Average Number Of Inputs Research Articles

Call center performance to drive patient access.

135 Background: Access to oncology care through a specialty call center within a large academic institution was analyzed to determine service levels necessary to schedule patient consults within 7 days of contact. The specialty call center is the primary resource for ambulatory appointment scheduling, and staffed by 1 licensed provider and 6 agents who administratively coordinate patient care. Hours of operation are 8AM-5PM. Methods: Data was analyzed between a period of 1/1/2018 and 9/30/2018 (P1), providing call statistics (S1): inbound & outbound calls, calls not answered, average speed of answer, agent availability. Observations of agents identified key processes, staffing analysis, and productivity levels. A dependency map was developed to identify key inputs and outputs for all call center activities. The number of dependencies were identified for each activity. Results: A dependency map identified 22 unique agent activities, categorized by administrative tasks (n = 9), multi-disciplinary team coordination (n = 7), direct patient interaction (n = 5), and patient benefit processing (n = 3). Average number of inputs per activity yielded 2.4 (Range = 0 to 8), while outputs were 2.0 (Range = 0 to 6). Inbound and outbound calls spanned multiple categories, with 28,730 calls received or placed during P1. 77% of inbound calls were answered, and remaining calls were directed to voicemail. 89% of all calls occurred between 9AM and 4PM, with slight variation each hour. Staffing Analysis revealed 91.3% availability rate equivalent to 5.48 agents available per day. Average call hours (inbound and outbound) per agent ranged from 2.45hrs/day to 5.18 hours/day, with variation attributed to the unique activities. An algorithm was created to determine appropriate staffing levels to ensure S1 requirements and timely coordination of care. Conclusions: A specialty call center requires a detailed understanding of dependencies that contribute to timely scheduling, while maximizing customer service. Dependency mapping provides visibility to clinical and non-clinical teams of necessary activities to coordinate appointments. Staffing models can assist in understanding the variables that influence S1, and provide support for staffing decisions in periods of high/low demand.

Toward Optimal Fusion Algorithms With Security Against Wolves and Lambs in Biometrics

It is known that different users have different degrees of accuracy in biometric authentication, and claimants and enrollees who cause false accepts against many others are referred to as wolves and lambs, respectively. The aim of this paper is to develop a fusion algorithm, which has security against both of the animals while minimizing the number of query samples a genuine claimant has to input. To achieve our aim, we first introduce a taxonomy of wolves and lambs, and propose a minimum log-likelihood ratio-based sequential fusion scheme (MLR scheme). We prove that this scheme keeps wolf attack probability and lamb accept probability, the maximum of the claimant-specific false accept probability (FAP), and the enrollee-specific FAP, less than a desired value if log-likelihood ratios are perfectly estimated, except in the case of adaptive spoofing wolves. We also prove that this scheme is optimal with regard to false reject probability (FRP), and asymptotically optimal with respect to the average number of inputs (ANIs) under some conditions. We further propose an input order decision scheme based on the Kullback-Leibler (KL) divergence, which maximizes the expectation of a genuine log-likelihood ratio, to further reduce ANI of the MLR scheme in the case where the KL divergence differs from one modality to another. The results of the experimental evaluation using a virtual multimodal (one face and eight fingerprints) data set showed the effectiveness of our schemes.

Emergence of robustness against noise: A structural phase transition in evolved models of gene regulatory networks

We investigate the evolution of Boolean networks subject to a selective pressure which favors robustness against noise, as a model of evolved genetic regulatory systems. By mapping the evolutionary process into a statistical ensemble and minimizing its associated free energy, we find the structural properties which emerge as the selective pressure is increased and identify a phase transition from a random topology to a "segregated-core" structure, where a smaller and more densely connected subset of the nodes is responsible for most of the regulation in the network. This segregated structure is very similar qualitatively to what is found in gene regulatory networks, where only a much smaller subset of genes--those responsible for transcription factors-is responsible for global regulation. We obtain the full phase diagram of the evolutionary process as a function of selective pressure and the average number of inputs per node. We compare the theoretical predictions with Monte Carlo simulations of evolved networks and with empirical data for Saccharomyces cerevisiae and Escherichia coli.

Heuristic Approach to Sparse Approximation of Gene Regulatory Networks

Determining the structure of the gene regulatory network using the information in genomewide profiles of mRNA abundance, such as microarray data, poses several challenges. Typically, "static" rather than dynamical profile measurements, such as those taken from steady state tissues in various conditions, are the starting point. This makes the inference of causal relationships between genes difficult. Moreover, the paucity of samples relative to the gene number leads to problems such as overfitting and underconstrained regression analysis. Here we present a novel method for the sparse approximation of gene regulatory networks that addresses these issues. It is formulated as a sparse combinatorial optimization problem which has a globally optimal solution in terms of l(0) norm error. In order to seek an approximate solution of the l(0) optimization problem, we consider a heuristic approach based on iterative greedy algorithms. We apply our method to a set of gene expression profiles comprising of 24,102 genes measured over 79 human tissues. The inferred network is a signed directed graph, hence predicts causal relationships. It exhibits typical characteristics of regulatory networks organism with partially known network topology, such as the average number of inputs per gene as well as the in-degree and out-degree distribution.

Development of input connections in neural cultures

We introduce an approach for the quantitative assessment of the connectivity in neuronal cultures, based on the statistical mechanics of percolation on a graph. This allows us to monitor the development of the culture and to see the emergence of connectivity in the network. The culture becomes fully connected at a time equivalent to the expected time of birth. The spontaneous bursting activity that characterizes cultures develops in parallel with the connectivity. The average number of inputs per neuron can be quantitatively determined in units of m(0), the number of activated inputs needed to excite the neuron. For m(0) approximately 15 we find that hippocampal neurons have on average approximately 60-120 inputs, whereas cortical neurons have approximately 75-150, depending on neuronal density. The ratio of excitatory to inhibitory neurons is determined by using the GABA(A) antagonist bicuculine. This ratio changes during development and reaches the final value at day 7-8, coinciding with the expected time of the GABA switch. For hippocampal cultures the inhibitory cells comprise approximately 30% of the neurons in the culture whereas for cortical cultures they are approximately 20%. Such detailed global information on the connectivity of networks in neuronal cultures is at present inaccessible by any electrophysiological or other technique.

Fatigue estimation of visual display terminal (VDT) workers by physiological data--preliminary study of a new computer program for VDT work analysis

The methods for assessing VDT workload based on individual capabilities are necessary to evaluate general fatigue by VDT work. We have developed a new computer program for assessing data entry performance of each individual. Four healthy male volunteers conducted a data entry task at their own pace using this program under a controlled environment. They performed the task for 30 minutes twice with 10 minutes rest between them. As a result, the average number of inputs was 133.2 characters/min in the first task and 129.1 characters/min after taking the rest. The error rate was 8.6% and 10.6% in each task. This program was useful to evaluate their data entry performance. We also reported our concern with a drop in nasal skin temperature that was observed during these VDT tasks.

2+p-SAT: Relation of typical-case complexity to the nature of the phase transition

Heuristic methods for solution of problems in the NP-complete class of decision problems often reach exact solutions, but fail badly at “phase boundaries,” across which the decision to be reached changes from almost always having one value to almost always having a different value. We report an analytic solution and experimental investigations of the phase transition that occurs in the limit of very large problems in K-SAT. Studying a model which interpolates K-SAT between K=2 and K=3, we find a change from a continuous to a discontinuous phase transition when K, the average number of inputs per clause, exceeds 0.4. The cost of finding solutions also increases dramatically above this changeover. The nature of its “random first-order” phase transition, seen at values of K large enough to make the computational cost of solving typical instances increase exponentially with problem size, suggests a mechanism for the cost increase. There has been evidence for features like the “backbone” of frozen inputs which characterizes the UNSAT phase in K-SAT in the study of models of disordered materials, but this feature and this transition are uniquely accessible to analysis in K-SAT. The random first-order transition combines properties of the first-order (discontinuous onset of order) and second-order (with power law scaling, e.g., of the width of the critical region in a finite system) transitions known in the physics of pure solids. Such transitions should occur in other combinatoric problems in the large N limit. Finally, improved search heuristics may be developed when a “backbone” is known to exist. ©1999 John Wiley & Sons, Inc. Random Struct. Alg., 15, 414–435, 1999

Use of formamide increases the number of detectable inputs to polyneuronally innervated mammalian skeletal muscle

We evaluated the extent of polyneuronal innervation in neonatal rat lateral gastrocnemius muscle with intracellular recording techniques using both formamide and d-tubocurarine as paralytic agents. We detected more polyneuronal innervation during the first postnatal week using formamide than d-tubocurarine. Both the average number of inputs per muscle cell and the percentage of polyinnervated cells were greater when formamide, rather than d-tubocurarine, was used to diminish muscle contraction. The difference in the extent of polyneuronal innervation detected using the two methods declines during the first postnatal week and the number of inputs observed with formamide during the second postnatal week does not differ from that seen with d-tubocurarine.

Asymmetric neural networks with multispin interactions.

A dilute nonsymmetric ferromagnetic neural network with multispin interactions and a version of the Hopfield model with multispin interactions are solved in the limit where the average number of inputs per spin is finite. It is found that in such asymmetric systems the critical exponents of local quantities can take a few different values, instead of just one.

Inhomogeneous magnetization in dilute asymmetric and symmetric systems.

A dilute nonsymmetric ferromagnet and a version of the Hopfield model are solved in the limit where the average number of inputs per spin is finite. The solution is based on the fact that the magnetization of each site is a function of the number and the type of inputs and their connection strengths. Extension of this solution to the symmetric case is discussed. The solution can also be extended to any other distribution of interactions.

Expected Capacity of (m2)-Networks

A concentrator is an interconnection network with n inputs and m outputs, n > m, wherein any specified subset of inputs of size less than or equal to some number, called the network's actual capacity, can always be simultaneously connected to some equal-sized but unspecifiable subset of outputs. Guaranteed throughput as described by actual capacity has heretofore been the principle measure for evaluating concentrator performance. In many applications, however, a more practical measure of a concentrator's capability is a probabilistic measure of its throughput in the following sense: given an input subset of size k, k ≤ m, what is the average number of inputs that can be connected to outputs? This measure will be called expected capacity. This paper considers the expected capacity of a special class of sparse crossbar concentrators called ( m 2 ) networks. It is seen that the expected capacity values for ( m 2 ) networks are usually quite close to k.

Embryonic growth and innervation of rat skeletal muscles. III. Neural regulation of junctional and extra-junctional acetylcholine receptor clusters.

The number and distribution of acetylcholine (ACh) receptors on muscle cells was studied during development of normal, paralysed and aneural embryonic rat diaphragm muscles. (i) ACh receptors initially are dispersed over the surface of rat embryo myotubes. At day 15| of gestation junctional receptor clusters (‘J-clusters’) form in a well ordered band across the midline of the diaphragm muscle; these also form in denervated and paralysed muscles. At about day 18 of gestation additional ‘EJ-clusters’ develop to either side of the midpoint of treated muscles. (ii) If a nerve terminal is present, J-clusters increase in length with time. The time course of generation of new endplates calculated from frequency distributions of J-cluster lengths accurately predicts the muscle growth curve established from muscle fibre counts. (iii) The mean length of J-clusters in paralysed muscles was greater than in controls, due to small new-formed clusters failing to appear. In muscles allowed to recover from paralysis the mean length was less, due to a preponderance of small, new-formed clusters. These observations show that development of new endplates, which is thought to reflect the development of new muscle cells, is halted in paralysed muscles, and recovery from paralysis is associated with the generation of many new endplates. (iv) J-clusters appeared, but failed to grow, in aneural muscles. In muscles denervated during the later stages of gestation, analysis of the distribution of J-cluster lengths shows that new clusters failed to appear, and existing clusters showed little or no increase in length after the time of removal of the nerve. (v) EJ-clusters form by aggregation of dispersed receptors, and their mean length increases with time. They do not appear to be stable entities, and are removed within 2 d of recovery from paralysis. In paralysed muscles, with both J-clusters and EJclusters present, only J-clusters attract nerve sprouts or become innervated. (vi) A curve is derived showing development of the total number of synaptic terminals in a muscle. This number increases during days 13-18 of gestation, reaching a peak of about 170 % of the adult value during dl8 and d l9 of gestation. There are two episodes of terminal elimination, one during days 19-21 of gestation, and another about 2 weeks postnatally. During the first postnatal week the number of terminals remains constant at about 140% of the adult number, while the average number of inputs per fibre goes down and the number of muscle fibres increases. (vii) Innervation is essential for muscle development. Motoneurons cannot regulate the number of muscle fibres by requiring a simple one-to-one relation between nerve terminal and muscle fibre, and if their role is regulatory as well as supportive of muscle development then some more complex relationship between nerve terminals and developing myotubes must be postulated.

Redundancy Testing in Combinational Networks

A simple, necessary and sufficient test is developed for testing whether a single connection in a tree-type NAND network is redundant. A procedure is presented for testing every connection in the network. The computational complexity of the procedure is mi2 where m = the number of gates and i = the average number of inputs per gate in the network.