A Graph Theoretic Approach for Multi-Objective Budget Constrained Capsule Wardrobe Recommendation

Traditionally, capsule wardrobes are manually designed by expert fashionistas through their creativity and technical prowess. The goal is to curate minimal fashion items that can be assembled into several compatible and versatile outfits. It is usually a cost and time intensive process, and hence lacks scalability. Although there are a few approaches that attempt to automate the process, they tend to ignore the price of items or shopping budget. In this article, we formulate this task as a multi-objective budget constrained capsule wardrobe recommendation ( MOBCCWR ) problem. It is modeled as a bipartite graph having two disjoint vertex sets corresponding to top-wear and bottom-wear items, respectively. An edge represents compatibility between the corresponding item pairs. The objective is to find a 1-neighbor subset of fashion items as a capsule wardrobe that jointly maximize compatibility and versatility scores by considering corresponding user-specified preference weight coefficients and an overall shopping budget as a means of achieving personalization. We study the complexity class of MOBCCWR , show that it is NP-Complete, and propose a greedy algorithm for finding a near-optimal solution in real time. We also analyze the time complexity and approximation bound for our algorithm. Experimental results show the effectiveness of the proposed approach on both real and synthetic datasets.

DeepGOZero: Improving protein function prediction from sequence and zero-shot learning based on ontology axioms

Motivation: Protein functions are often described using the Gene Ontology (GO) which is an ontology consisting of over 50,000 classes and a large set of formal axioms. Predicting the functions of proteins is one of the key challenges in computational biology and a variety of machine learning methods have been developed for this purpose. However, these methods usually require significant amount of training data and cannot make predictions for GO classes which have only few or no experimental annotations. Results: We developed DeepGOZero, a machine learning model which improves predictions for functions with no or only a small number of annotations. To achieve this goal, we rely on a model-theoretic approach for learning ontology embeddings and combine it with neural networks for protein function prediction. DeepGOZero can exploit formal axioms in the GO to make zero-shot predictions, i.e., predict protein functions even if not a single protein in the training phase was associated with that function. Furthermore, the zero-shot prediction method employed by DeepGOZero is generic and can be applied whenever associations with ontology classes need to be predicted. Availability: http://github.com/bio-ontology-research-group/deepgozero

Group Theoretical Description of the Periodic System

The group theoretical description of the periodic system of elements in the framework of the Rumer–Fet model is considered. We introduce the concept of a single quantum system, the generating core of which is an abstract C*-algebra. It is shown that various concrete implementations of the operator algebra depend on the structure of the generators of the fundamental symmetry group attached to the energy operator. In the case of the generators of the complex shell of a group algebra of a conformal group, the spectrum of states of a single quantum system is given in the framework of the basic representation of the Rumer–Fet group, which leads to a group-theoretic interpretation of the Mendeleev’s periodic system of elements. A mass formula is introduced that allows giving the termwise mass splitting for the main multiplet of the Rumer–Fet group. The masses of elements of the Seaborg table (eight-periodic extension of the Mendeleev table) are calculated starting from the atomic number Z=3 and going to Z=220. The continuation of the Seaborg homology between lanthanides and actinides is established with the group of superactinides. A 10-periodic extension of the periodic table is introduced in the framework of the group-theoretic approach. The multiplet structure of the extended table’s periods is considered in detail. It is shown that the period lengths of the system of elements are determined by the structure of the basic representation of the Rumer–Fet group. The theoretical masses of the elements of 10th and 11th periods are calculated starting from Z=221 and going to to Z=364. The concept of hypertwistor is introduced.

Coordination mechanisms for digital and sustainable textile supply chain

PurposeSupply chain network is complicated to manage due to the involvement of a number of agents. Formation of virtual organization using Industry 4.0 (I4.0) is an approach to improve the efficiency and effectiveness and to overcome the complexities of the channel. However, the task of managing the channel further becomes complicated after incorporating sustainability into the supply chain. To fill this gap, this paper focuses on designing of mechanism and demonstration of I4.0-based virtual organization to coordinate sustainable supply chain.Design/methodology/approachIn this paper, we model and compare I4.0-based virtual organization models using four other traditional contracts with centralized supply chain. The non-cooperative game theoretic approach has been used for the analysis of models.FindingsOur game-theoretic analysis shows that investment in I4.0 and sustainable innovation are beneficial for the overall supply chain. Our results show that linear two-part tariff contract and I4.0-based virtual organization model can perfectly coordinated with the supply chain.Research limitations/implicationsThis study consider deterministic model settings with full information game. Therefore researchers are encouraged to study I4.0-based coordination models under information asymmetry and uncertain situations.Practical implicationsThe paper includes implications for the development of I4.0-based coordination model to tackle the problems of channel coordination.Originality/valueThis study proposes I4.0-based game-theoretic model for the sustainable supply chain coordination.

A New Top-Down Context-Free Parsing for Syntactic Pattern Recognition

The numerous different mathematical methods used to solve pattern recognition snags may be assembled into two universal approaches: the decision-theoretic approach and the syntactic(structural) approach. In this paper, at first syntactic pattern recognition method and formal grammars are described and then has been investigated one of the techniques in syntactic pattern recognition called top – down tabular parser known as Earley’s algorithm Earley's tabular parser is one of the methods of context -free grammar parsing for syntactic pattern recognition. Earley's algorithm uses array data structure for implementing, which is the main problem and for this reason takes a lots of time, searching in array and grammar parsing, and wasting lots of memory. In order to solve these problems and most important, the cubic time complexity, in this article, a new algorithm has been introduced, which reduces wasting the memory to zero, with using linked list data structure. Also, with the changes in the implementation and performance of the algorithm, cubic time complexity has transformed into O (n*R) order. Key words: syntactic pattern recognition, tabular parser, context –free grammar, time complexity, linked list data structure.

WHY AND WHEN SOME PEOPLE WOULD THINK A GIRL IS MORE LIKELY TO CHEAT: A GAME-THEORETIC APPROACH

The question, “Who is more likely to cheat in a relationship?” has been discussed a lot recently. This short exercise intends to explain that in a world where people subscribe to certain “Beliefs” and under a certain condition called the “No Love Condition”, a situation can be found such that, in a relationship, the girl is more likely to cheat than the boy. The question of “Why?” is answered by the Beliefs and the No Love Condition. The question of “When?” is answered by the situation to be found.