Adaptive Non-linear Pattern Matching Automata

Efficient pattern matching is fundamental for practical term rewrite engines. By preprocessing the given patterns into a finite deterministic automaton the matching patterns can be decided in a single traversal of the relevant parts of the input term. Most automaton-based techniques are restricted to linear patterns, where each variable occurs at most once, and require an additional post-processing step to check so-called variable consistency. However, we can show that interleaving the variable consistency and pattern matching phases can reduce the number of required steps to find all matches. Therefore, we take the existing adaptive pattern matching automata as introduced by Sekar et al and extend these with consistency checks. We prove that the resulting deterministic pattern matching automaton is correct, and show several examples where some reduction can be achieved.

Steered Beam Adaptive Antenna Arrays

In this chapter, the performance of steered beam adaptive arrays is presented with its corresponding analytical expressions. Computer simulations are used to illustrate the performance of the array under various operating conditions. In this chapter, we ignore the presence of mutual coupling between the array elements. The principal system elements of the adaptive array consist of an array of sensors (antennas), a pattern-forming network, and an adaptive pattern control unit or adaptive processor that adjusts the variable weights in the pattern-forming network. The adaptive pattern control unit may furthermore be conveniently subdivided into a signal processor unit and an adaptive control algorithm. The manner in which these elements are actually implemented depends on the propagation medium in which the array is to operate, the frequency spectrum of interest, and the user’s knowledge of the operational signal environment.

How coaches’ need-supportive and controlling behaviors are related to different (mal)adaptive outcomes in water polo players: a person-centered approach

The relationship between both coaches’ need-supportive and controlling behaviors and different athletes’ motivational outcomes has been previously examined. However, little is known about the coexistence of coaches’ need-supportive and controlling behaviors in the sports context and even less, about what coach’s motivating style configuration may yield the most and the least adaptive pattern of outcomes in relation to athletes’ motivating experiences. Grounded in self-determination theory (SDT), this study aimed to identify coach motivating style groups based on athletes’ perceptions of need-supportive and four controlling behaviors (i.e., controlling use of rewards, negative conditional regard, intimidation, and personal control), and to examine their differences in terms of athletes’ motivational outcomes and sport commitment. Using a sample of 658 young water polo players (Mage = 14.76, SD = 1.36), results revealed five distinct coach motivating style groups. A coexistence of need-supportive and controlling use of rewards was identified among athletes in two groups. The “very low support-high control” group yielded the most maladaptive outcomes, while the “high support-low control” group was the most optimal style, even when compared to coaches that combined high need-supportive and controlling practices. This study provides deeper insights on how athletes may perceive simultaneously coach’s need-supportive and controlling behaviors, and how some controlling practices imply a higher motivational cost among athletes.

A novel automated tower graph based ECG signal classification method with hexadecimal local adaptive binary pattern and deep learning

Electrocardiography (ECG) signal recognition is one of the popular research topics for machine learning. In this paper, a novel transformation called tower graph transformation is proposed to classify ECG signals with high accuracy rates. It employs a tower graph, which uses minimum, maximum and average pooling methods altogether to generate novel signals for the feature extraction. In order to extract meaningful features, we presented a novel one-dimensional hexadecimal pattern. To select distinctive and informative features, an iterative ReliefF and Neighborhood Component Analysis (NCA) based feature selection is utilized. By using these methods, a novel ECG signal classification approach is presented. In the preprocessing phase, tower graph-based pooling transformation is applied to each signal. The proposed one-dimensional hexadecimal adaptive pattern extracts 1536 features from each node of the tower graph. The extracted features are fused and 15,360 features are obtained and the most discriminative 142 features are selected by the ReliefF and iterative NCA (RFINCA) feature selection approach. These selected features are used as an input to the artificial neural network and deep neural network and 95.70% and 97.10% classification accuracy was obtained respectively. These results demonstrated the success of the proposed tower graph-based method.

A Systematic Compact Model Parameter Calibration with Adaptive Pattern Search Algorithm

A systematic device-model calibration (extraction) methodology has been proposed to reduce parameter calibration time of advanced compact model for modern nano-scale semiconductor devices. The adaptive pattern search algorithm is a variant of the direct search method, which explore in the parameter space with adaptive searching step and direction. It is very straightforward, but powerful, in high dimensional optimization problem since adaptive step and direction are decided by simple computation. The proposed method iterates less but shows superior accuracy over the conventional method. It is possible to be applied to a behavioral or empirical model correspond to emerging devices, such as tunneling field-effect transistor (TFET) and negative capacitance field-effect transistor (NCFET) due to its universality in parameter calibration for the model accuracy.

PD-L1 expression in angiomatoid fibrous histiocytoma

Angiomatoid fibrous histiocytoma (AFH) is a rare tumor of intermediate malignancy. Treatment options for unresectable and/or metastatic tumors are very limited. Immunotherapy with PD-1/PD-L1 inhibitors may be worth exploring. The aim of this study was to evaluate the expression of PD-L1 in AFHs. PD-L1 expression was assessed on 36 AFHs from 36 pediatric patients by immunohistochemical staining of PD-L1 (clone 22C3). Positivity was defined as membranous expression in ≥ 1% of either tumor or immune cells. The correlations between PD-L1 expression and clinicopathologic features were assessed. Two patients had lymph node metastasis. All patients underwent surgical resection; three of them also had systemic chemotherapy. Three patients had recurrence after initial resection; all patients were alive with a median follow-up of 2.5 years. Overall, twenty-two (61%) tumors were positively stained for PD-L1 and positivity was seen on both tumor and immune cells in eighteen of the 22 tumors. A positive correlation was found between tumor cell PD-L1 expression and CD8+ T-cell infiltration. There were no statistically significant differences between the status of PD-L1 expression and the clinicopathological features assessed. PD-L1 expression was identified in 61% of AFHs with a predominantly adaptive pattern. Our findings provide a rationale for future studies evaluating the potential of checkpoint immunotherapy for patients with unresectable and/or metastatic tumor.

Adaptive Camouflage for Moving Objects

Targets that are well camouflaged under static conditions are often easily detected as soon as they start moving. We investigated and evaluated ways to design camouflage that dynamically adapts to the background and conceals the target while taking the variation in potential viewing directions into account. In a human observer experiment, recorded imagery was used to simulate moving (either walking or running) and static soldiers, equipped with different types of camouflage patterns and viewed from different directions. Participants were instructed to detect the soldier and to make a rapid response as soon as they have identified the soldier. Mean target detection rate was compared between soldiers in standard (Netherlands) Woodland uniform, in static camouflage (adapted to the local background) and in dynamically adapting camouflage. We investigated the effects of background type and variability on detection performance by varying the soldiers’ environment (such as bushland and urban). In general, detection was easier for dynamic soldiers compared to static soldiers, confirming that motion breaks camouflage. Interestingly, we show that motion onset and not motion itself is an important feature for capturing attention. Furthermore, camouflage performance of the static adaptive pattern was generally much better than for the standard Woodland pattern. Also, camouflage performance was found to be dependent on the background and the local structures around the soldier. Interestingly, our dynamic camouflage design outperformed a method which simply displays the ‘exact’ background on the camouflage suit (as if it was transparent), since it is better capable of taking the variability in viewing directions into account. By combining new adaptive camouflage technologies with dynamic adaptive camouflage designs such as the one presented here, it may become feasible to prevent detection of moving targets in the (near) future.

Emulation of Biphasic Plasticity in Retinal Electrical Synapse for Light-Adaptive Pattern Pre-Processing

Electrical synapse provides rapid, bidirectional communication in nervous systems, accomplishing tasks distinct from and complementary to chemical synapses. Here, we demonstrate an artificial electrical synapse based on high-order conductance transition...