Naor-Yung paradigm with shared randomness and applications

The Naor-Yung paradigm (Naor and Yung, STOC’90) allows to generically boost security under chosen-plaintext attacks (CPA) to security against chosen-ciphertext attacks (CCA) for public-key encryption (PKE) schemes. The main idea is to encrypt the plaintext twice (under independent public keys), and to append a non-interactive zero-knowledge (NIZK) proof that the two ciphertexts indeed encrypt the same message. Later work by Camenisch, Chandran, and Shoup (Eurocrypt’09) and Naor and Segev (Crypto’09 and SIAM J. Comput.’12) established that the very same techniques can also be used in the settings of key-dependent message (KDM) and key-leakage attacks (respectively). In this paper we study the conditions under which the two ciphertexts in the Naor-Yung construction can share the same random coins. We find that this is possible, provided that the underlying PKE scheme meets an additional simple property. The motivation for re-using the same random coins is that this allows to design much more efficient NIZK proofs. We showcase such an improvement in the random oracle model, under standard complexity assumptions including Decisional Diffie-Hellman, Quadratic Residuosity, and Subset Sum. The length of the resulting ciphertexts is reduced by 50%, yielding truly efficient PKE schemes achieving CCA security under KDM and key-leakage attacks. As an additional contribution, we design the first PKE scheme whose CPA security under KDM attacks can be directly reduced to (low-density instances of) the Subset Sum assumption. The scheme supports keydependent messages computed via any affine function of the secret ke

Architecture and performance of the KM3NeT front-end firmware

The KM3NeT infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Estimating the assessment difficulty of CVSS environmental metrics : an experiment

[Context] The CVSS framework provides several dimensions to score vulnerabilities. The environmental metrics allow security analysts to downgrade or upgrade vulnerability scores based on a company’s computing environments and security requirements. [Question] How difficult is for a human assessor to change the CVSS environmental score due to changes in security requirements (let alone technical configurations) for PCI-DSS compliance for networks and systems vulnerabilities of different type? [Results] A controlled experiment with 29 MSc students shows that given a segmented network it is significantly more difficult to apply the CVSS scoring guidelines on security requirements with respect to a flat network layout, both before and after the network has been changed to meet the PCI-DSS security requirements. The network configuration also impact the correctness of vulnerabilities assessment at system level but not at application level. [Contribution] This paper is the first attempt to empirically investigate the guidelines for the CVSS environmental metrics. We discuss theoretical and practical key aspects needed to move forward vulnerability assessments for large scale systems

A gene expression signature of Retinoblastoma loss-of-function predicts resistance to neoadjuvant chemotherapy in ER-positive/HER2-positive breast cancer patients

HER2-positive (HER2+) breast cancers show heterogeneous response to chemotherapy, with the ER-positive (ER+) subgroup deriving less benefit. Loss of retinoblastoma tumor suppressor gene (RB1) function has been suggested as a cardinal feature of breast cancers that are more sensitive to chemotherapy and conversely resistant to CDK4/6 inhibitors. We performed a retrospective analysis exploring RBsig, a gene signature of RB loss, as a potential predictive marker of response to neoadjuvant chemotherapy in ER+/HER2+ breast cancer patients.We selected clinical trials of neoadjuvant chemotherapy +/- anti-HER2 therapy in HER2+ breast cancer patients with available information on gene expression data, hormone receptor status, and pathological complete response (pCR) rates. RBsig expression was computed in silico and correlated with pCR.Ten studies fulfilled the inclusion criteria and were included in the analysis (514 patients). Overall, of 211 ER+/HER2+ breast cancer patients, 49 achieved pCR (23%). The pCR rate following chemotherapy +/- anti-HER2 drugs in patients with RBsig low expression was significantly lower compared to patients with RBsig high expression (16% vs. 30%, respectively; Fisher's exact test p = 0.015). The area under the ROC curve (AUC) was 0.62 (p = 0.005). In the 303 ER-negative (ER-)/HER2+ patients treated with chemotherapy +/- anti-HER2 drugs, the pCR rate was 43%. No correlation was found between RBsig expression and pCR rate in this group.Low expression of RBsig identifies a subset of ER+/HER2+ patients with low pCR rates following neoadjuvant chemotherapy +/- anti-HER2 therapy. These patients may potentially be spared chemotherapy in favor of anti-HER2, endocrine therapy, and CDK 4/6 inhibitor combinations

Italian pediatric nutrition survey

Introduction the prevalence of malnutrition in children and its impact on clinical outcomes is underrecognized by clinicians in Italy as well as worldwide. A novel definition of pediatric malnutrition has been recently proposed by a working group of the Academy of Nutrition and Dietetics and American Society for Parenteral and Enteral Nutrition (A.S.P.E.N.), based on the correlation between illness and the use of zscores of anthropometric measurements. Aim to investigate the prevalence of malnutrition and related nutritional support among hospitalized children in Italy, in a nationwide survey performed in a single day (16/4/2015). Methods an open access website (http://nday.biomedia.net) was used to collected data from 73 hospitals and 101 wards in 14 Italian regions (1994 patients). Anonymous information was collected on hospitals' characteristics, patient's anthropometry, admission diagnosis, presence of chronic diseases and use of nutritional support: oral nutritional supplements (ONS), enteral nutrition (EN) or parenteral nutrition (PN). Z-scores of anthropometric measurements, calculated with Epi Info 7.1.5, defined nutritional status: wasting was identified by BMI or Weight-for-Length z-score (<â\u88\u921 mild, <â\u88\u922 moderate, <â\u88\u923 severe), stunting by Height-for-Age Z-score <â\u88\u922. WHO 2006 and CDC 2000 growth charts were used respectively for children younger and older than 2 years old. Results 1790 complete records were obtained for hospitalized patients aged 0â\u80\u9320 years, with median age 6.16 (0.1â\u80\u9320 years and 53.3% males). 52.9% were aged 0â\u80\u936 years and 58.8% of children suffered from chronic diseases. Wasting was detected in 28.7% of the total sample with higher occurrence observed in age ranges 0â\u80\u936 and 14â\u80\u9320 years, while 17.3% of patients showed stunting; surprisingly almost 27% of them were aged 0â\u80\u932. A ranking of the admission diagnosis with the highest rate of malnutrition was complied. The prevalence of wasting was significantly (p < 0.005) higher amongst children with chronic diseases (34.1% vs. 27.1%); stunting prevalence tripled in patients with chronic disease (24.5% vs. 8.3%). Only 23.5% of malnourished children (17%, 25.6% and 36.7%, respectively mild, moderate and severe malnutrition) received nutritional support: 11.7% received oral nutrition supplements (ONS, modular or complete), 11.5% enteral nutrition (EN, 6.4% via nasogastric tube, 5.1% via gastrostomy) and 6.8 % received parenteral nutrition (PN); in some patients a combination of two. Nutritional support is more commonly used among stunting patients, 39.5% of children under treatment. Conclusion Malnutrition of any grade was observed in nearly 1/3 and stunting in 17% of the reported hospitalized children, and it is likely to be underrecognized as the nutritional support reached only a small part of the malnourished children