Selective encryption in the CCSDS standard for lossless and near-lossless multispectral and hyperspectral image compression

In this paper, we investigate low-complexity encryption solutions to be embedded in the recently proposed CCSDS standard for lossless and near-lossless multispectral and hyperspectral image compression. The proposed approach is based on the randomization of selected components in the image compression pipeline, namely the sign of prediction residual and the fixed part of Rice-Golomb codes, inspired by similar solutions adopted in video coding. Thanks to the adaptive nature of the CCSDS algorithm, even simple randomization of the sign of prediction residuals can provide a sufficient scrambling of the decoded image when the encryption key is not available. Results on the standard CCSDS test set show that the proposed technique uses on average only about 20% of the keystream compared to a conventional stream cipher, with a negligible increase of the rate of the encoder

Gaussian class-conditional simplex loss for accurate, adversarially robust deep classifier training

In this work, we present the Gaussian Class-Conditional Simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that improves over the state-of-the-art in terms of classification accuracy and adversarial robustness, with little extra cost for network training. The proposed method learns a mapping of the input classes onto Gaussian target distributions in a latent space such that a hyperplane can be used as the optimal decision surface. Instead of maximizing the likelihood of target labels for individual samples, our loss function pushes the network to produce feature distributions yielding high inter-class separation and low intra-class separation. The mean values of the learned distributions are centered on the vertices of a simplex such that each class is at the same distance from every other class. We show that the regularization of the latent space based on our approach yields excellent classification accuracy. Moreover, GCCS provides improved robustness against adversarial perturbations, outperforming models trained with conventional adversarial training (AT). In particular, our model learns a decision space that minimizes the presence of short paths toward neighboring decision regions. We provide a comprehensive empirical evaluation that shows how GCCS outperforms state-of-the-art approaches over challenging datasets for targeted and untargeted gradient-based, as well as gradient-free adversarial attacks, both in terms of classification accuracy and adversarial robustness

Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification

Deep learning has shown outstanding performance in several applications including image classification. However, deep classifiers are known to be highly vulnerable to adversarial attacks, in that a minor perturbation of the input can easily lead to an error. Providing robustness to adversarial attacks is a very challenging task especially in problems involving a large number of classes, as it typically comes at the expense of an accuracy decrease. In this work, we propose the Gaussian class-conditional simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that provides adversarial robustness while at the same time achieving or even surpassing the classification accuracy of state-of-the-art methods. Differently from other frameworks, the proposed method learns a mapping of the input classes onto target distributions in a latent space such that the classes are linearly separable. Instead of maximizing the likelihood of target labels for individual samples, our objective function pushes the network to produce feature distributions yielding high inter-class separation. The mean values of the distributions are centered on the vertices of a simplex such that each class is at the same distance from every other class. We show that the regularization of the latent space based on our approach yields excellent classification accuracy and inherently provides robustness to multiple adversarial attacks, both targeted and untargeted, outperforming state-of-the-art approaches over challenging datasets

Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification

Deep learning has shown outstanding performance in several applications including image classification. However, deep classifiers are known to be highly vulnerable to adversarial attacks, in that a minor perturbation of the input can easily lead to an error. Providing robustness to adversarial attacks is a very challenging task especially in problems involving a large number of classes, as it typically comes at the expense of an accuracy decrease. In this work, we propose the Gaussian class-conditional simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that provides adversarial robustness while at the same time achieving or even surpassing the classification accuracy of state-of-the-art methods. Differently from other frameworks, the proposed method learns a mapping of the input classes onto target distributions in a latent space such that the classes are linearly separable. Instead of maximizing the likelihood of target labels for individual samples, our objective function pushes the network to produce feature distributions yielding high inter-class separation. The mean values of the distributions are centered on the vertices of a simplex such that each class is at the same distance from every other class. We show that the regularization of the latent space based on our approach yields excellent classification accuracy and inherently provides robustness to multiple adversarial attacks, both targeted and untargeted, outperforming state-of-the-art approaches over challenging datasets

Longitudinal and Transverse Wakefields Simulations and Studies in Dielectric-Coated Circular Waveguides

In recent years, there has been a growing interest and rapid experimental progress on the use of e.m. fields produced by electron beams passing through dielectric-lined structures and on the effects they might have on the drive and witness bunches. Short ultra-relativistic electron bunches can excite very intense wakefields, which provide an efficient acceleration through the dielectric wakefield accelerators (DWA) scheme with higher gradient than that in the conventional RF LINAC. These beams can also generate high power narrow band THz coherent Cherenkov radiation. These high gradient fields may create strong instabilities on the beam itself causing issues in plasma acceleration experiments (PWFA), plasma lensing experiments and in recent beam diagnostic applications. In this work we report the results of the simulations and studies of the wakefields generated by electron beams at different lengths and charges passing on and off axis in dielectric-coated circular waveguides. We also propose a semi-analytical method to calculate these high gradient fields without resorting to time consuming simulations

Reciprocal regulation of the bile acid-activated receptor FXR and the interferon-γ-STAT-1 pathway in macrophages

AbstractNuclear receptors are a family of ligand regulated factors that exert homeostatic functions at the interface between metabolic and immune function. The farnesoid X receptor (FXR) is a bile acid sensor expressed in immune cells such as macrophages where it exerts counter-regulatory effects. FXR deficient mice demonstrate disregulated immune response. Expression of FXR is down-regulated in inflamed tissues but the mechanism that leads to FXR down-regulation by inflammatory mediators is unknown. In the present study we have investigated the effect of inflammation-related cytokines on macrophages and demonstrated that INFγ is a potent inhibitor of FXR gene expression/function in macrophages. STAT1 silencing and over-expression experiments demonstrated that FXR repression is mediated by INFγ dependent activation of STAT1. Since IFNγ is a potent activator of STAT1 we searched for STAT1 binding sites in the human FXR genomic and identified a region of the human FXR gene between the second and third exon that contains three hypothetical STAT1 binding sites. RAW 264.7 transiently transfected with an FXR genomic reporter construct which contained the three STAT binding sites responded to IFNγ with a robust decrease in the reporter activity, demonstrating the potent modulation of FXR transcription by IFNγ. Chromatin immunoprecipitation assay revealed that this region was immunoprecipitated following treatment of macrophage cell lines and supershift assay demonstrated that STAT1 was able to bind one of three identified sites. In summary, these results suggest that IFNγ induced STAT1 homodimers modulate the transcriptional repression of FXR gene in macrophages during inflammation-related cytokines

A wireless method to obtain the impedance from scattering parameters

The coaxial wire method is a common and appreciated choice to assess the beam coupling impedance (BCI) of an accelerator element. Nevertheless, the results obtained from wire measurements could be inaccurate due to the presence of the stretched conductive. The aim of this work is to establish a solid technique to obtain the BCI from electromagnetic simulations, without modifications of the device under test. In this framework, we identified a new relation to get the resistive wall beam coupling impedance of a circular chamber directly from the scattering parameters. Furthermore, a possible generalization of the method to arbitrary cross section geometries has been studied and validated with numerical simulations

Epithelial thymic tumours in paediatric age: a report from the TREP project

<p>Abstract</p> <p>Background</p> <p>Thymic epithelial tumours (thymoma and carcinoma) are exceptionally rare in children. We describe a national multicentre series with a view to illustrating their clinical behaviour and the results of treatment.</p> <p>Methods</p> <p>From January 2000 all patients under 18 years of age diagnosed with "<it>rare paediatric tumours</it>" were centrally registered by the Italian centres participating in the TREP project (<b>T</b>umori <b>R</b>ari in <b>E</b>tà <b>P</b>ediatrica [Rare Tumours in Paediatric Age]). The clinical data of children with a thymic epithelial tumour registered as at December 2009 were analyzed for the purposes of the present study.</p> <p>Results</p> <p>Our series comprised 4 patients with thymoma and 5 with carcinoma (4 males, 5 females; median age 12.4 years). The tumour masses were mainly large, exceeding 5 cm in largest diameter. Based on the Masaoka staging system, 3 patients were stage I, 1 was stage III, 1 was stage IVa and 4 were stage IVb.</p> <p>All 3 patients with stage I thymoma underwent complete tumour resection at diagnosis and were alive 22, 35 and 93 months after surgery. One patient with a thymoma metastasizing to the kidneys died rapidly due to respiratory failure.</p> <p>Thymic carcinomas were much more aggressive, infiltrating nearby organs (in 4 cases) and regional nodes (in 5), and spreading to the bone (in 3) and liver (in 1). All patients received multidrug chemotherapy (platinum derivatives + etoposide or other drugs) with evidence of tumour reduction in 3 cases. Two patients underwent partial tumour resection (after chemo-radiotherapy in one case) and 4 patients were given radiotherapy (45-54 Gy). All patients died of their disease.</p> <p>Conclusions</p> <p>Children with thymomas completely resected at diagnosis have an excellent prognosis while thymic carcinomas behave aggressively and carry a poor prognosis despite multimodal treatment.</p

Hydrogen sulphide induces μ opioid receptor-dependent analgesia in a rodent model of visceral pain

<p>Abstract</p> <p>Background</p> <p>Hydrogen sulphide (H₂S) is a gaseous neuro-mediator that exerts analgesic effects in rodent models of visceral pain by activating K_ATPchannels. A body of evidence support the notion that K_ATPchannels interact with endogenous opioids. Whether H₂S-induced analgesia involves opioid receptors is unknown.</p> <p>Methods</p> <p>The perception of painful sensation induced by colorectal distension (CRD) in conscious rats was measured by assessing the abdominal withdrawal reflex. The contribution of opioid receptors to H₂S-induced analgesia was investigated by administering rats with selective μ, κ and δ opioid receptor antagonists and antisenses. To investigate whether H₂S causes μ opioid receptor (MOR) transactivation, the neuronal like cells SKNMCs were challenged with H₂S in the presence of MOR agonist (DAMGO) or antagonist (CTAP). MOR activation and phosphorylation, its association to β arrestin and internalization were measured.</p> <p>Results</p> <p>H₂S exerted a potent analgesic effects on CRD-induced pain. H₂S-induced analgesia required the activation of the opioid system. By pharmacological and molecular analyses, a robust inhibition of H₂S-induced analgesia was observed in response to central administration of CTAP and MOR antisense, while κ and δ receptors were less involved. H₂S caused MOR transactivation and internalization in SKNMCs by a mechanism that required AKT phosphorylation. MOR transactivation was inhibited by LY294002, a PI3K inhibitor, and glibenclamide, a K_ATPchannels blocker.</p> <p>Conclusions</p> <p>This study provides pharmacological and molecular evidence that antinociception exerted by H₂S in a rodent model of visceral pain is modulated by the transactivation of MOR. This observation provides support for development of new pharmacological approaches to visceral pain.</p

Design of high gradient, high repetition rate damped C-band rf structures

The gamma beam system of the European Extreme Light Infrastructure–Nuclear Physics project foresees the use of a multibunch train colliding with a high intensity recirculated laser pulse. The linac energy booster is composed of 12 traveling wave C-band structures, 1.8 m long with a field phase advance per cell of 2π=3 and a repetition rate of 100 Hz. Because of the multibunch operation, the structures have been designed with a dipole higher order mode (HOM) damping system to avoid beam breakup (BBU). They are quasiconstant gradient structures with symmetric input couplers and a very effective damping of the HOMs in each cell based on silicon carbide (SiC) rf absorbers coupled to each cell through waveguides. An optimization of the electromagnetic and mechanical design has been done to simplify the fabrication and to reduce the cost of the structures. In the paper, after a review of the beam dynamics issues related to the BBU effects, we discuss the electromagnetic and thermomechanic design criteria of the structures. We also illustrate the criteria to compensate the beam loading and the rf measurements that show the effectiveness of the HOM damping