bluuebunny/crossref_metadata_2025_split huggingface dataset:
Robustness Verification of Semantic Segmentation Neural Networks Using Relaxed Reachability
Authors: Tran Hoang-Dung , Pal Neelanjana , Musau Patrick , Lopez Diego Manzanas , Hamilton Nathaniel , Yang Xiaodong , Bak Stanley , Johnson Taylor T. 
Published: July 2021

AbstractThis paper introduces robustness verification for semantic segmentation neural networks (in short, semantic segmentation networks [SSNs]), building on and extending recent approaches for robustness verification of image classification neural networks. Despite recent progress in developing verification methods for specifications such as local adversarial robustness in deep neural networks (DNNs) in terms of scalability, precision, and applicability to different network architectures, layers, and activation functions, robustness verification of semantic segmentation has not yet been considered. We address this limitation by developing and applying new robustness analysis methods for several segmentation neural network architectures, specifically by addressing reachability analysis of up-sampling layers, such as transposed convolution and dilated convolution. We consider several definitions of robustness for segmentation, such as the percentage of pixels in the output that can be proven robust under different adversarial perturbations, and a robust variant of intersection-over-union (IoU), the typical performance evaluation measure for segmentation tasks. Our approach is based on a new relaxed reachability method, allowing users to select the percentage of a number of linear programming problems (LPs) to solve when constructing the reachable set, through a relaxation factor percentage. The approach is implemented within NNV, then applied and evaluated on segmentation datasets, such as a multi-digit variant of MNIST known as M2NIST. Thorough experiments show that by using transposed convolution for up-sampling and average-pooling for down-sampling, combined with minimizing the number of ReLU layers in the SSNs, we can obtain SSNs with not only high accuracy (IoU), but also that are more robust to adversarial attacks and amenable to verification. Additionally, using our new relaxed reachability method, we can significantly reduce the verification time for neural networks whose ReLU layers dominate the total analysis time, even in classification tasks.
Computing Bottom SCCs Symbolically Using Transition Guided Reduction
Authors: Beneš Nikola , Brim Luboš , Pastva Samuel , Šafránek David 
Published: July 2021

AbstractDetection of bottom strongly connected components (BSCC) in state-transition graphs is an important problem with many applications, such as detecting recurrent states in Markov chains or attractors in dynamical systems. However, these graphs’ size is often entirely out of reach for algorithms using explicit state-space exploration, necessitating alternative approaches such as the symbolic one.Symbolic methods for BSCC detection often show impressive performance, but can sometimes take a long time to converge in large graphs. In this paper, we provide a symbolic state-space reduction method for labelled transition systems, calledinterleaved transition guided reduction(ITGR), which aims to alleviate current problems of BSCC detection by efficiently identifying large portions of the non-BSCC states.We evaluate the suggested heuristic on an extensive collection of 125 real-world biologically motivated systems. We show that ITGR can easily handle all these models while being either the only method to finish, or providing at least an order-of-magnitude speedup over existing state-of-the-art methods. We then use a set of synthetic benchmarks to demonstrate that the technique also consistently scales to graphs with more than$$2^{1000}$$21000vertices, which was not possible using previous methods.
Synthesis with Asymptotic Resource Bounds
Authors: Hu Qinheping , Cyphert John , D’Antoni Loris , Reps Thomas 
Published: July 2021

AbstractWe present a method for synthesizing recursive functions that satisfy both a functional specification and an asymptotic resource bound. Prior methods for synthesis with a resource metric require the user to specify aconcreteexpression exactly describing resource usage, whereas our method uses big-Onotation to specify theasymptoticresource usage. Our method can synthesize programs with complex resource bounds, such as a sort function that has complexity$$O(n\log (n))$$O(nlog(n)).Our synthesis procedure uses a type system that is able to assign an asymptotic complexity to terms, and can track recurrence relations of functions. These typing rules are justified by theorems used in analysis of algorithms, such as the Master Theorem and the Akra-Bazzi method. We implemented our method as an extension of prior type-based synthesis work. Our tool,SynPlexity, was able to synthesize complex divide-and-conquer programs that cannot be synthesized by prior solvers.
DNNV: A Framework for Deep Neural Network Verification
Authors: Shriver David , Elbaum Sebastian , Dwyer Matthew B. 
Published: July 2021

AbstractDespite the large number of sophisticated deep neural network (DNN) verification algorithms, DNN verifier developers, users, and researchers still face several challenges. First, verifier developers must contend with the rapidly changing DNN field to support new DNN operations and property types. Second, verifier users have the burden of selecting a verifier input format to specify their problem. Due to the many input formats, this decision can greatly restrict the verifiers that a user may run. Finally, researchers face difficulties in re-using benchmarks to evaluate and compare verifiers, due to the large number of input formats required to run different verifiers. Existing benchmarks are rarely in formats supported by verifiers other than the one for which the benchmark was introduced. In this work we present DNNV, a framework for reducing the burden on DNN verifier researchers, developers, and users. DNNV standardizes input and output formats, includes a simple yet expressive DSL for specifying DNN properties, and provides powerful simplification and reduction operations to facilitate the application, development, and comparison of DNN verifiers. We show how DNNV increases the support of verifiers for existing benchmarks from 30% to 74%.
BDD4BNN: A BDD-Based Quantitative Analysis Framework for Binarized Neural Networks
Authors: Zhang Yedi , Zhao Zhe , Chen Guangke , Song Fu , Chen Taolue 
Published: July 2021

AbstractVerifying and explaining the behavior of neural networks is becoming increasingly important, especially when they are deployed in safety-critical applications. In this paper, we study verification and interpretability problems for Binarized Neural Networks (BNNs), the 1-bit quantization of general real-numbered neural networks. Our approach is to encode BNNs into Binary Decision Diagrams (BDDs), which is done by exploiting the internal structure of the BNNs. In particular, we translate the input-output relation of blocks in BNNs to cardinality constraints which are in turn encoded by BDDs. Based on the encoding, we develop a quantitative framework for BNNs where precise and comprehensive analysis of BNNs can be performed. We demonstrate the application of our framework by providing quantitative robustness analysis and interpretability for BNNs. We implement a prototype tool and carry out extensive experiments, confirming the effectiveness and efficiency of our approach.
Brain-Inspired Algorithms for Processing of Visual Data
Authors: Strisciuglio Nicola , Petkov Nicolai 
Published: July 2021

AbstractThe study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compose it. These findings inspired researchers in image processing and computer vision to deploy such models to solve problems of visual data processing.In this paper, we review approaches for image processing and computer vision, the design of which is based on neuro-scientific findings about the functions of some neurons in the visual cortex. Furthermore, we analyze the connection between the hierarchical organization of the visual system of the brain and the structure of Convolutional Networks (ConvNets). We pay particular attention to the mechanisms of inhibition of the responses of some neurons, which provide the visual system with improved stability to changing input stimuli, and discuss their implementation in image processing operators and in ConvNets.
Einleitung
Authors: Kalisch Markus , Meier Lukas 
Published: July 2021

ZusammenfassungIn der Einführung wird anhand von Beispielen und den damit verbundenen Fragestellungen motiviert, wieso und wann man ein logistisches Regressionsmodell verwenden kann und wieso das lineare Regressionsmodell dazu nicht geeignet ist.
Das logistische Regressionsmodell
Authors: Kalisch Markus , Meier Lukas 
Published: July 2021

ZusammenfassungDas lineare Regressionsmodell wird zu Kapitelbeginn als zweistufiges Modell neu interpretiert. Darauf aufbauend wird das logistische Regressionsmodell äquivalent als Modell für binäre Zielgrößen eingeführt. Dabei wird gezeigt, dass die logistische Regression ein Spezialfall der verallgemeinerten linearen Modelle (GLM) ist. Als wichtige Komponenten solcher Modelle werden Verteilung, Erwartungswert, linearer Prädiktor und Linkfunktion besprochen. Als Alternative zu dieser Herangehensweise wird die logistische Regression als latentes Variablenmodell vorgestellt. Anschließend wird die Interpretation der Modellparameter auf der Skala der Log-Odds, Odds und Wahrscheinlichkeit ausführlich besprochen. Den Abschluss bildet ein kurzer Ausblick auf Modelle mit mehreren Variablen und Methoden der Parameterschätzung bzw. Inferenz.
Ausblick
Authors: Kalisch Markus , Meier Lukas 
Published: July 2021

ZusammenfassungIm Ausblick geht es um die Überprüfung der Modellannahmen und häufige Probleme der logistischen Regression. Zusätzlich wird darauf eingegangen, wie die logistische Regression auf mehr als zwei Klassen erweitert werden kann.
Supersymmetry, T-duality and heterotic α′-corrections
Authors: Lescano Eric , Núñez Carmen A. , Rodríguez Jesús A. 
Published: July 2021

Abstract
Higher-derivative interactions and transformation rules of the fields in the effective field theories of the massless string states are strongly constrained by space-time symmetries and dualities. Here we use an exact formulation of ten dimensional $$ \mathcal{N} $$
N
= 1 supergravity coupled to Yang-Mills with manifest T-duality symmetry to construct the first order α′-corrections of the heterotic string effective action. The theory contains a supersymmetric and T-duality covariant generalization of the Green-Schwarz mechanism that determines the modifications to the leading order supersymmetry transformation rules of the fields. We compute the resulting field-dependent deformations of the coefficients in the supersymmetry algebra and construct the invariant action, with up to and including four-derivative terms of all the massless bosonic and fermionic fields of the heterotic string spectrum.
Estimation of conditional distribution functions from data with additional errors applied to shape optimization
Authors: Hansmann Matthias , Horn Benjamin M. , Kohler Michael , Ulbrich Stefan 
Published: July 2021

AbstractWe study the problem of estimating conditional distribution functions from data containing additional errors. The only assumption on these errors is that a weighted sum of the absolute errors tends to zero with probability one for sample size tending to infinity. We prove sufficient conditions on the weights (e.g. fulfilled by kernel weights) of a local averaging estimate of the codf, based on data with errors, which ensure strong pointwise consistency. We show that two of the three sufficient conditions on the weights and a weaker version of the third one are also necessary for the spc. We also give sufficient conditions on the weights, which ensure a certain rate of convergence. As an application we estimate the codf of the number of cycles until failure based on data from experimental fatigue tests and use it as objective function in a shape optimization of a component.
Exploring the potential of cranial non-metric traits as a tool for personal identification: the never-ending dilemma
Authors: Palamenghi Andrea , Borlando Alessia , De Angelis Danilo , Sforza Chiarella , Cattaneo Cristina , Gibelli Daniele 
Published: July 2021

AbstractForensic anthropologists tasked with identification of skeletal remains often have to set up new strategies to overcome the limitations of conventional individualizing markers. A sound acquaintance with non-metric traits is essential for a reliable distinction between normal variations and pathological or traumatic conditions, yet the role of cranial variants in the identification process is still somehow ill-defined. One hundred crania (50 males and 50 females) of known sex and age were selected from the Collezione Antropologica LABANOF (a documented contemporary skeletal collection) and non-metric traits were scored as present or absent and by side. The frequencies of 13 traits were used to calculate the compound probabilities to find an individual with an exact combination of cranial features in the worldwide population. The probabilities of the majority of the individuals (53%) are within the 1 out of 10 million–1 out of 1 million interval. However, a fair number of subjects (25%) of the sample have the probabilities falling into the 1 out of 1 billion–1 out of 100 million interval, while the probabilities of a small portion of the sample (10%) are less than 1 out of 1 billion. This pilot study illustrates that some combinations of cranial variants are quite rare and may represent potential evidence to discern presumptive identifications, when an appropriate set of traits is selected and antemortem data are available for comparison. However, further research on larger and various samples is needed to confirm or discard the use of combinations of cranial non-metric traits as individualizing markers.
Risk of intracranial haemorrhage and ischaemic stroke after convexity subarachnoid haemorrhage in cerebral amyloid angiopathy: international individual patient data pooled analysis
Authors: Hostettler Isabel Charlotte , Wilson Duncan , Fiebelkorn Catherine Arnold , Aum Diane , Ameriso Sebastián Francisco , Eberbach Federico , Beitzke Markus , Kleinig Timothy , Phan Thanh , Marchina Sarah , Schneckenburger Romain , Carmona-Iragui Maria , Charidimou Andreas , Mourand Isabelle , Parreira Sara , Ambler Gareth , Jäger Hans Rolf , Singhal Shaloo , Ly John , Ma Henry , Touzé Emmanuel , Geraldes Ruth , Fonseca Ana Catarina , Melo Teresa , Labauge Pierre , Lefèvre Pierre-Henry , Viswanathan Anand , Greenberg Steven Mark , Fortea Juan , Apoil Marion , Boulanger Marion , Viader Fausto , Kumar Sandeep , Srikanth Velandai , Khurram Ashan , Fazekas Franz , Bruno Veronica , Zipfel Gregory Joseph , Refai Daniel , Rabinstein Alejandro , Graff-Radford Jonathan , Werring David John 
Published: July 2021

Abstract
Objective
To investigate the frequency, time-course and predictors of intracerebral haemorrhage (ICH), recurrent convexity subarachnoid haemorrhage (cSAH), and ischemic stroke after cSAH associated with cerebral amyloid angiopathy (CAA).

Methods
We performed a systematic review and international individual patient-data pooled analysis in patients with cSAH associated with probable or possible CAA diagnosed on baseline MRI using the modified Boston criteria. We used Cox proportional hazards models with a frailty term to account for between-cohort differences.

Results
We included 190 patients (mean age 74.5 years; 45.3% female) from 13 centers with 385 patient-years of follow-up (median 1.4 years). The risks of each outcome (per patient-year) were: ICH 13.2% (95% CI 9.9–17.4); recurrent cSAH 11.1% (95% CI 7.9–15.2); combined ICH, cSAH, or both 21.4% (95% CI 16.7–26.9), ischemic stroke 5.1% (95% CI 3.1–8) and death 8.3% (95% CI 5.6–11.8). In multivariable models, there is evidence that patients with probable CAA (compared to possible CAA) had a higher risk of ICH (HR 8.45, 95% CI 1.13–75.5, p = 0.02) and cSAH (HR 3.66, 95% CI 0.84–15.9, p = 0.08) but not ischemic stroke (HR 0.56, 95% CI 0.17–1.82, p = 0.33) or mortality (HR 0.54, 95% CI 0.16–1.78, p = 0.31).

Conclusions
Patients with cSAH associated with probable or possible CAA have high risk of future ICH and recurrent cSAH. Convexity SAH associated with probable (vs possible) CAA is associated with increased risk of ICH, and cSAH but not ischemic stroke. Our data provide precise risk estimates for key vascular events after cSAH associated with CAA which can inform management decisions.
Influence of Annealing Treatments on Welded CuNi Sheets on the Corrosion Behaviour in Sea Water
Authors: Biezma Maria Victoria , Linhardt Paul , Strobl Susanne , Haubner Roland 
Published: July 2021

AbstractDue to their good corrosion resistance in seawater, tubes, rods, and forgings made from copper-nickel alloys (Cu-Ni) are widely used in ship building, offshore platforms, desalination plants, and numerous other applications. Welding is a major technology for joining this material. Since the microstructures of base material, weld seam, and heat affected zone may be quite different, the consequences with respect to corrosion must be considered. In this study the influence of microstructure on corrosion behaviour was investigated using welded test coupons of CuNi10Fe1.6 Mn, as-welded and after heat-treatment, by metallographic examination and electrochemical corrosion tests in synthetic seawater.
Stable films of zinc-hexacyanoferrate: electrochemistry and ion insertion capabilities
Authors: Sciacca Rosalinda , Zamponi Silvia , Berrettoni Mario , Giorgetti Marco 
Published: July 2021

AbstractA stable film of zinc hexacyanoferrate is deposited on a GC (glassy carbon) substrate following a specific electrochemical protocol. The electrode maintains its characteristic even after dry and wet processes. SEM characterization confirms the cubic morphology of the materials and the IR suggests the presence of the FeII-C-N-ZnII structural unit. The electrochemical characterization indicates a very good stability of the film, thus opening application in ion exchange system. The focus is on monovalent, divalent, and trivalent ions. These results, the zinc low toxicity, and cost make zinc hexacyanoferrate films a promising candidate for many electrochemical applications.
Formal testing of timed graph transformation systems using metric temporal graph logic
Authors: Schneider Sven , Maximova Maria , Sakizloglou Lucas , Giese Holger 
Published: July 2021

AbstractEmbedded real-time systems generate state sequences where time elapses between state changes. Ensuring that such systems adhere to a provided specification of admissible or desired behavior is essential. Formal model-based testing is often a suitable cost-effective approach. We introduce an extended version of the formalism of symbolic graphs, which encompasses types as well as attributes, for representing states of dynamic systems. Relying on this extension of symbolic graphs, we present a novel formalism of timed graph transformation systems (TGTSs) that supports the model-based development of dynamic real-time systems at an abstract level where possible state changes and delays are specified by graph transformation rules. We then introduce an extended form of the metric temporal graph logic (MTGL) with increased expressiveness to improve the applicability of MTGL for the specification of timed graph sequences generated by a TGTS. Based on the metric temporal operators of MTGL and its built-in graph binding mechanics, we express properties on the structure and attributes of graphs as well as on the occurrence of graphs over time that are related by their inner structure. We provide formal support for checking whether a single generated timed graph sequence adheres to a provided MTGL specification. Relying on this logical foundation, we develop a testing framework for TGTSs that are specified using MTGL. Lastly, we apply this testing framework to a running example by using our prototypical implementation in the tool AutoGraph.
Experimental evaluation of kaolin stabilised with class F fly ash
Authors: Tamang Pratistha , Sriskantharajah Arathany , Ferreira Pedro , Lopez-Querol Susana 
Published: July 2021

AbstractThis study aims to investigate the effectiveness of fly ash (FA) in stabilising a kaolin soil through laboratory tests. Kaolin is an example of moderate plasticity clays that require stabilisation methods for construction purposes. The influence of FA on the improvement of kaolin is studied by varying its dosages in the mixtures (0%, 10% to 20%) as well as the cement content, used as an activator in different percentages (5 and 7%). The influence of the dry unit weight and the curing time of the soil mixture is also analysed through unconfined compressive strength and indirect tensile strength tests. The experimental results show that the strength increases linearly with both FA and cement contents. Moreover, higher initial dry unit weights also yield higher final strengths. To further assess the improvement, the application of the porosity over the volumetric cement content ratio, as the main variable, succeeded in attaining a relationship with the strength and the stiffness of the studied soil. Results for the combined effect of the porosity and the volumetric cement on the secant modulus were also determined. Furthermore, a unique relationship was obtained combining porosity, volumetric cement and FA content.
Raptors, racing pigeons and perceptions of attacks
Authors: Kettel Esther F. , Yarnell Richard W. , Quinn John L. , Gentle Louise K. 
Published: July 2021

AbstractRaptors are often the cause of human-wildlife conflict because they may predate economically valuable species, and it is the perceived extent of predation that may augment conflict between raptors and people who keep and race pigeons. This study uses data obtained through questionnaires and an online raptor-attack reporting feature to investigate the frequency of racing pigeon losses and the perceptions of pigeon fanciers. Responses suggest that those who kept more pigeons and entered more races lost a higher proportion of pigeons. Losses were also influenced by the predatory species: sparrowhawks (Accipter nisus) were more likely to attack pigeons at lofts, whilst peregrines (Falco peregrinus) were more likely to attack pigeons during training, with patterns linked to the raptors’ breeding seasons. Pigeon fanciers were almost unanimous in their perception that raptors threaten the future of the hobby. Previous studies show that only a small proportion of racing pigeons are likely to be lost to raptors, yet pigeon fanciers believe that raptors are the main cause of losses, highlighting a possible mismatch between the perceived and actual causes of loss. This misconception may be a main source of this human-raptor conflict, so educating pigeon fanciers about the true impact of raptors could help to alleviate the issue. A shift in the beginning of the pigeon racing season by one month, and careful sighting of pigeon lofts in gardens, are also recommended in order to reduce raptor attacks.