Abstract

Here we report the synthesis of an N-heterocyclic carbene (NHC)-stabilised phosphinidene oxide by the controlled oxygenation of a phosphinidene under ambient conditions. This compound can be further oxygenated to a phosphinidene dioxide. The stoichiometric reduction of a phosphinidene oxide with KC8 resembles the pinacol coupling reaction–the reduction of a carbonyl compound. We also looked at the stoichiometric oxidation of NHC-coordinated phosphinidene, phosphinidene oxide and phosphinidene dioxide with [NO][SbF6].

Abstract

Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency with our new Stransitive data set, and a new machine learning based method, the first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that predicts small relative protein stability changes for missense mutations that do not significantly alter the structure. It estimates a residue’s contributions toward protein stability (ΔG) in its local structural environment, and the difference between independently predicted contributions of the reference and mutant residues is reported as ΔΔG. We show that this self-consistent machine learning architecture is immune to many common biases in data sets, relies less on data than existing methods, is robust to overfitting, and can explain a substantial portion of the variance in experimental data.

Abstract

We identify a set of dynamical maps of open quantum system, and refer to them as 'epsilon-Markovian' maps. It is constituted of maps which, in a higher dimensional system–environment Hilbert space, possibly violate Born approximation but only a 'little'. We characterize the 'epsilon-nonmarkovianity' of a general dynamical map by the minimum distance of that map from the set of epsilon-Markovian maps. We analytically derive an inequality which gives a bound on the epsilon-nonmarkovianity of the dynamical map, in terms of an entanglement-like resource generated between the system and its 'immediate' environment. In the special case of a vanishing epsilon, this inequality gives a relation between the epsilon-nonmarkovianity of the reduced dynamical map on the system and the entanglement generated between the system and its immediate environment. We numerically investigate the behavior of the similar distant based measures of non-Markovianity for classes of amplitude damping and phase damping channels.

Abstract

Presburger arithmetic provides the mathematical core for the polyhedral compilation techniques that drive analytical cache models, loop optimization for ML and HPC, formal verification, and even hardware design. Polyhedral compilation is widely regarded as being slow due to the potentially high computational cost of the underlying Presburger libraries. Researchers typically use these libraries as powerful black-box tools, but the perceived internal complexity of these libraries, caused by the use of C as the implementation language and a focus on end-user-facing documentation, holds back broader performance-optimization efforts. With FPL, we introduce a new library for Presburger arithmetic built from the ground up in modern C++. We carefully document its internal algorithmic foundations, use

Abstract

As Deep Neural Network models for face processing tasks approach human-like performance, their deployment in critical applications such as law enforcement and access control has seen an upswing, where any failure may have far-reaching consequences. We need methods to build trust in deployed systems by making their working as transparent as possible. Existing visualization algorithms are designed for object recognition and do not give insightful results when applied‘ to the face domain. In this work, we present ‘Canonical Saliency Maps’, a new method which highlights relevant facial areas by projecting saliency maps onto a canonical face model. We present two kinds of Canonical Saliency Maps: image-level maps and model-level maps. Image-level maps highlight facial features responsible for the decision made by a deep face model on a given image, thus helping to understand how a DNN made a prediction on the image. Model-level maps provide an understanding of what the entire DNN model focuses on in each task, and thus can be used to detect biases in the model. Our qualitative and quantitative results show the usefulness of the proposed canonical saliency maps, which can be used on any deep face model regardless of the architecture.

Abstract

Recently, machine learning (ML) has proven to yield fast and accurate predictions of chemical properties to accelerate the discovery of novel molecules and materials. The majority of the work is on organic molecules, and much more work needs to be done for inorganic molecules, especially clusters. In the present work, we introduce a simple topological atomic descriptor called TAD, which encodes chemical environment information of each atom in the cluster. TAD is a simple and interpretable descriptor where each value represents the atom count in three shells. We also introduce the DART deep learning enabled topological interaction model, which uses TAD as a feature vector to predict energies of metal clusters, in our case gallium clusters with sizes ranging from 31 to 70 atoms. The DART model is designed based on the principle that the energy is a function of atomic interactions and allows us to model these complex atomic interactions to predict the energy. We further introduce a new dataset called GNC_31–70, which comprises structures and DFT optimized energies of gallium clusters with sizes ranging from 31 to 70 atoms. We show how DART can be used to accelerate the process of identification of low energy structures without geometry optimization. Albeit using a topological descriptor, DART achieves a mean absolute error (MAE) of 3.59 kcal mol−1 (0.15 eV) on the test set. We also show that our model can distinguish core and surface atoms in the Ga-70 cluster, which the model has never encountered earlier. Finally, we demonstrate the transferability of the DART model by predicting energies for about 6k unseen configurations picked up from molecular dynamics (MD) data for three cluster sizes (46, 57, and 60) within seconds. The DART model was able to reduce the load on DFT optimizations while identifying unique low energy structures from MD data.

Abstract

Guanidinium ion is a toxic cellular metabolite. The ykkC-III riboswitch, an mRNA stretch, regulates the gene expression by undergoing a conformational change in response to the binding of a free guanidinium ion and thereby plays a potentially important role in alleviating guanidinium toxicity in cells. An experimental crystal structure of the guanidinium-bound aptamer domain of the riboswitch from Thermobifida Fusca revealed the overall RNA architecture and mapped the specific noncovalent interactions that stabilize the ligand within the binding pocket aptamer. However, details of how the aptamer domain discriminates the cognate ligand from its closest structurally analogous physiological metabolites (arginine and urea), and how the binding of cognate ligand arrays information from the aptamer domain to the expression platform for regulating the gene expression, are not well understood. To fill this void, we perform a cumulative of 2 μs all-atom explicit-solvent molecular dynamics (MD) simulations on the full aptamer domain, augmented with quantum-chemical calculations on the ligand-binding pocket, to compare the structural and dynamical details of the guanidinium-bound state with the arginine or urea bound states, as well as the unbound (open) state. Analysis of the ligand-binding pocket reveals that due to unfavorable interactions with the binding-pocket residues, urea cannot bind the aptamer domain and thereby cannot alter the gene expression. Although interaction of the guanidyl moiety of arginine within the binding pocket is

Abstract

In this article, we propose an efficient Bloom filter and fuzzy extractor based user authentication technique, which is significant for an Internet of Things (IoT)-enabled multi-hop Wireless Sensor Networks (IoT-WSNs). The novelty of the proposed authentication technique is that it prevents fraudulent querying data delivery at the starting phase (i.e., at the querying sensor node itself) to stop false or counterfeit data flooding (which can exhaust the resources of IoT-WSNs) from the resource-constrained IoT-enabled sensor nodes to the nearby gateway node. The proposed authentication technique has the ability to significantly reduce the Bloom filter based false positive for user authentication in IoT-WSNs, while also preserving the constraint resources of WSNs. To estimate the security robustness of the proposed protocol based on the security goals of IoT-WSNs, we perform the formal security analysis using the random oracle and Real-Or-Random (ROR) models, informal security analysis and also the formal security verification using a widely-recognized automated software validation tool, known as Automated Validation of Internet Security Protocols and Applications (AVISPA). Next, communication and computational overheads analysis reveal that the proposed protocol is appropriate for resource-constrained sensor nodes. Furthermore, a correlative security feature and complexity analysis outcomes validate that the proposed protocol is reliable, secure, and efficient in comparison with other existing state of art related user authentication protocols.

Abstract

Electric vehicles have emerged as the future of transport worldwide with the rising need for greener and energy-efficient transportation solutions. This remarkable shift delegated a wide variety of protocols for reliable, scalable and secure communications in the electric vehicle transportation system. With this vision in mind, security researchers have proposed diverse authentication protocols vital for the electric vehicle charging system to protect the information exchanged between the various principals involved. Several existing authentication techniques, on the other hand, are either vulnerable to significant security threats or are limited to static charging. This article proposes a robust authentication mechanism for charging electric vehicles while driving, a scenario that is rapidly approaching in the future. The proposed protocol makes use of secure and lightweight primitives such as elliptic curves and hash functions. The proposed protocol's security is being analyzed to demonstrate that it is capable of withstanding relevant attacks. Additionally, the protocol achieves its objectives at a cost that is practically conceivable in terms of transmission and computing.

Abstract

Digitizing via scanning the physical artifact often forms the first primary step in preserving historical handwritten manuscripts. To maximally utilize scanner surface area and minimize manual labor, multiple manuscripts are usually scanned together into a scanned image. Therefore, the first crucial task in manuscript content understanding is to ensure that each of the individual manuscripts within a scanned image can be isolated (segmented) on a per-instance basis. Existing deep network based approaches for manuscript layout understanding implicitly assume a single or two manuscripts per image. Since this assumption may be routinely violated, there is a need for a precursor system which extracts individual