Abstract

Pattern mining is an important task of data mining and involves the extraction of interesting associations from large transactional databases. Typically, a given transactional database D gets updated due to the addition and deletion of transactions. Consequently, some of the previously discovered patterns may become invalid, while some new patterns may emerge. This has motivated significant research efforts in the area of incremental mining. The goal of incremental mining is to efficiently mine patterns when D gets updated with additions and/or deletions of transactions as opposed to mining all of the patterns from scratch. Incidentally, active research efforts are being made to develop incremental pattern mining algorithms for extracting frequent patterns, sequential patterns and utility patterns. Another important type of pattern is the coverage pattern (CP), which has significant applications in areas such as banner advertising, search engine advertising and visibility mining. However, none of the existing works address the issue of incremental mining for extracting CPs. In this regard, the main contributions of this work are twofold. First, we introduce the problem of incremental mining of CPs. Second, we propose an approach, designated as Comprehensive Coverage Pattern Mining, for efficiently extracting CPs under the incremental paradigm. We have also performed extensive experiments using two real click-stream datasets and one synthetic dataset to demonstrate the overall effectiveness of our proposed approach.

Abstract

Large gains in the rate of cache-aided broadcast communication are obtained using coded caching, but to obtain this most existing centralized coded caching schemes require that the files at the server be divisible into a large number of parts (this number is called subpacketization). In fact, most schemes require the subpacketization to be growing asymptotically as exponential in √[\leftroot -1\uproot 1r]K for some positive integer r and K being the number of users. On the other extreme, few schemes having subpacketization linear in K are known; however, they require large number of users to exist, or they offer only little gain in the rate. In this work, we propose two new centralized coded caching schemes with low subpacketization and moderate rate gains utilizing projective geometries over finite fields. Both the schemes achieve the same asymptotic subpacketization, which is exponential in O((logK) 2 ) (thus improving on the √[\leftroot -1\uproot 1r]K exponent). The first scheme has a larger cache requirement but has at most a constant rate (with increasing K), while the second has small cache requirement but has a larger rate. As a special case of our second scheme, we get a new linear subpacketization scheme, which has a more flexible range of parameters than the existing linear subpacketization schemes. Extending our techniques, we also obtain low subpacketization schemes for other multi-receiver settings such as distributed computing and the cache-aided interference channel. We validate the performance of all our schemes via extensive numerical comparisons. For a special class of symmetric caching schemes with a given subpacketization level, we propose two new information theoretic lower bounds on the optimal rate of coded caching.

Abstract

We demonstrate time-, space-, and weight-based calibration techniques for pressure monitoring in the low-cost plastic optical fiber (POF) sensor carpet. It is found that the POF-based pressure sensing platform has several limitations, such as output voltage variations and difference in the sensor response for horizontal and vertical fibers due to convex and concave shapes of bend. To surpass the these limitations, we have developed time-based calibration using time windowing technique to reduce the standard deviation of photodiode output voltage variations by 98%. We have also described space and weight calibration techniques based on the convex and concave shapes of fiber bend. These techniques help in improving the Landweber image reconstruction algorithm to obtain significant clarity and improvement in object pressure images. We also report an artificial intelligence-based algorithm to determine the accuracy of positioning of the load. Experimental results demonstrate that this algorithm gives a mean square error of 0.875 cm in position detection on the carpet. We discuss the potential for a compact and cost-effective pressure sensor carpet, which integrates with the living environment and the outside world.

Abstract

The heterogeneous fast side-chain dynamics of proteins plays crucial roles in molecular recognition and binding. Site-specific NMR experiments quantify these motions by measuring the model-free order parameter (Oaxis2) on a scale of 0 (most flexible) to 1 (least flexible) for each methyl-containing residue of proteins. Here, we have examined ligand-induced variations in the fast side-chain dynamics and conformational entropy of calmodulin (CaM) using five different CaM–peptide complexes. Oaxis2 of CaM in the ligand-free (Oaxis,U2) and ligand-bound (Oaxis,B2) states are calculated from molecular dynamics trajectories and conformational energy surfaces obtained using the adaptive biasing force (ABF) method. ΔOaxis2 = Oaxis,B2 – Oaxis,U2 follows a Gaussian-like unimodal distribution whose second moment is a potential indicator of the binding affinity of these complexes. The probability for the binding-induced Oaxis,U2 → Oaxis,B2 transition decreases with increasing magnitude of ΔOaxis2, indicating that large flexibility changes are improbable for side chains of CaM after ligand binding. A linear correlation established between ΔOaxis2 and the conformational entropy change of the protein makes possible the determination of the conformational entropy of binding of protein–ligand complexes. The results not only underscore the functional importance of fast side-chain fluctuations but also highlight key motional and thermodynamic correlates of protein–ligand binding.

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.