ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.British Petroleum (98-A-1204); Defense Advanced Research Projects Agency (90-0083, 90-0175, 90-0128); National Science Foundation (IRI-90-00539); Army Research Office (DAAL-03-88-K0088

Fuzzy ARTMAP, Slow Learning and Probability Estimation

A nonparametric probability estimation procedure using the fuzzy ARTMAP neural network is here described. Because the procedure does not make a priori assumptions about underlying probability distributions, it yields accurate estimates on a wide variety of prediction tasks. Fuzzy ARTMAP is used to perform probability estimation in two different modes. In a 'slow-learning' mode, input-output associations change slowly, with the strength of each association computing a conditional probability estimate. In 'max-nodes' mode, a fixed number of categories are coded during an initial fast learning interval, and weights are then tuned by slow learning. Simulations illustrate system performance on tasks in which various numbers of clusters in the set of input vectors mapped to a given class.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (AFOSR-90-0083, ONR-N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-1075

Energetic Impact of Jet Inflated Cocoons in Relaxed Galaxy Clusters

Jets from active galactic nuclei (AGN) in the cores of galaxy clusters have the potential to be a major contributor to the energy budget of the intracluster medium (ICM). To study the dependence of the interaction between the AGN jets and the ICM on the parameters of the jets themselves, we present a parameter survey of two-dimensional (axisymmetric) ideal hydrodynamic models of back-to-back jets injected into a cluster atmosphere (with varying Mach numbers and kinetic luminosities). We follow the passive evolution of the resulting structures for several times longer than the active lifetime of the jet. The simulations fall into roughly two classes, cocoon-bounded and non-cocoon bounded sources. We suggest a correspondence between these two classes and the Faranoff-Riley types. We find that the cocoon-bounded sources inject significantly more entropy into the core regions of the ICM atmosphere, even though the efficiency with which energy is thermalized is independent of the morphological class. In all cases, a large fraction (50--80%) of the energy injected by the jet ends up as gravitational potential energy due to the expansion of the atmosphere.Comment: 12 pages, Accepted for publication in Ap

MicroRNA-330-5p as a putative modulator of neoadjuvant chemoradiotherapy sensitivity in oesophageal adenocarcinoma

Oesophageal adenocarcinoma (OAC) is the sixth most common cause of cancer deaths worldwide, and the 5-year survival rate for patients diagnosed with the disease is approximately 17%. The standard of care for locally advanced disease is neoadjuvant chemotherapy or, more commonly, combined neoadjuvant chemoradiation therapy (neo-CRT) prior to surgery. Unfortunately, ~60-70% of patients will fail to respond to neo-CRT. Therefore, the identification of biomarkers indicative of patient response to treatment has significant clinical implications in the stratification of patient treatment. Furthermore, understanding the molecular mechanisms underpinning tumour response and resistance to neo-CRT will contribute towards the identification of novel therapeutic targets for enhancing OAC sensitivity to CRT. MicroRNAs (miRNA/miR) function to regulate gene and protein expression and play a causal role in cancer development and progression. MiRNAs have also been identified as modulators of key cellular pathways associated with resistance to CRT. Here, to identify miRNAs associated with resistance to CRT, pre-treatment diagnostic biopsy specimens from patients with OAC were analysed using miRNA-profiling arrays. In pre-treatment biopsies miR-330-5p was the most downregulated miRNA in patients who subsequently failed to respond to neo-CRT. The role of miR-330 as a potential modulator of tumour response and sensitivity to CRT in OAC was further investigated in vitro. Through vector-based overexpression the E2F1/p-AKT survival pathway, as previously described, was confirmed as a target of miR-330 regulation. However, miR-330-mediated alterations to the E2F1/p-AKT pathway were insufficient to significantly alter cellular sensitivity to chemotherapy (cisplatin and 5-flurouracil). In contrast, silencing of miR-330-5p enhanced, albeit subtly, cellular resistance to clinically relevant doses of radiation. This study highlights the need for further investigation into the potential of miR-330-5p as a predictive biomarker of patient sensitivity to neo-CRT and as a novel therapeutic target for manipulating cellular sensitivity to neo-CRT in patients with OAC

Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps

A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors. The architecture, called Fuzzy ARTMAP, achieves a synthesis of fuzzy logic and Adaptive Resonance Theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Fuzzy ARTMAP also realizes a new Minimax Learning Rule that conjointly minimizes predictive error and maximizes code compression, or generalization. This is achieved by a match tracking process that increases the ART vigilance parameter by the minimum amount needed to correct a predictive error. As a result, the system automatically learns a minimal number of recognition categories, or "hidden units", to met accuracy criteria. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy logic play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Improved prediction is achieved by training the system several times using different orderings of the input set. This voting strategy can also be used to assign probability estimates to competing predictions given small, noisy, or incomplete training sets. Four classes of simulations illustrate Fuzzy ARTMAP performance as compared to benchmark back propagation and genetic algorithm systems. These simulations include (i) finding points inside vs. outside a circle; (ii) learning to tell two spirals apart; (iii) incremental approximation of a piecewise continuous function; and (iv) a letter recognition database. The Fuzzy ARTMAP system is also compared to Salzberg's NGE system and to Simpson's FMMC system.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI 90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-0175

Impact of different handling styles (good vs. adverse) on growth performance, behavior, and cortisol concentrations in beef cattle

Our objective was to determine effects of aggressive handling on growth performance, behavior, and cortisol concentrations in beef calves. Crossbred calves (313 ± 4.7 kg; n = 54; 24 steers, 30 heifers) from a single herd were stratified by gender, body weight, and initial chute score, then allocated randomly to one of six pens. Each pen was randomly assigned to one of two handling treatments (good or adverse) applied on days 7, 35, 63, and 91. The objective of good treatment was to handle the calves quietly and gently to minimize stress. The objective of adverse treatment was to move the calves quickly and expose them to stimuli. Body weight, exit velocity, and chute scores (based on 5 point subjective scale) were recorded and salivary samples for cortisol were collected (4 calves/ pen) on days 0, 7, 35, 63, and 91. Pen scores (5 point subjective scale) were recorded on days 12, 42, and 87. Data were analyzed statistically using a mixed model. Chute scores tended to be higher (more agitated) in the adverse treatment on day 7, but scores did not differ on subsequent days (treatment × day; P = 0.06). Salivary cortisol concentrations on day 63 were greater in cattle on the adverse treatment (treatment × day, P = 0.001). Body weight, exit velocity, and pen scores were not affected by treatment (P ≥ 0.24). While differences were observed, these cattle appeared to acclimate to short-term adverse handling which did not seem to dramatically affect performance or behavior of beef cattle

A Due Process Right to Record the Police

Do citizens have a right to record the actions of law enforcement officers? This topic has been the subject of considerable discussion, and no small degree of litigation, in recent years. The increase in litigation is driven by dramatic improvements in camera technology, which allow individuals to record and share images in ways that were previously available, if at all, only to members of large media organizations. Most of the discussion and litigation has revolved around the question of whether there is a First Amendment right to record police officers in public. In the recent First Circuit case of Glik v. Cunniffe, for example, passerby Simon Glik caught sight of three police officers arresting a young man. Hearing a passerby shout that the officers were hurting the man, Glik turned on his cell phone and began capturing video. The police officers objected to being recorded, arrested Glik and charged him with violating the state’s “wiretap” law by recording them without their consent, and seized his camera and memory chip in the process as evidence. The First Circuit held that the right to record police officers in public is a “clearly established” part of the First Amendment’s protections, and held the officers were not entitled to qualified immunity. Though the issue has not yet reached the Supreme Court, it seems safe to say that the case for First Amendment protection regarding photos and video of law enforcement officers in public is quite strong, and is in the process of being resolved. This Article, however, argues that independent of any First Amendment right, there is also a due process right to record the actions of law enforcement, and that this right applies even when the interaction takes place in private, and not in public places. This question of a due process right to record the police has not yet produced the degree of attention and litigation that public recording has, but the growth of inexpensive recording equipment and its inclusion in smart phones ensures that such attention and litigation are sure to be forthcoming

Three dimensional reconstructed MRI of an acrylic meniscal cartilage phantom: the effect of acquisition slice thickness upon accuracy of volume measurement

AUSTRALI

RIDS for Red Books from the Spacecraft Monitoring and Control Working Group of the CCSDS

This document presents a listing of the Review Item Dispositions (RIDs) to be presented to the The Consultative Committee for Space Data Systems (CCSDS)