dependencies among different parties. For responsible disclosure, we have reported the issues to affected vendors and received positive feedback. Our study sheds light on an influential attack surface of the HTTPS ecosystem and calls for proper mitigation against MITM attacks.

Covalent organic frameworks (COFs) are emerging novel catalyst carrier. In this work, Pd/COF-LZU1 was one-pot prepared by p-phenylenediamine (PDA), triformylbenzene (TFB), and palladium acetate (Pd(OAc)2) under the catalysis of acetic acid (HAc). The effects of HAc concentration on the Pd/COF-LZU1 particle size, Pd loading, and catalytic activity in the reduction of 4-nitrophenol (4-NP) were investigated. It was found that, with the increased HAc concentration, the Pd/COF-LZU1 particle size reduced, and Pd loading and catalytic activity displayed maximum for 3 mol L−1 HAc. The catalytic reduction of 4-NP can be described by the pseudo-first-order kinetic model, and the rate constant is 20 times higher than that of palladium acetate. Pd/COF-LZU1 also showed excellent stability and reusability, demonstrating great potential in catalytic reduction reactions.

Lipophilic marine algal toxins (LMATs) are highly toxic secondary metabolites produced by marine microalgae that pose a great threat to marine aquaculture organisms and human health. In this study, a novel and automated method for the simultaneous determination of six groups of LMATs in seawater was developed by on-line solid phase extraction (SPE) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS). Condition optimization and method validation were performed, and the recoveries of all 14 target LMATs featuring different properties ranged from 83.5% to 96.0%. The limits of detection of most target compounds were within ≤3.000 ng/L with good precision (relative standard deviation ≤ 12.1%) and linearity (R2≥0.9916). Compared with off-line SPE methods, the proposed on-line SPE method has better recovery, sensitivity, repeatability, and throughput; in addition, the volume of seawater sample necessary to conduct determinations is greatly reduced in the present method. Finally, the method was applied to determine LMATs in actual seawater samples collected from the Bohai and South Yellow Seas of China in summer, and okadaic acid and pectenotoxin-2 were detected in all seawater samples. The highest concentration of ∑LMATs (22.23 ng/L) occurred in the coastal mariculture area of Shandong Province. Therefore, routine monitoring of LMATs in seawater of the coastal mariculture zone is necessary to prevent shellfish contamination especially in summer, and the proposed on-line SPE-LC-MS/MS method is a powerful way for direct and automatic detection of various LMATs in coastal mariculture area.

Background and PurposeAlthough tumor size and nodal status are the most important prognostic factors, it is believed that nodal status outperforms tumor size as a prognostic factor. In particular, when patients have a nodal stage greater than N2 (more than nine positive lymph nodes), it is well accepted that tumor size does not retain its prognostic value. Even in the newest American Joint Committee on Cancer (AJCC) prognostic staging system, which includes molecular subtype as an important prognostic factor, T1-3N2 patients are categorized as the same population. The same is true for T1-4N3 patients. Moreover, some physicians have speculated that for tumors staged N2 or greater, the smaller the tumor is, the more aggressive the tumor. Thus, this study aims to investigate the prognostic value of tumor stage (T stage) in patients with extensive nodal involvement and to compare the survival of T4N × M0 and T × N3M0.Patients and MethodsFemale breast cancer patients with nine or more positive lymph nodes or with T4 tumors were identified in the SEER registry between 2010 and 2015. The effect of T stage on breast cancer-specific survival (BCSS) was assessed using the Kaplan–Meier survival curve method and risk-adjusted Cox proportional hazard regression modeling. Survival comparison of T4NxM0 and TxN3M0 patients was also achieved using the Kaplan–Meier survival curve method and risk-adjusted Cox proportional hazard regression model.ResultsOverall, 21,696 women with N2-3 tumors were included from 284,073 patients.T stage, nodal stage (N stage), ER, PR, HER2 and grade were all independent prognostic factors (p ConclusionsIn patients with extensive nodal status, tumor stage remains a prognostic factor independent of other factors, such as ER, PR, HER2, and grade. In patients with T4Nx or TxN3 tumors, T4 tumors exhibit worse outcomes than N3 tumors independent of other prognostic factors. The AJCC staging system should be modified based on these findings.

Content Delivery Networks (CDNs) aim to improve network performance and protect against web attack traffic for their hosting websites. And the HTTP range request mechanism is majorly designed to reduce unnecessary network transmission. However, we find the specifications failed to consider the security risks introduced when CDNs meet range requests. In this study, we present a novel class of HTTP amplification attack, Range-based Amplification (RangeAmp) Attacks. It allows attackers to massively exhaust not only the outgoing bandwidth of the origin servers deployed behind CDNs but also the bandwidth of CDN surrogate nodes. We examined the RangeAmp attacks on 13 popular CDNs to evaluate the feasibility and real-world impacts. Our experiment results show that all these CDNs are affected by the RangeAmp attacks. We also disclosed all security issues to affected CDN vendors and already received positive feedback from 12 vendors.

The Tibetan Plateau (TP) is a region with high altitudes and complicated terrain conditions. Due to the special conditions of this region, it is also regarded as the third pole of the Earth. The land cover and vegetation in this region have not been extensively studied, so this study investigated the possibility of using a combined classifier that was established based on D-S evidence theory to extract the land cover of the TP. Multiple feature images were obtained based on a single classification rule, and the feature images were normalized to obtain the basic probability assignment (BPA). The BPA was used as the evidence source to represent the belief level of each type of land cover. The information for the different belief levels was combined based on the D-S evidence theory. The maximum belief level of the combination results was used to identify the land cover types on the TP. The results of this study indicate that based on the D-S evidence theory, multiple classifiers can effectively be combined to improve the classification results. This study has also revealed that more classifiers fused together to make a combined classifier did not result in the combined classifier's accuracy being higher than those of the original classifiers. Higher accuracies were only obtained when more high accuracy evidence theory was used in the classifier combination, in which case, the combined classifier's classification accuracy was also high.

The quality of GAN-generated fake images has improved significantly, and recent GAN approaches, such as StyleGAN, achieve near indistinguishability from real images for the naked eye. As a result, adversaries are attracted to using GAN-generated fake images for disinformation campaigns and fraud on social networks. However, training an image generation network to produce realistic-looking samples remains a time-consuming and difficult problem, so adversaries are more likely to use published GAN models to generate fake images. In this paper, we analyze the frequency domain to attribute and detect fake images generated by a known GAN model. We derive a similarity metric on the frequency domain and develop a new approach for GAN image attribution. We conduct experiments on four trained GAN models and two real image datasets. Our results show high attribution accuracy against real images and those from other GAN models. We further analyze our method under evasion attempts and find the frequency-based approach is comparatively robust. In this paper, we analyze the frequency domain to attribute and detect fake images generated by a known GAN model. We derive a similarity metric on the frequency domain and develop a new approach for GAN image attribution. We conduct experiments on four trained GAN models and two real image datasets. Our results show high attribution accuracy against real images and those from other GAN models. We further analyze our method under evasion attempts and find the frequency-based approach is comparatively robust.

We previously showed that NUDT21-spanning copy-number variations (CNVs) are associated with intellectual disability (Gennarino et al., 2015). However, the patients’ CNVs also included other genes. To determine if reduced NUDT21 function alone can cause disease, we generated Nudt21+/- mice to mimic NUDT21-deletion patients. We found that although these mice have 50% reduced Nudt21 mRNA, they only have 30% less of its cognate protein, CFIm25. Despite this partial protein-level compensation, the Nudt21+/- mice have learning deficits, cortical hyperexcitability, and misregulated alternative polyadenylation (APA) in their hippocampi. Further, to determine the mediators driving neural dysfunction in humans, we partially inhibited NUDT21 in human stem cell-derived neurons to reduce CFIm25 by 30%. This induced APA and protein level misregulation in hundreds of genes, a number of which cause intellectual disability when mutated. Altogether, these results show that disruption of NUDT21-regulated APA events in the brain can cause intellectual disability.

Knowledge bases (KBs), which store high-quality information, are crucial for many applications, such as enhancing search results and serving as external sources for data cleaning. Not surprisingly, there exist outdated facts in most KBs due to the rapid change of information. Naturally, it is important to keep KBs up-to-date. Traditional wisdom has investigated the problem of using reference data (such as new facts extracted from the news) to detect outdated facts in KBs. However, existing approaches can only cover a small percentage of facts in KBs. In this paper, we propose a novel human-in-the-loop approach for outdated fact detection in KBs. It trains a binary classifier using features such as historical update frequency and existence time of a fact to compute the likelihood of a fact in a KB to be outdated. Then, it interacts with humans to verify whether a fact with high likelihood is indeed outdated. In addition, it also uses logical rules to detect more outdated facts based on human feedback. The outdated facts detected by the logical rules will also be fed back to train the ML model further for data augmentation. Extensive experiments on real-world KBs, such as Yago and DBpedia, show the effectiveness of our solution.

A data summarization for the large table can be of great help, which provides a concise and informative overview and assists the user to quickly figure out the subject of the data. However, a high quality summarization needs to have two desirable properties: presenting notable entities and achieving broad domain coverage. In this demonstration, we propose a summarizer system called PocketView that is able to create a data summarization through a pocket view of the table. The attendees will experience the following features of our system:(1) time-sensitive notability evaluation - PocketView can automatically identify notable entities according to their significance and popularity in user-defined time period; (2) broad-coverage pocket view - Our system will provide a pocket view for the table without losing any domain, which is much simpler and clearer for attendees to figure out the subject compared with the original table.

Hydropower accounts for almost half of all electricity production in Sweden, while also being the most important energy balancing resource. Nuclear power plants are gradually being decommissioned up to 2045 in Sweden according to the government’s plans. It means there is a need for a dramatic expansion of renewable energy production, especially for hydropower as a balancing resource. The availability of renewable energy fluctuates with the weather, seasons, and between years, which is an important factor for the coordination of renewable energy production.The aim of this research is to investigate how runoff forecasts affect hydropower production planning when the account is taken to hydro-climatic variations. This problem is studied by using production planning models fed by runoff forecasts that exhibit climate-driven periodicity. Statistical ensemble predictions and receding horizon control are implemented to reveal the effect on the production. Further, we utilize half-a-century long daily hydro-climatological data to runoff forecasts that are aggregated in ensembles particularly reflecting the bi-annual climate periodicity apparent from spectral analysis of the data.Dalälven River Basin is used as a study case, which is a watershed stretching from western Sweden to the Baltic in the east and which has more than 30 hydropower stations. Four forecast ensemble scenarios have been analyzed out by studying the periodicity of discharge data in Dalälven River Basin. According to the seasonal and two-year periodicity, the four scenario ensembles are defined as: a) Odd year, wet month; b) Even year, dry month; c) Odd year, dry month; d) Even year, wet month. [A1] A large-scale optimal hydropower production model was built in MATLAB, in order to simulate hydropower production in Dalälven River Basin. It includes 13 reservoirs and 36 hydropower plants, and applies the sequential-quadratic-programming (SQP ) as the optimization method. Receding horizon control is embedded into the optimal production programming, which can correct the systematic error. The historical data shows that the one-year-long sampling has a consistent biannual periodicity, with wet and dry years of different strength depending on the start month of the data selection in the year. It indicates the discharge data selection start from November and December gives the most dramatic effect on periodicity, while starting from May and July have a lower impact. Dynamic programming of hydropower production shows for both dry and wet runoff conditions that matching the forecast ensemble with the right phase of the actual climate conditions has a significant effect on the production profit.

The viability of a renewable electricity system depends on long-term climate variations, uneven spatiotemporal distribution of renewable energy, and technical constraints. A major problem is to achieve a sustainable balance of water usage and consumption, as well as adequate energy and water distribution and storage capacities. In particular, hydropower offers a large capacity for energy storage and production flexibility, but only stands for a minor part of the total energy potential. In this study we explored the spatial and temporal variance of hydropower availability for a 35-year period based on historical hydro-meteorological data from large parts of Europe. A spectral analysis of these historical time-series shows that spatiotemporal coordination of the hydropower system covered in the Global Reservoir and Dam Database (GRanD) can potentially contribute with a “virtual” energy storage capacity that is up to four times the actual energy storage capacity contained in the existing hydropower reservoirs. Such virtual energy storage capacity implies reduced water storage demand, hence, indirectly contributes to reduced constraints of the food-water-energy nexus also in a wider system perspective. We found that the most significant benefits from a spatiotemporal management arise at distances of 1,200 – 3,000 km, i.e. on the continental scale, which can have implications for a future renewable energy system at large. The analysis also covers what we denote “energy-domain-specific drought”, which implies a shortage of energy storage capacity to avoid a deficit of energy for a given time period, and which may be reduced by the spatiotemporal coordination of power production.

Postzygotic mutations are acquired in all of the normal tissues throughout an individual’s lifetime and hold clues for identifying mutagenesis causing factors. The process and underlying mechanism of postzygotic mutations in normal tissues is still poorly understood. In this study, we investigated postzygotic mutation spectra in healthy individuals by optimized ultra-deep exome sequencing of time series samples from the same volunteer and samples from different individuals. In cells of blood, sperm, and muscle, we resolved three common types of mutational signature. Two of them are known to represent clock-like mutational processes, and their proportions in mutation profiles associated with polymorphisms of epigenetic regulation genes, suggesting the contribution of personal genetic backgrounds to underlying biological process. Notably, the third signature, characterized by C>T transitions at GpCpN sites, tends to be a feature of diverse normal tissues. Mutations of this type were likely to occur early in embryo development even before the tissue differentiation, as indicated by their relatively high allele frequencies, sharing variants between multiple tissues, and lacking of age-related accumulation. Almost all tumors shown in public datasets did not have this signature detected except for 19.6% of clear cell renal cell carcinoma samples, which featured by activation of the hypoxia-induced signaling pathway. Moreover, in vitro activation of HIF signaling pathway successfully introduced the corresponding mutation profile of this signature in a culture-expanded human embryonic stem cell line. Therefore, embryonic hypoxia may explain this novel signature across multiple normal tissues. Our study suggest hypoxic conditions in the early stage of embryo development may be a crucial factor for the C>T transitions at GpCpN sites and individual genetic background also related to shaping human postzygotic mutation profiles. Author Summary The process and related mechanism of post-zygotic mutations in normal tissues is still poorly understood. By analyzing post-zygotic mutations in blood, sperm and muscle from healthy individuals, we found a normal tissues specific mutation type characterized by C>T transitions at GpCpN sites. Almost none of tumors in The Cancer Genome Atlas project harbors this type of mutations, except for a subset of clear cell renal cell carcinoma samples with higher activity of hypoxia inducible signaling pathways. We further reproduce the enrichment of this type of mutations in human embryonic stem cells by specific activating hypoxia inducible factor 1α. Taken together, we propose that hypoxic conditions are one crucial factor responsible for the occurrence of post-zygotic mutations, especially the C>T transition in GpCpN sites, in the early stage of embryo development in healthy individuals.

Mutations in genes encoding synaptic proteins cause many neurodevelopmental disorders, with the majority affecting postsynaptic apparatuses and much fewer in presynaptic proteins. Syntaxin-binding protein 1 (STXBP1, also known as MUNC18-1) is an essential component of the presynaptic neurotransmitter release machinery. De novo heterozygous pathogenic variants in STXBP1 are among the most frequent causes of neurodevelopmental disorders including intellectual disabilities and epilepsies. These disorders, collectively referred to as STXBP1 encephalopathy, encompass a broad spectrum of neurologic and psychiatric features, but the pathogenesis remains elusive. Here we modeled STXBP1 encephalopathy in mice and found that Stxbp1 haploinsufficiency caused cognitive, psychiatric, and motor dysfunctions, as well as cortical hyperexcitability and seizures. Furthermore, Stxbp1 haploinsufficiency reduced cortical inhibitory neurotransmission via distinct mechanisms from parvalbumin-expressing and somatostatin-expressing interneurons. These results demonstrate that Stxbp1 haploinsufficient mice recapitulate cardinal features of STXBP1 encephalopathy and indicate that GABAergic synaptic dysfunction is likely a crucial contributor to disease pathogenesis.

An efficient and practical synthetic strategy, suitable for one-pot synthesis, for novel amide-linked oligosaccharide mimetics from sugar amino acid precursors is described. Twenty substrates, including twelve amide-linked disaccharide mimetics, four amide-linked trisaccharide mimetics and four amide-linked tetrasaccharide mimetics, were synthesized based on this strategy and its optimal conditions. This potentially general method is featured by high yields, gram-scale synthesis, wide substrate scope, and mild reaction conditions.

The effects of different storage temperatures (2, 4 and 8 ℃) and their corresponding optimal heat treatment conditions on the quality, physiological and biochemical indexes of Cucumber Fruits during storage were studied by using the quadratic regression orthogonal rotation combination design. The effects of different storage temperatures (2, 4 and 8 ℃) and their corresponding optimal heat treatment conditions on the chilling injury, hardness, weightlessness rate, polyphenol oxidase (PPO), catalase (CAT), peroxidase (POD), H2O2, super oxygen anion free radical (O2-), ASA and GSH were determined. The results showed that heat treatment could inhibit chilling injury, while heat treatment combined with 4 ℃ low temperature storage could effectively inhibit the decline of fruit hardness and weight loss rate, delay the increase of peroxidase (POD) and polyphenol oxidase (PPO) activities, inhibit the increase of H2O2 and superoxide anion free radical O2- and significantly inhibit the browning of cucumber, delay the decline of ascorbic acid and maintain the content of GSH, it was beneficial to adjust the balance of active oxygen system. The results showed that under the storage condition of 4 ℃, the hot water treatment condition of cucumber was 39.4 ℃ and 24.3 min, which could delay the senescence of cucumber fruit and better maintain the quality of cucumber fruit.

Caffeic acid(CA) was grafted on PVDF by UV pre-activation/thermal initiated grafting method with PVDF solution as substrates for the first time. The PVDF-g-CA was employed for fabrication of adsorptive membranes for cesium, and the static and dynamic adsorption, along with elution and membrane reusability, were studied. The results show that, under the same concentrations and grafting time, the average grafting degree of UV pre-activation/thermal initiated grafting method is much higher than that of the two-step UV grafting and the one-step UV grafting. The PVDF-g-CA membranes show high adsorption capacity for Cs+(0.23 mmol/g, 25 °C), and the selectivity factor of Cs+ vs. Li+, Na+ and K+ is 8.46, 3.68 and 3.23. This is the first report about the selective adsorption of Cs+ by CA-based materials. The adsorption isotherm can be well described by the Langmuir model. The membrane can be reused for 4 times without apparent performance deterioration, indicating great potential in adsorption of cesium from aqueous solutions.

The popularity of cloud hosting services also brings in new security chal- lenges: it has been reported that these services are increasingly utilized by miscreants for their malicious online activities. Mitigating this emerging threat, posed by such “bad repositories” (simply Bar), is challenging due to the different hosting strategy to traditional hosting service, the lack of direct observations of the repositories by those outside the cloud, the reluctance of the cloud provider to scan its customers’ repositories without their consent, and the unique evasion strategies employed by the adversary. In this paper, we took the first step toward understanding and detecting this emerging threat. Using a small set of “seeds” (i.e., confirmed Bars), we identified a set of collective features from the websites they serve (e.g., attempts to hide Bars), which uniquely characterize the Bars. These features were utilized to build a scanner that detected over 600 Bars on leading cloud platforms like Amazon, Google, and 150 K sites, including popular ones like , using them. Highlights of our study include the pivotal roles played by these repositories on malicious infrastructures and other important discoveries include how the adversary exploited legitimate cloud repositories and why the adversary uses Bars in the first place that has never been reported. These findings bring such malicious services to the spotlight and contribute to a better understanding and ultimately eliminating this new threat.

Covalent organic framework (COF) membranes are attractive candidates for molecular separation. Herein, COF-LZU1 composite membranes were prepared by a stoichiometric spraying layer-by-layer self-assembly method for the first time. The membranes displayed a high rejection (>90%) and permeance (>400 L m-2 h-1 MPa-1, 50-880% higher than traditional membranes) for dyes (Mw 350-800).

The popularity of online gambling could bring negative social impact, and many countries ban or restrict online gambling. Taking China for example, online gambling violates Chinese laws and hence is illegal. However, illegal online gambling websites are still thriving despite strict restrictions, since they are able to make tremendous illicit profits by trapping and cheating online players. In this paper, we conduct the first deep analysis on illegal online gambling targeting Chinese to unveil its profit chain. After successfully identifying more than 967,954 suspicious illegal gambling websites, we inspect these illegal gambling websites from five aspects, including webpage structure similarity, SEO (Search Engine Optimization) methods, the abuse of Internet infrastructure, third-party online payment, and gambling group. Then we conduct a measurement study on the profit chain of illegal online gambling, investigating the upstream and downstream of these illegal gambling websites. We mainly focus on promotion strategies, third-party online payment, the abuse of third-party live chat services, and network infrastructures. Our findings shed the light on the ecosystem of online gambling and help the security community thwart illegal online gambling.

Mg-Al-Fe layered double hydroxides (LDHs) were exfoliated and incorporated in polyether sulfone membranes for the removal of phosphate and fluoride for the first time. The exfoliation methods, coagulation bath, LDH amount, interfering ions, adsorption isotherm, desorption and reuse of the membranes were investigated. It was found that LDHs could be quickly exfoliated in formamide/N,N-dimethylformamide (DMF) solvent mixtures with sodium carboxymethyl cellulose as a stabilizer. The membranes displayed much higher adsorption capacity for phosphate (5.61mg/g) and faster adsorption rate than the un-exfoliated materials. With increased DMF content in the coagulation bath, the static and dynamic adsorption capacity rose. Interference from Cl- and SO42- (50mg/L) on adsorption of phosphates was not apparent. The membranes displayed excellent reusability in dynamic adsorption/desorption. The membranes also showed high adsorption capacity for fluorides (1.61mg/g).

Given a relational table, we study the problem of detecting and repairing erroneous data, as well as marking correct data, using well curated knowledge bases (KBs). We propose detective rules (DRs), a new type of data cleaning rules that can make actionable decisions on relational data, by building connections between a relation and a KB. The main invention is that a DR simultaneously models two opposite semantics of an attribute belonging to a relation using types and relationships in a KB: The positive semantics explains how its value should be linked to other attribute values in a correct tuple, and the negative semantics indicate how a wrong attribute value is connected to other correct attribute values within the same tuple. Naturally, a DR can mark correct values in a tuple if it matches the positive semantics. Meanwhile, a DR can detect/repair an error if it matches the negative semantics. We study fundamental problems associated with DRs, e.g., rule consistency and rule implication. We present efficient algorithms to apply DRs to clean a relation, based on rule order selection and inverted indexes. Moreover, we discuss approaches on how to generate DRs from examples. Extensive experiments, using both real-world and synthetic datasets, verify the effectiveness and efficiency of applying DRs in practice.

Our previous study has reported that maternal separation (MS) results in abnormal behaviors such as depressive-like and anxious-like behaviors in adult rats. However, the underlying mechanism remains unclear. In this study, we focused on the relationship between MS and autophagy in an effort to explain potential mechanisms. Pups experienced MS from postnatal days 7 to 21 and were randomly divided into the control (CON) group and MS group. Data showed that MS induced depressive-like and anxious-like behaviors in adult rats. Compared with the CON group, the glutamate level was obviously increased in the hippocampus (HP) and prefrontal cortex (PFC) of rats in the MS group. Interestingly, both beclin-1 expression and ratio of LC3II/LC3I were decreased in the HP, but they were increased in the PFC. Furthermore, N-methyl-d-aspartate receptor subunit 2B (NR2B) expression was significantly increased in the HP and PFC. Additionally, postsynaptic density-95 was increased in the HP; however, it was unchanged in the PFC. Moreover, the expression of synaptophysin was obviously decreased in both the HP and PFC. In conclusion, these findings suggest that MS induces different autophagic responses in the HP and PFC (i.e., the inhibition of autophagy in the HP and the activation of autophagy in the PFC), which can be related to the NR2B signaling pathway.

The evolution of mobile malware poses a serious threat to smartphone security. Today, sophisticated attackers can adapt by maximally sabotaging machine-learning classifiers via polluting training data, rendering most recent machine learning-based malware detection tools (such as D rebin , D roid APIM iner , and M a M a D roid ) ineffective. In this paper, we explore the feasibility of constructing crafted malware samples; examine how machine-learning classifiers can be misled under three different threat models; then conclude that injecting carefully crafted data into training data can significantly reduce detection accuracy. To tackle the problem, we propose K uafu D et , a two-phase learning enhancing approach that learns mobile malware by adversarial detection. K uafu D et includes an offline training phase that selects and extracts features from the training set, and an online detection phase that utilizes the classifier trained by the first phase. To further address the adversarial environment, these two phases are intertwined through a self-adaptive learning scheme, wherein an automated camouflage detector is introduced to filter the suspicious false negatives and feed them back into the training phase. We finally show that K uafu D et can significantly reduce false negatives and boost the detection accuracy by at least 15%. Experiments on more than 250,000 mobile applications demonstrate that K uafu D et is scalable and can be highly effective as a standalone system.

Membrane fouling exists in the preparation of nanoparticles with a membrane reactor. The effects of membrane molecular weight cut-off (MWCO), transmembrane pressure and reactants concentration on the membrane fouling, as well as fouling mechanism and membrane cleaning, were investigated with the preparation of BaSO4 nanoparticles as a typical example. It was found that, the fouling degree at the entrance side of membrane module was lower than that at the exit side due to the different transmembrane pressure, permeation fluxes and boundary layer. At the same reaction degree, the reduction factor of permeation fluxes increased with the increasing membrane MWCO, transmembrane pressures, and reactants concentration. Two opposite processes, i.e. deposition and growth of nanoparticles on the membrane surface and the sweeping effects by the liquid, determined the membrane fouling. Ultrasonic cleaning displayed some effects on the recovery of permeation fluxes.

High-spatial-resolution images play an important role in land cover classification, and object-based image analysis (OBIA) presents a good method of processing high-spatial-resolution images. Segmentation, as the most important premise of OBIA, significantly affects the image classification and target recognition results. However, scale selection for image segmentation is difficult and complicated for OBIA. The main challenge in image segmentation is the selection of the optimal segmentation parameters and an algorithm that can effectively extract the image information. This paper presents an approach that can effectively select an optimal segmentation scale based on land object average areas. First, 20 different segmentation scales were used for image segmentation. Next, the classification and regression tree model (CART) was used for image classification based on 20 different segmentation results, where four types of features were calculated and used, including image spectral bands value, texture value, vegetation indices, and spatial feature indices, respectively. WorldView-3 images were used as the experimental data to verify the validity of the proposed method for the selection of the optimal segmentation scale parameter. In order to decide the effect of the segmentation scale on the object area level, the average areas of different land objects were estimated based on the classification results. Experiments based on the multi-scale segmentation scale testify to the validity of the land object’s average area-based method for the selection of optimal segmentation scale parameters. The study results indicated that segmentation scales are strongly correlated with an object’s average area, and thus, the optimal segmentation scale of every land object can be obtained. In this regard, we conclude that the area-based segmentation scale selection method is suitable to determine optimal segmentation parameters for different land objects. We hope the segmentation scale selection method used in this study can be further extended and used for different image segmentation algorithms.

In this study, scalable and facile electroless plating accompanied with phosphorization process is explored for preparation of interconnected network of Ni2P–Co2P on carbon clothes (CC) substrate. The binder-free Ni2P–Co2P/CC electrocatalyst with optimized Ni2P:Co2P ratio displays outstanding HER activity in both alkaline and acidic conditions requiring only 93 mV and 172 mV vs. RHE of overpotential to reach 10 mA cm−2 of current density. The superior HER activity of Ni2P–Co2P/CC could be attributed to (i) the abundant interfacial contact and larger electrochemical surface area; (ii) regulated electronic structure and hydrophilic surface of the catalyst, and (iii) interfacial charge redistribution contributing more high-valence Ni active sites, as visualized by off-axis electron holography. Overall, this work directly demonstrates the interfacial charge redistribution in Ni2P–Co2P/CC interconnected network, and suggests a facile strategy for structural design and preparation of highly efficient phosphide electrocatalysts.

Covalent organic frameworks (COFs) exhibit potentials in adsorption, separation, heterogeneous catalysis, and energy storage due to low density, high thermal stability and inherent porosity. In this work, p-phenylenediamine (PDA) and 2,4,6-trihydroxy-benzene-1,3,5-tricarbaldehyde (TFP) were simultaneously evaporated onto modified poly(1,1-difluoroethylene) (PVDF) membrane in a chemical vapor deposition reactor to prepare COF-TpPa1 membranes for the separation of dyes. The effects of evaporation temperature, evaporation time, amount of Fe powder and pores size of PVDF membranes were studied, and the long-term operation was evaluated. The membranes showed an excellent water permeance (up to 400 L m−2 h−1 MPa−1) with high rejection (>90%) for different dyes (Mw 450–800), and low rejection (

Emerging vehicular trajectory data have opened up opportunities to benefit many real-world applications, e.g., frequent trajectory based navigation systems, road planning, car pooling, etc. The similarity join is a key operation to enable such applications, which finds similar trajectory pairs from two large collections of trajectories. Existing similarity metrics on trajectories rely on aligning sampling points of two trajectories. However, due to different sampling rates or different vehicular speeds, the sample points in similar trajectories may not be aligned. To address this problem, we propose a new bi-directional mapping similarity ( $\mathtt{BDS}$ ), which allows a sample point of a trajectory to align to the closest location (which may not be a sample point) on the other trajectory, and vice versa. Since it is expensive to enumerate every two trajectories and compute their similarity, we propose Strain-Join , a signature-based trajectory similarity join framework. Strain-Join first generates signatures for each trajectory such that if two trajectories do not share common signatures, they cannot be similar. In order to utilize this property to prune dissimilar pairs, we devise several techniques to generate high-quality signatures and propose an efficient filtering algorithm to prune dissimilar pairs. For the pairs not pruned by the filtering algorithm, we propose effective verification algorithms to verify whether they are similar. Experimental results on real datasets show that our algorithm outperforms state-of-the-art techniques in terms of both effectiveness and efficiency.

Integrity constraint based data repairing is an iterative process consisting of two parts: detect and group errors that violate given integrity constraints (ICs); and modify values inside each group such that the modified database satisfies those ICs. However, most existing automatic solutions treat the process of detecting and grouping errors straightforwardly (e.g., violations of functional dependencies using string equality), while putting more attention on heuristics of modifying values within each group. In this paper, we propose a revised semantics of violations and data consistency w.r.t. a set of ICs. The revised semantics relies on string similarities, in contrast to traditional methods that use syntactic error detection using string equality. Along with the revised semantics, we also propose a new cost model to quantify the cost of data repair by considering distances between strings. We show that the revised semantics provides a significant change for better detecting and grouping errors, which in turn improves both precision and recall of the following data repairing step. We prove that finding minimum-cost repairs in the new model is NP-hard, even for a single FD. We devise efficient algorithms to find approximate repairs. In addition, we develop indices and optimization techniques to improve the efficiency. Experiments show that our approach significantly outperforms existing automatic repair algorithms in both precision and recall.

A series of imidazole ionic liquid (IL) functionalized porous organic polymers (POPs) with high surface areas has been successfully prepared based on p-dichloroxylene (p-DCX) and N-methylimidazole (N-MI) as fundamental building blocks through the FeCl3-catalyzed Friedel–Crafts alkylation reaction. The IL-functionalized POPs show CO2 uptake up to 73.6 cm3 g−1 (273 K, 1 bar). Their isosteric heat of adsorption can be increased from 7.84 to 17.35 kJ mol−1 by adjusting the molar ratios of N-MI to p-DCX. These materials could also be used as reusable acid catalysts in the syntheses of benzimidazole derivatives from aromatic aldehydes and o-phenylene diamine in good yields (87–91%).

An efficient and practical approach for the synthesis of substituted benzannulated seven-membered