This article will scientifically evaluate individual COVID-19 policies of countries around the world, U.S. states, and Japanese prefectures, respectively. The efficacy of the vaccines has been reported in many of the world’s top medical journals, but even after more than a year of vaccination, the claims have yet to be met. Human emotions, behaviors, and individual policies can significantly influence the outcome against the pandemic. The evaluation in this article is based on a single determinant of the policy outcome. Scoring policies is based on dividing the number of deaths due to COVID-19 by the population in millions. The lower the score, the better the policy. Unfortunately, scores monotonically increase, so that policymakers can only suppress them but cannot improve or decrease them. Therefore, mistakes by policymakers cannot be corrected in the future and they are fatal forever. The result using three tools will reveal the best COVID-19 policy in the world. The revealed policy should have been or be adopted in individual countries for mitigating and ending the COVID-19 pandemic. This article also suggests what is needed in our society for reducing the unnecessary deaths due to COVID-19.

In this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB). Given a document in a KB consisting of words and entity annotations, we train our model to predict the entity that the document describes and map the document and its target entity close to each other in a continuous vector space. Our model is trained using a large number of documents extracted from Wikipedia. The performance of the proposed model is evaluated using two tasks, namely fine-grained entity typing and multiclass text classification. The results demonstrate that our model achieves state-of-the-art performance on both tasks. The code and the trained representations are made available online for further academic research.

The latest study shows that various substances indoor have an adverse effect on the quality of human life. It was reported that a high concentration of Carbon Dioxide (CO2) has an adverse effect on human's intellectual activity. In order to improve our intellectual activities, we must clarify the diffusion of CO2 concentration from human breath by CO2 measurement units. In this study, we have measured and analyzed the diffusion of CO2 depending on human positions, the distance and heights in the room by simultaneous multi-point sensing. In order to control the amount of CO2 emitted from human breath, we examined using respiration induction that human breath frequency is controlled by music. The results show that CO2 from human breath diffuses throughout the room regardless of the position of persons, distance and height difference. We also have found that it is possible to influence CO2 emissions by respiration induction using monotonous music.

We describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and supervised settings. As a result, we achieved state-of-the-art results on all three of these tasks. Our code and trained models are publicly available for further academic research.

Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB) (e.g., Wikipedia).In this paper, we propose a novel embedding method specifically designed for NED.The proposed method jointly maps words and entities into the same continuous vector space.We extend the skip-gram model by using two models.The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words.By combining contexts based on the proposed embedding with standard NED features, we achieved state-of-theart accuracy of 93.1% on the standard CoNLL dataset and 85.2% on the TAC 2010 dataset.

Recent years, robots are useful at home such as cleaning task or communication tools. So there are a lot of studies about cooperative behavior with robots. In order to realize cooperative tasks with robots, it is necessary that robots estimate human intention from human behavior and act in the context based on the intention. In this research, we construct an agent model that enables coordinated behavior by estimating human intention. We focus on collision avoidance as an example of a simple cooperative behavior. We implemented that a agent has Meta-Strategy model in a virtual environment. We have a collision avoidance experiment between virtual agent and human subject and analyzed subject's behavior. It was confirmed that the agent's behavior can influence the human avoidance behavior from experimental results. By indicating the agent's intention, we consider it is possible to achieve cooperative collision avoidance.

In this paper, we propose Micro Secure Socket Layer (MSSL) for 8-bit flash micro controller that is about 1.3 Kbytes in code size. We have analyzed and compared various cryptographic protocols in TCP/IP stack for Micro Server to propose a simple secure layer based on simple handshake processing and encryption. Security implementation for Micro Server, which has very limited size of memory with a small processor, is very difficult and challenging task. However, security on the applications of ubiquitous sensors has become very important issues recently. Crackers can easily access to the sensor nodes or Micro Servers without security. Because the proposed MSSL is very small in code size, it can be implemented and is suitable for small sensors and Micro Server systems.

File-system integrity tools (FIT) are commonly deployed host-based intrusion detections (HIDS) tool to detect unauthorized file-system changes. While FIT are widely used, this kind of HIDS has many drawbacks: the intrusion detection is not done in real-time manner, which might render the whole scheme useless if the attacker can somehow take over the system with privileged access in the time between. The administrator also has a lot of problems to keep the base-line database updating. Besides, the database and the FIT itself are vulnerable if the attacker gains local privileged access.This paper presents a novel approach to address the outstanding problems of the current FIT. We propose a design and implementation of a tool named XenFIT for Xen virtual machines. XenFIT can monitor and fires alarms on intrusion in real-time manner, and our approach does not require to create and update the database like in the legacy methods. XenFIT works by dynamically patching memory of the protected machine, so it is not necessary to install any kernel code or user-space application into the protected machines. As a result, XenFIT is almost effortless to deploy and maintain. In addition, thanks to the advantage introduced by Xen, the security polices as well as the detection process are put in a secure machine, so XenFIT is tamper-resistant with attack, even in case the attacker takes over the whole VM he is penetrating in. Finally, if deploying strictly, XenFIT is able to function very stealthily to avoid the suspect of the intruder.

In this letter, a new self-organizing map (SOM) algorithm with computational cost O(log2 M) is proposed where M2 is the size of a feature map. The first SOM algorithm with O(M2) was originally proposed by Kohonen. The proposed algorithm is composed of the subdividing method and the binary search method. The proposed algorithm does not need the neighborhood functions so that it eliminates the computational cost in learning of neighborhood vectors and the labor of adjusting the parameters of neighborhood functions. The effectiveness of the proposed algorithm was examined by an analysis of codon frequencies of Escherichia coli (E. coli) K12 genes. These drastic computational reduction and accessible application that requires no adjusting of the neighborhood function will be able to contribute to many scientific areas.

Microcode optimization is an NP-complete combinatorial optimization problem. This paper proposes a new method based on the Hopfield neural network for optimizing the wordwidth in the control memory of a microprogrammed digital computer. We present two methodologies, viz., the maximum clique approach, and a cost function based method to minimize an objective function. The maximum clique approach albeit being near O(1) in complexity, is limited in its use for small problem sizes, since it only partitions the data based on the compatibility between the microoperations, and does not minimize the cost function. We thereby use this approach to condition the data initially (to form compatibility classes), and then use the proposed second method to optimize on the cost function. The latter method is then able to discover better solutions than other schemes for the benchmark data set.

This study proposes an adaptive agent as a hybrid of genetic algorithm and neural network, and to clarify the effectiveness of the combination of two mechanisms in the dynamic environment. Evolution and learning can be explained as the mechanism of searching a solution in the enormous possibilities at the population level and individual level, respectively. There are two ways of combination of genetic algorithm and neural network: Darwinian and Lamarckian frameworks. In the Lamarckian framework the acquired traits during the lifetime can be passed on to the offspring directly, while in the Darwinian framework, these cannot be passed on. We propose a "neural agent" whose initial weights of their neural networks are determined by their genome data, as a simple model of the hybrid system of genetic algorithm and neural network. We examine which framework is better in the dynamic system. The result of our simulation shows that the Darwinian framework is better than Lamarckian.

A near-optimum parallel algorithm for solving PLA folding problems is presented in this paper where the problem is NP-complete and one of the most fundamental problems in VLSI design. The proposed system is composed of n/spl times/n neurons based on an artificial two-dimensional maximum neural network where n is the number of inputs and outputs or the number of product lines of PLA. The two-dimensional maximum neurons generate the permutation of inputs and outputs or product lines. Our algorithm can solve not only a simple folding problem but also multiple, bipartite, and constrained folding problems. We have discovered improved solutions in four benchmark problems over the best existing algorithms using the proposed algorithm.

The paper presents a parallel algorithm for time-slot assignment problems in TDM hierarchical switching systems, based on the neural network model. The TDM systems are operated in repetitive frames composed of several time-slots. A time-slot represents a switching configuration where one packet is transmitted through an I/O line. The goal of the algorithm is to find conflict-free time-slot assignments for given switching demands. The algorithm runs on a maximum of n/sup 2//spl times/m processors for m-time-slot problems in n/spl times/n TDM systems. In small problems up to a 24/spl times/24 TDM system, the algorithm can find the optimum solution in a nearly constant time, when it is performed on n/sup 2//spl times/m processors.>

How can a knight be moved on a chessboard so that the knight visits each square once and only once and goes back to the starting square? The earliest serious attempt to find a knight's tour on the chessboard was made by L. Euler in 1759 /spl lsqb/1/spl rsqb/. In this correspondence, a parallel algorithm based on the hysteresis McCulloch-Pitts neurons is proposed to solve the knight's tour problem. The relation between the traveling salesman problem and the knight's tour problem is also discussed. A large number of simulation runs were performed to investigate the behavior of the hysteresis McCulloch-Pitts neural model. The purpose of this correspondence is to present a case study/spl minus/how to successfully represent the combinatorial optimization problems by means of neural network.>

The performances of seven neural network models for traffic control problems in multistage interconnection networks are compared. The decay term, three neuron models, and two heuristics were evaluated. The goal of the traffic control problems is to find conflict-free switching configurations with the maximum throughput. The simulation results show that the hysteresis McCullock-Pitts neuron model without the decay term and with two heuristics has the best performance.>

Topological via-minimization (TVM) algorithms in two-layer channels based on the artificial neural network model are presented. TVM problems require not only assigning wires or nets between terminals to one of two layers without an intersection, but also minimizing the number of vias, which are the single contacts between the nets in the two layers. The goal of the algorithm is to embed the maximum number of nets without an intersection. Two types of TVM problems are examined: split rectangular TVM (RTVM) problems and split circular TVM (CTVM) problems. The algorithms require 3n processing elements for the n-net split RTVM problems, and 5n processing elements for the n-net split CTVM problems. The algorithms were verified by solving seven problems with 20 to 80 nets. The algorithms can be easily extended to problems with more than two layers.>

A parallel algorithm based on an artificial neural network model for broadcast scheduling problems in packet radio networks is presented. The algorithm requires n*m processing elements for an n-mode-m-slot radio network problem. The algorithm is verified by simulating 13 different networks.>

A parallel algorithm for channel routing problems is presented. The problem is to route the given interconnections between two rows of terminals on a multilayer channel where the channel area must be minimized. The current advancement of VLSI chip technology allows one to use four layers composed of two metal layers and two polysilicon layers for routing in a chip. The goal of the proposed parallel algorithm is to find the near-optimum routing solution for the given interconnections in a short time. The algorithm is applied to four-layer channel routing problems requiring n*m*2 processing elements for the n-net-m-track problem. The algorithm has three advantages over the conventional algorithms: (1) it can be easily modified for accommodating more than four-layer problems; (2) it runs both on a sequential machine and on a parallel machine with maximally n*m*2 processors; and (3) the program size is very small. The algorithm is verified by solving seven bench-mark problems.>

The application of the Hopfield neural network for the multispectral unsupervised classification of MR images is reported. Winner-take-all neurons were used to obtain a crisp classification map using proton density-weighted and T(2)-weighted images in the head. The preliminary studies indicate that the number of iterations needed to reach ;good' solutions was nearly constant with the number of clusters chosen for the problem.

The authors propose the first parallel improvement algorithm using the maximum neural network model for the bipartite subgraph problem. The goal of this NP-complete problem is to remove the minimum number of edges in a given graph such that the remaining graph is a bipartite graph. A large number of instances have been simulated to verify the proposed algorithm, with the simulation result showing that the algorithm finds a solution within 200 iteration steps and the solution quality is superior to that of the best existing algorithm. The algorithm is extended for the K-partite subgraph problem where no algorithm has been proposed.

A new parallel processing algorithm for solving string search problems is presented. The proposed algorithm uses O(m*n) processors where n is the length of a text and m is the length of a pattern. It requires two and only two iteration steps to find the pattern in the text, while the best existing parallel algorithm needs the computation time O(loglog n).>

For the original article see ibid., vol.8, no.3, p.257-67 (1989). The commenters point out that their parallel algorithm provides the maximum planar subgraph, and they compare it to the maximal planar subgraph provided by R. Jayakumar et al. in the above-mentioned paper. The space-time product complexity is also compared.>

The authors refers to the work of Y. Takefuji et al. (see ibid., vol.1, pp. 263-267, Sept. (1990)), which is concerned with the problem of RNA secondary structure prediction, and draws the reader's attention to his own model and experiments in training the neural networks on small tRNA subsequences. The author admits that Takefuji et al. outline an elegant way to map the problem onto neural architectures, but suggests that such mappings can be augmented with empirical knowledge (e.g., free energy values of base pairs and substructures) and the ability to learn. In their reply, Y. Takefuji and K.-C. Lee hold that the necessity of the learning capability for the RNA secondary structure prediction is questionable. They believe that the task is to build a robust parallel algorithm considering more thermodynamic properties in the model.

A parallel algorithm based on neural networks for solving sorting problems is presented. The proposed algorithm usesO(n2) processing elements called binary neurons, wherenis the number of unsorted elements. It requires two and only two iteration steps, while the conventional parallel sorting algorithm using (On) processors proposed by F.T. Leighton (1984) requires the computation timeO(logn). A set of simulation results substantiates the proposed algorithm. The hardware system based on the proposed parallel algorithm is also presented

A parallel algorithm for finding a near-maximum independent set in a circle graph is presented. An independent set in a graph is a set of vertices, no two of which are adjacent. A maximum independent set is an independent set whose cardinality is the largest among all independent sets of a graph. The algorithm is modified for predicting the secondary structure in ribonucleic acids (RNA). The proposed system, composed of an n neural network array (where n is the number of edges in the circle graph of the number of possible base pairs), not only generates a near-maximum independent set but also predicts the secondary structure of ribonucleic acids within several hundred iteration steps. The simulator discovered several solutions which are more stable structures, in a sequence of 359 bases from the potato spindle tuber viroid, than previously proposed structures.

A parallel algorithm for tiling with polyominoes is presented. The tiling problem is to pack polyominoes in a finite checkerboard. The algorithm using lxmxn processing elements requires O(1) time, where l is the number of different kinds of polyominoes on an mxn checkerboard. The algorithm can be used for placement of components or cells in a very large-scale integrated circuit (VLSI) chip, designing and compacting printed circuit boards, and solving a variety of two- or three-dimensional packing problems.

A statistical learning model called the multinomial conjunctoid is reviewed. Multinomial conjunctoids are based on a well-developed, statistical-decision-theory framework, which guarantees that conjunctoid learning will converge to optimal states over learning trials and the learning will be fast during these trials. In addition, a prototype multinomial conjunctoid module based on CMOS VLSI technology is introduced.>

In this paper a parallel and piplined fast matrix equation solver in GF(2) is proposed where the elements are 0s or 1s. The solver employing the iterative logic circuits which are suitable for VLSI implementation can be realized by the conventional Gauss Jordan Elimination Method. O(n) gate stages in the pipeline and O(n3) total gates are required for solving A X = b where A is a matrix of n×n, X and b are vectors respectively. The organization of the solver is discussed in this paper.