Joint multiple dictionary learning for tensor sparse coding

Traditional dictionary learning algorithms are used for finding a sparse representation on high dimensional data by transforming samples into a one-dimensional (1D) vector. This 1D model loses the inherent spatial structure property of data. An alternative solution is to employ Tensor Decomposition for dictionary learning on their original structural form —a tensor— by learning multiple dictionaries along each mode and the corresponding sparse representation in respect to the Kronecker product of these dictionaries. To learn tensor dictionaries along each mode, all the existing methods update each dictionary iteratively in an alternating manner. Because atoms from each mode dictionary jointly make contributions to the sparsity of tensor, existing works ignore atoms correlations between different mode dictionaries by treating each mode dictionary independently. In this paper, we propose a joint multiple dictionary learning method for tensor sparse coding, which explores atom correlations for sparse representation and updates multiple atoms from each mode dictionary simultaneously. In this algorithm, the Frequent-Pattern Tree (FP-tree) mining algorithm is employed to exploit frequent atom patterns in the sparse representation. Inspired by the idea of K-SVD, we develop a new dictionary update method that jointly updates elements in each pattern. Experimental results demonstrate our method outperforms other tensor based dictionary learning algorithms.

Clinicopathologic features and outcome of mycophenolate-induced colitis in renal transplant recipients

Reports on the clinical course of mycophenolic acid (MPA)-related colitis in kidney transplant recipients are scarce. This study aimed at assessing MPA-related colitis incidence, risk factors, and progression after kidney transplantation. All kidney transplant patients taking MPA who had colonic biopsies for persistent chronic diarrhea, between 2000 and 2012, at the Kidney Transplantation Unit of Botucatu Medical School Hospital, Brazil, were included. Cytomegalovirus (CMV) immunohistochemistry was performed in all biopsy specimens. Data on presenting symptoms, medications, immunosuppressive drugs, colonoscopic findings, and follow-up were obtained. Of 580 kidney transplant patients on MPA, 34 underwent colonoscopy. Colonoscopic findings were associated with MPA usage in 16 patients. The most frequent histologic patterns were non-specific colitis (31.3%), inflammatory bowel disease (IBD)-like colitis (25%), normal/near normal (18.8%), graft-versus-host disease-like (18.8%), and ischemia-like colitis (12.5%). All patients had persistent acute diarrhea and weight loss. Six of the 16 MPA-related diarrhea patients (37.5%) showed acute dehydration requiring hospitalization. Diarrhea resolved when MPA was switched to sirolimus (50%), discontinued (18.75%), switched to azathioprine (12.5%), or reduced by 50% (18.75%). No graft loss occurred. Four patients died during the study period. Late-onset MPA was more frequent, and no correlation with MPA dose or formulation was found.

Dynamics of mother-pup interactions: tradeoff between environment and physiology

The emergence and maintenance of maternal behavior are under the influence of environmental cues such as light and dark periods. This article discusses the characteristic neurobiology of the behavioral patterns of lactating rats. Specifically, the hormonal basis and neurocircuits that determine whether mother rats show typical sequential patterns of behavioral responses are discussed. During lactation, rats express a sequential pattern of behavioral parameters that may be determined by hormonal variations. Sensorial signals emitted by pups, as well as environmental cues, are suggested to serve as conditioned stimuli for these animals. Finally, the expression of maternal behavior is discussed under neuroeconomic and evolutionary perspectives.

Single-room usage patterns and allocation decision-making in an Australian public hospital: a sequential exploratory study

AIMS AND OBJECTIVES: The aims are to (1) measure occupancy rates of single and shared rooms; (2) compare single room usage patterns and (3) explore the practice, rationale and decision-making processes associated with single rooms; across one Australian public health service.

BACKGROUND: There is a tendency in Australia and internationally to increase the proportion of single patient rooms in hospitals. To date there have been no Australian studies that investigate the use of single rooms in clinical practice.

DESIGN: This study used a sequential exploratory design with data collected in 2014.

METHODS: A descriptive survey was used to measure the use of single rooms across a two-week time frame. Semi-structured interviews were undertaken with occupancy decision-makers to explore the practices, rationale decision-making process associated with single-room allocation.

RESULTS: Total bed occupancy did not fall below 99·4% during the period of data collection. Infection control was the primary reason for patients to be allocated to a single room, however, the patterns varied according to ward type and single-room availability. For occupancy decision-makers, decisions about patient allocation was a complex and challenging process, influenced and complicated by numerous factors including occupancy rates, the infection status of the patient/s, funding and patient/family preference. Bed moves were common resulting from frequent re-evaluation of need.

CONCLUSION: Apart from infection control mandates, there was little tangible evidence to guide decision-making about single-room allocation. Further work is necessary to assist nurses in their decision-making.

RELEVANCE TO CLINICAL PRACTICE: There is a trend towards increasing the proportion of single rooms in new hospital builds. Coupled with the competing clinical demands for single room care, this study highlights the complexity of nursing decision-making about patient allocation to single rooms, an issue urgently requiring further attention.

Statistical significance of sequential firing patterns in multi-neuronal spike trains

Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.

Sequential Memory with ART: A Self-Organizing Network Capable of Learning Sequences of Patterns

A model which extends the adaptive resonance theory model to sequential memory is presented. This new model learns sequences of events and recalls a sequence when presented with parts of the sequence. A sequence can have repeated events and different sequences can share events. The ART model is modified by creating interconnected sublayers within ART's F2 layer. Nodes within F2 learn temporal patterns by forming recency gradients within LTM. Versions of the ART model like ART I, ART 2, and fuzzy ART can be used.

Mining frequent patterns for AMP-activated protein kinase regulation on skeletal muscle

Background
AMP-activated protein kinase (AMPK) has emerged as a significant signaling intermediary that regulates metabolisms in response to energy demand and supply. An investigation into the degree of activation and deactivation of AMPK subunits under exercise can provide valuable data for understanding AMPK. In particular, the effect of AMPK on muscle cellular energy status makes this protein a promising pharmacological target for disease treatment. As more AMPK regulation data are accumulated, data mining techniques can play an important role in identifying frequent patterns in the data. Association rule mining, which is commonly used in market basket analysis, can be applied to AMPK regulation.

Results
This paper proposes a framework that can identify the potential correlation, either between the state of isoforms of α, β and γ subunits of AMPK, or between stimulus factors and the state of isoforms. Our approach is to apply item constraints in the closed interpretation to the itemset generation so that a threshold is specified in terms of the amount of results, rather than a fixed threshold value for all itemsets of all sizes. The derived rules from experiments are roughly analyzed. It is found that most of the extracted association rules have biological meaning and some of them were previously unknown. They indicate direction for further research.

Conclusion
Our findings indicate that AMPK has a great impact on most metabolic actions that are related to energy demand and supply. Those actions are adjusted via its subunit isoforms under specific physical training. Thus, there are strong co-relationships between AMPK subunit isoforms and exercises. Furthermore, the subunit isoforms are correlated with each other in some cases. The methods developed here could be used when predicting these essential relationships and enable an understanding of the functions and metabolic pathways regarding AMPK.

Adaptive load shedding for mining frequent patterns from data streams

Most algorithms that focus on discovering frequent patterns from data streams assumed that the machinery is capable of managing all the incoming transactions without any delay; or without the need to drop transactions. However, this assumption is often impractical due to the inherent characteristics of data stream environments. Especially under high load conditions, there is often a shortage of system resources to process the incoming transactions. This causes unwanted latencies that in turn, affects the applicability of the data mining models produced – which often has a small window of opportunity. We propose a load shedding algorithm to address this issue. The algorithm adaptively detects overload situations and drops transactions from data streams using a probabilistic model. We tested our algorithm on both synthetic and real-life datasets to verify the feasibility of our algorithm.

EFP-M2 : efficient model for mining frequent patterns in transactional database

Discovering frequent patterns plays an essential role in many data mining applications. The aim of frequent patterns is to obtain the information about the most common patterns that appeared together. However, designing an efficient model to mine these patterns is still demanding due to the capacity of current database size. Therefore, we propose an Efficient Frequent Pattern Mining Model (EFP-M2) to mine the frequent patterns in timely manner. The result shows that the algorithm in EFP-M2l is outperformed at least at 2 orders of magnitudes against the benchmarked FP-Growth.

Analisi e applicazione dei processi di data mining al flusso informativo di sistemi real-time: Implementazione e analisi di un algoritmo autoadattivo per la ricerca di frequent patterns su macchine automatiche.

Analisi e applicazione dei processi di data mining al flusso informativo di sistemi real-time. Implementazione e analisi di un algoritmo autoadattivo per la ricerca di frequent patterns su macchine automatiche.

Analisi e applicazione dei processi di data mining al flusso informativo di sistemi real-time: Adattamento di un algoritmo di apprendimento automatico per la caratterizzazione e la ricerca di frequent patterns su macchine automatiche

La tesi da me svolta durante questi ultimi sei mesi è stata sviluppata presso i laboratori di ricerca di IMA S.p.a.. IMA (Industria Macchine Automatiche) è una azienda italiana che naque nel 1961 a Bologna ed oggi riveste il ruolo di leader mondiale nella produzione di macchine automatiche per il packaging di medicinali. Vorrei subito mettere in luce che in tale contesto applicativo l’utilizzo di algoritmi di data-mining risulta essere ostico a causa dei due ambienti in cui mi trovo. Il primo è quello delle macchine automatiche che operano con sistemi in tempo reale dato che non presentano a pieno le risorse di cui necessitano tali algoritmi. Il secondo è relativo alla produzione di farmaci in quanto vige una normativa internazionale molto restrittiva che impone il tracciamento di tutti gli eventi trascorsi durante l’impacchettamento ma che non permette la visione al mondo esterno di questi dati sensibili. Emerge immediatamente l’interesse nell’utilizzo di tali informazioni che potrebbero far affiorare degli eventi riconducibili a un problema della macchina o a un qualche tipo di errore al fine di migliorare l’efficacia e l’efficienza dei prodotti IMA. Lo sforzo maggiore per riuscire ad ideare una strategia applicativa è stata nella comprensione ed interpretazione dei messaggi relativi agli aspetti software. Essendo i dati molti, chiusi, e le macchine con scarse risorse per poter applicare a dovere gli algoritmi di data mining ho provveduto ad adottare diversi approcci in diversi contesti applicativi: • Sistema di identificazione automatica di errore al fine di aumentare di diminuire i tempi di correzione di essi. • Modifica di un algoritmo di letteratura per la caratterizzazione della macchina. La trattazione è così strutturata: • Capitolo 1: descrive la macchina automatica IMA Adapta della quale ci sono stati forniti i vari file di log. Essendo lei l’oggetto di analisi per questo lavoro verranno anche riportati quali sono i flussi di informazioni che essa genera. • Capitolo 2: verranno riportati degli screenshoot dei dati in mio possesso al fine di, tramite un’analisi esplorativa, interpretarli e produrre una formulazione di idee/proposte applicabili agli algoritmi di Machine Learning noti in letteratura. • Capitolo 3 (identificazione di errore): in questo capitolo vengono riportati i contesti applicativi da me progettati al fine di implementare una infrastruttura che possa soddisfare il requisito, titolo di questo capitolo. • Capitolo 4 (caratterizzazione della macchina): definirò l’algoritmo utilizzato, FP-Growth, e mostrerò le modifiche effettuate al fine di poterlo impiegare all’interno di macchine automatiche rispettando i limiti stringenti di: tempo di cpu, memoria, operazioni di I/O e soprattutto la non possibilità di aver a disposizione l’intero dataset ma solamente delle sottoporzioni. Inoltre verranno generati dei DataSet per il testing di dell’algoritmo FP-Growth modificato.