The control properties of discrete-time switched linear systems (SLSs) with switching signals generated by logical dynamical systems are studied using the semitensor product (STP) approach. With the algebraic state-space representation (ASSR), the linear modes and the logical generators are aggregated as a system with hybrid states, leading to the criteria of reachability, controllability, observability, and reconstructibility of the SLSs. Algorithms for checking these properties are given. Then, two kinds of realization problems concerning whether the logical dynamical systems can generate the desired switching signals are investigated, and necessary and sufficient conditions for the realizability of the desired switching signals are given with respect to the cases of fixed operating time (FOT) switching and finite reference signal switching.

Automatic speech recognition (ASR) techniques have become powerful tools, enhancing efficiency in law enforcement scenarios. To ensure fairness for demographic groups in different acoustic environments, ASR engines must be tested across a variety of speakers in realistic settings. However, describing the fairness discrepancies between models with confidence remains a challenge. Meanwhile, most public ASR datasets are insufficient to perform a satisfying fairness evaluation. To address the limitations, we built FairLENS - a systematic fairness evaluation framework. We propose a novel and adaptable evaluation method to examine the fairness disparity between different models. We also collected a fairness evaluation dataset covering multiple scenarios and demographic dimensions. Leveraging this framework, we conducted fairness assessments on one open-source and eleven commercially available state-of-the-art ASR models. Our results reveal that certain models exhibit more biases than others, serving as a fairness guideline for users to make informed choices when selecting ASR models for a given real-world scenario. We further explored model biases towards specific demographic groups and observed that shifts in the acoustic domain can lead to the emergence of new biases.

It is a common belief that the order of a Boolean network is mainly determined by its attractors, including fixed points and cycles. Using the semi-tensor product (STP) of matrices and the algebraic state-space representation (ASSR) of the Boolean networks, this article reveals that in addition to this explicit order, there is a certain implicit or hidden order, which is determined by the fixed points and limit cycles of their dual networks. The structure and certain properties of dual networks are investigated. Instead of a trajectory, which describes the evolution of a state, the hidden order provides a global horizon to describe the evolution of the overall network. We conjecture that the order of networks is mainly determined by the dual attractors via their corresponding hidden orders. Then these results about the Boolean networks are further extended to the k -valued case.

In the primate's primary visual cortex (V1), cells are classified in terms of two categories: simple cells and complex cells, given their response properties. While simple cells respond strongly to gating and bar stimuli at a certain phase and location, responses of complex cells are insensitive to small translation of stimulus within the receptive field [1]. Inspired by the response properties of simple and complex cells in the primary visual cortex, we propose a computational network to learn the receptive fields of these cells, and address the development of translation invariance from a temporal sequence of natural images. A generative model with sparseness constraints is devised to minimize the energy of prediction errors. Each simple cell is modulated by a higher layer of complex cells in a multiplicative fashion, where a slowness property and a trace-like rule are enforced on complex cells, as the result of a temporal coherence soft constraint. Furthermore, non-negativity constraints of the latent cell variables and weight matrices are imposed to fit the known neurophysiology. We present an online gradient descent algorithm to train our model from natural image sequences, in which a pre-training strategy is used to initialize the weights. The developed connection weights show that complex cell outputs are directly proportional to quadratic forms of simple cell responses. Each receptive field of simple cells develop a Gabor-like orientation filter, and each complex cell pools similar simple cell receptive fields - in retinotopic and feature space - producing the locally-invariant representation.

Learning sparse representations for deep networks has drawn considerable research interest in recent years. In this paper, we present a novel framework to learn sparse representations via a generalized encoder-decoder architecture. The basic idea is to adopt a fast approximation to the iterative sparse coding solution and form an efficient nonlinear encoder to map an input to a sparse representation. A set of basis functions is then learned through the minimization of an energy function consisting of a sparseness prior and linear decoder constraints. Applying a greedy layer-wise learning scheme, this framework can be extended to more layers to learn deep networks. The proposed learning algorithm is also highly efficient as no iterative operations are required, and both batch and on-line learning are supported. Given the sparse representation and basis functions, an optimized decoding procedure is carried out to reconstruct and denoise the input signals. We evaluate our model on natural image patches to develop a dictionary of V1-like Gabor filters, and further show that basis functions in a higher layer (e.g., V2) combine the filters in a lower layer to generate more complex patterns to benefit the high-level tasks. We then use the sparse representations to recognize objects in two benchmark data sets (i.e., CIFAR-10 and NORB) via a linear SVM classifier, and demonstrate better or comparable recognition performances with respect to state-of-art algorithms. The image reconstruction of MNIST images and the restoration of corrupted versions are presented at the end.

Conventional sparse coding learns optimal dictionaries of feature bases to approximate input signals; however, it is not favorable to classify the inputs. Recent research has focused on building discriminative sparse coding models to facilitate the classification tasks. In this paper, we develop a new discriminative sparse coding model via bidirectional flows. Sensory inputs (from bottom-up) and discriminative signals (supervised from top-down) are propagated through a hierarchical network to form sparse representations at each level. The ℓ0-constrained sparse coding model allows highly efficient online learning and does not require iterative steps to reach a fixed point of the sparse representation. The introduction of discriminative top-down information flows helps to group reconstructive features belonging to the same class and thus to benefit the classification tasks. Experiments are conducted on multiple data sets including natural images, hand-written digits and 3-D objects with favorable results. Compared with unsupervised sparse coding via only bottom-up directions, the two-way discriminative approach improves the recognition performance significantly.

In this paper, we propose an object learning system that incorporates sensory information from an automotive radar system and a video camera. The radar system provides coarse attention for the focus of visual analysis on relatively small areas within the image plane. The attended visual areas are coded and learned by a three-layer neural network utilizing what is called in-place learning: Each neuron is responsible for the learning of its own processing characteristics within the connected network environment, through inhibitory and excitatory connections with other neurons. The modeled bottom-up, lateral, and top-down connections in the network enable sensory sparse coding, unsupervised learning, and supervised learning to occur concurrently. This paper is applied to learn two types of encountered objects in multiple outdoor driving settings. Cross-validation results show that the overall recognition accuracy is above 95% for the radar-attended window images. In comparison with the uncoded representation and purely unsupervised learning (without top-down connection), the proposed network improves the overall recognition rate by 15.93% and 6.35%, respectively. The proposed system is also compared favorably with other learning algorithms. The result indicates that our learning system is the only one that is fit for incremental and online object learning in a real-time driving environment.

Attention and recognition have been addressed separately as two challenging computational vision problems, but an engineering-grade solution to their integration and interaction is still open. Inspired by the brain's dorsal and ventral pathways in cortical visual processing, we present a neuromorphic architecture, called Where-What Network 2 (WWN-2), to integrate object attention and recognition interactively through their experience-based development. This architecture enables three types of attention: feature-based bottom-up attention, position-based top-down attention, and object-based top-down attention, as three possible information flows through the Y-shaped network. The learning mechanism of the network is rooted in a simple but efficient cell-centered synaptic update model, entailing the dual optimization of Hebbian directions and cell firing-age dependent step sizes. The inputs to the network are a sequence of images, where specific foreground objects may appear anywhere within an unknown, complex, natural background. The WWN-2 regulates the network to dynamically establish and consolidate position-specified and type-specified representations through a supervised learning mode. The network has reached 92.5% object recognition rate and an average of 1.5 pixels in position error after 20 epochs of training.

A pathway in the central nervous system (CNS) is a path through which nervous signals are processed in an orderly fashion. A sensorimotor pathway starts from a sensory input and ends at a motor output, although almost all pathways are not simply unidirectional. In this paper, we introduce a simple, biologically inspired, unified computational model - Multi-layer In-place Learning Network (MILN), with a design goal to develop a recurrent network, as a function of sensorimotor signals, for open-ended learning of multiple sensorimotor tasks. The biologically motivated MILN provides automatic feature derivation and pathway refinement from the temporally real-time inputs. The work presented here is applied in the challenging application field of developing reactive behaviors from a video camera and a (noisy) radar range sensor for a vehicle-based robot in open, natural driving environments. An internal model of the agent’s experience of the environments is created and refined from the ground-up using a cell-centered model, based on the genomic equivalence principle. The outputs can be imposed by a teacher, at the same time as the learning is active. At any time instant, sensory information from the radar allows the system to focus its visual analysis on relatively small areas within the image plane (attention selection), in a computationally efficient way, suitable for real-time training. This system was trained with data from 10 different city and highway road environments, and cross validation shows that MILN was able to correctly recognize above 95% of the radar-extracted images from the multiple environments. The in-place learning mechanism compares with other learning algorithms favorably, as results of a comparison indicate that in-place learning is the only one to fit all the specified criteria of development of a general-purpose sensorimotor pathway.

In this paper, we propose a model to develop robotspsila covert and overt behaviors by using reinforcement and supervised learning jointly. The covert behaviors are handled by a motivational system, which is achieved through reinforcement learning. The overt behaviors are directly selected by imposing supervised signals. Instead of dealing with problems in controlled environments with a low-dimensional state space, our model is applied for the learning in non-stationary environments. Locally balanced incremental hierarchical discriminant regression (LBIHDR) tree is introduce to be the engine of cognitive mapping. Its balanced coarse-to-fine tree structure guarantees real-time retrieval in self-generated high-dimensional state space. Furthermore, K-nearest neighbor strategy is adopted to reduce training time complexity. Vision-based outdoor navigation are used as challenging task examples. In the experiment, the mean square error of heading direction is 0deg for re-substitution test and 1.1269deg for disjoint test, which allows the robot to drive without a big deviation from the correct path we expected. Compared with IHDR (W.S. Hwang and J. Weng, 2007), LBIHDR reduced the mean square error by 0.252deg and 0.5052deg, using re-substitution and disjoint test, respectively.