Adversarial Edit Attacks for Tree Data

Many machine learning models can be attacked with adversarial examples, i.e. inputs close to correctly classified examples that are classified incorrectly. However, most research on adversarial attacks to date is limited to vectorial data, in particular image data. In this contribution, we extend the field by introducing adversarial edit attacks for tree-structured data with potential applications in medicine and automated program analysis. Our approach solely relies on the tree edit distance and a logarithmic number of black-box queries to the attacked classifier without any need for gradient information. We evaluate our approach on two programming and two biomedical data sets and show that many established tree classifiers, like tree-kernel-SVMs and recursive neural networks, can be attacked effectively.Comment: accepted at the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL

Deep Randomized Neural Networks

Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers’ connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains

Deep Randomized Neural Networks

Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers' connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains

Locality in Theory Space

Locality is a guiding principle for constructing realistic quantum field theories. Compactified theories offer an interesting context in which to think about locality, since interactions can be nonlocal in the compact directions while still being local in the extended ones. In this paper, we study locality in "theory space", four-dimensional Lagrangians which are dimensional deconstructions of five-dimensional Yang-Mills. In explicit ultraviolet (UV) completions, one can understand the origin of theory space locality by the irrelevance of nonlocal operators. From an infrared (IR) point of view, though, theory space locality does not appear to be a special property, since the lowest-lying Kaluza-Klein (KK) modes are simply described by a gauged nonlinear sigma model, and locality imposes seemingly arbitrary constraints on the KK spectrum and interactions. We argue that these constraints are nevertheless important from an IR perspective, since they affect the four-dimensional cutoff of the theory where high energy scattering hits strong coupling. Intriguingly, we find that maximizing this cutoff scale implies five-dimensional locality. In this way, theory space locality is correlated with weak coupling in the IR, independent of UV considerations. We briefly comment on other scenarios where maximizing the cutoff scale yields interesting physics, including theory space descriptions of QCD and deconstructions of anti-de Sitter space.Comment: 40 pages, 11 figures; v2: references and clarifications added; v3: version accepted by JHE

Tools for Deconstructing Gauge Theories in AdS5

We employ analytical methods to study deconstruction of 5D gauge theories in the AdS5 background. We demonstrate that using the so-called q-Bessel functions allows a quantitative analysis of the deconstructed setup. Our study clarifies the relation of deconstruction with 5D warped theories.Comment: 30 pages; v2: several refinements, references adde

Climate variability during MIS 20–18 as recorded by alkenone-SST and calcareous plankton in the Ionian Basin (central Mediterranean)

This study shows the first Mediterranean high-resolution record of alkenone-derived sea surface temperature (SST) in the marine sediments outcropping at the Ideale section (IS) (southern Italy, central Mediterranean) from late marine isotope stage (MIS) 20 - through early MIS 18. The SST pattern evidences glacial-interglacial up to submillennial-scale temperature variation, with lower values (~13 °C) in late MIS 20 and substage 19b, and higher values (up to 21 °C) in MIS 19c and in the interstadials of MIS 19a. The SST data are combined with the new calcareous plankton analysis and the available, chronologically well-constrained carbon and oxygen isotope records in the IS. The multi-proxy approach, together with the location of the IS near the Italian coasts, the lower circalittoral-upper bathyal depositional setting, and high sedimentation rate allow to document long-and short-term paleoenvironmental modifications (sea level, rainfall, inorganic/organic/fresh water input to the basin), as a response to regional and global climate changes. The combined proxies reveal the occurrence of a terminal stadial event in late MIS 20 (here Med-HTIX), and warm-cold episodes (here Med-BATIX and Med-YDTIX) during Termination IX (TIX), which recall those that occurred through the last termination (TI). During these periods and the following ghost sapropel layer (insolation cycle 74, 784 ka) in the early MIS 19, high frequency internal changes are synchronously recorded by all proxies. The substage MIS 19c is warm but quite unstable, with several episodes of paleoenvironmental changes, associated with fluctuating tropical-subtropical water inflow through the Gibraltar Strait, variations of the cyclonic regime in the Ionian basin, and the southward shift of westerly winds and winter precipitation over southern Europe and Mediterranean basin. Three high-amplitude millennial-scale oscillations in the patterns of SST and calcareous plankton key taxa during MIS 19a are interpreted as linked to changes in temperature as well as in salinity due to periodical water column stratification and mixing. The main processes involved in the climate variability include changes in oceanographic exchanges through the Gibraltar Strait during modulations of Atlantic meridional overturning circulation and/or variations in atmospheric dynamics related to the influence of westerly and polar winds acting in the paleo-Ionian basin. A strong climate teleconnection between the North Atlantic and Mediterranean is discussed, and a prominent role of atmospheric processes in the central Mediterranean is evidenced by comparing data sets at the IS with Italian and extra-Mediterranean marine and terrestrial records

Conductance of the single-electron transistor: A comparison of experimental data with Monte Carlo calculations

We report on experimental results for the conductance of metallic single-electron transistors as a function of temperature, gate voltage and dimensionless conductance. In contrast to previous experiments our transistor layout allows for a direct measurement of the parallel conductance and no ad hoc assumptions on the symmetry of the transistors are necessary. Thus we can make a comparison between our data and theoretical predictions without any adjustable parameter. Even for rather weakly conducting transistors significant deviations from the perturbative results are noted. On the other hand, path integral Monte Carlo calculations show remarkable agreement with experiments for the whole range of temperatures and conductances.Comment: 8 pages, 7 figures, revtex4, corrected typos, submitted to PR

Inflation on the Brane with Vanishing Gravity

Many existing models of brane inflation suffer from a steep irreducible gravitational potential between the branes that causes inflation to end too early. Inspired by the fact that point masses in 2+1 D exert no gravitational force, we propose a novel unwarped and non-supersymmetric setup for inflation, consisting of 3-branes in two extra dimensions compactified on a sphere. The size of the sphere is stabilized by a combination of a bulk cosmological constant and a magnetic flux. Computing the 4D effective potential between probe branes in this background, we find a non-zero contribution only from exchange of level-1 KK modes of the graviton and radion. Identifying antipodal points on the 2-sphere projects out these modes, eliminating entirely the troublesome gravitational contribution to the inflationary potential.Comment: 19 pages, 11 figures, JHEP forma

Nonminimal Couplings in the Early Universe: Multifield Models of Inflation and the Latest Observations

Models of cosmic inflation suggest that our universe underwent an early phase of accelerated expansion, driven by the dynamics of one or more scalar fields. Inflationary models make specific, quantitative predictions for several observable quantities, including particular patterns of temperature anistropies in the cosmic microwave background radiation. Realistic models of high-energy physics include many scalar fields at high energies. Moreover, we may expect these fields to have nonminimal couplings to the spacetime curvature. Such couplings are quite generic, arising as renormalization counterterms when quantizing scalar fields in curved spacetime. In this chapter I review recent research on a general class of multifield inflationary models with nonminimal couplings. Models in this class exhibit a strong attractor behavior: across a wide range of couplings and initial conditions, the fields evolve along a single-field trajectory for most of inflation. Across large regions of phase space and parameter space, therefore, models in this general class yield robust predictions for observable quantities that fall squarely within the "sweet spot" of recent observations.Comment: 17pp, 2 figs. References added to match the published version. Published in {\it At the Frontier of Spacetime: Scalar-Tensor Theory, Bell's Inequality, Mach's Principle, Exotic Smoothness}, ed. T. Asselmeyer-Maluga (Springer, 2016), pp. 41-57, in honor of Carl Brans's 80th birthda

Distinguishing Binders from False Positives by Free Energy Calculations: Fragment Screening Against the Flap Site of HIV Protease

Molecular docking is a powerful tool used in drug discovery and structural biology for predicting the structures of ligand–receptor complexes. However, the accuracy of docking calculations can be limited by factors such as the neglect of protein reorganization in the scoring function; as a result, ligand screening can produce a high rate of false positive hits. Although absolute binding free energy methods still have difficulty in accurately rank-ordering binders, we believe that they can be fruitfully employed to distinguish binders from nonbinders and reduce the false positive rate. Here we study a set of ligands that dock favorably to a newly discovered, potentially allosteric site on the flap of HIV-1 protease. Fragment binding to this site stabilizes a closed form of protease, which could be exploited for the design of allosteric inhibitors. Twenty-three top-ranked protein–ligand complexes from AutoDock were subject to the free energy screening using two methods, the recently developed binding energy analysis method (BEDAM) and the standard double decoupling method (DDM). Free energy calculations correctly identified most of the false positives (≥83%) and recovered all the confirmed binders. The results show a gap averaging ≥3.7 kcal/mol, separating the binders and the false positives. We present a formula that decomposes the binding free energy into contributions from the receptor conformational macrostates, which provides insights into the roles of different binding modes. Our binding free energy component analysis further suggests that improving the treatment for the desolvation penalty associated with the unfulfilled polar groups could reduce the rate of false positive hits in docking. The current study demonstrates that the combination of docking with free energy methods can be very useful for more accurate ligand screening against valuable drug targets