nils-herrmann/clean_scirepeval huggingface dataset:
Active vision using an analog VLSI model of selective attention

Detailed processing of sensory information is a computationally demanding task. This is especially true for vision, where the amount of information provided by the sensors typically exceeds the processing capacity of the system. Rather than attempting to process all the sensory data simultaneously, an effective strategy is to focus on subregions of the input space, shifting from one, subregion to the other, in a serial fashion. This strategy is commonly referred to as selective attention. We present a neuromorphic active-vision system, that implements a saliency-based model of selective attention. Visual data is sensed and preprocessed in parallel by a transient imager chip and transmitted to a selective-attention chip. This chip sequentially selects the spatial locations of salient regions in the vision sensor's field of view. A host computer uses the output of the selective-attention chip to drive the motors on which the imager is mounted, and to orient it toward the selected regions. The system's design framework is modular and allows the integration of multiple sensors and multiple selective-attention chips. We present experimental results showing the performance of a two-chip system in response to well-controlled test stimuli and to natural stimuli.
A neuromorphic VLSI device for implementing 2D selective attention systems

Selective attention is a mechanism used to sequentially select and process salient subregions of the input space, while suppressing inputs arriving from nonsalient regions. By processing small amounts of sensory information in a serial fashion, rather than attempting to process all the sensory data in parallel, this mechanism overcomes the problem of flooding limited processing capacity systems with sensory inputs. It is found in many biological systems and can be a useful engineering tool for developing artificial systems that need to process in real-time sensory data. In this paper we present a neuromorphic hardware model of a selective attention mechanism implemented on a very large scale integration (VLSI) chip, using analog circuits. The chip makes use of a spike-based representation for receiving input signals, transmitting output signals and for shifting the selection of the attended input stimulus over time. It can be interfaced to neuromorphic sensors and actuators, for implementing multichip selective attention systems. We describe the characteristics of the circuits used in the architecture and present experimental data measured from the system.
A Current-Mode Hysteretic Winner-take-all Network, with Excitatory and Inhibitory Coupling

Winner-take-all (WTA) circuits are commonly used in a wide variety of applications. One of the most used current-mode WTA designs is the one originally proposed by Lazzaro et al. . Several extensions to this design have been suggested in the past. In this paper we present a variant of this current-mode WTA circuit, containing all of the enhancements previously proposed, together with new additional modifications that endow it with interesting hysteretic and lateral inhibition and excitation properties. We compare the performance of this WTA circuit to the original WTA design, providing experimental data obtained from a VLSI chip containing both types of circuits, designed using closely matched layouts. We derive analytically the response properties of the circuit's lateral diffusion network, pointing out the differences to previously proposed diffusion networks, and present experimental data confirming the theoretical predictions. We also describe application domains that can best exploit these types of hysteretic WTA circuits.
Modeling Selective Attention Using a Neuromorphic Analog VLSI Device

Attentional mechanisms are required to overcome the problem of flooding a limited processing capacity system with information. They are present in biological sensory systems and can be a useful engineering tool for artificial visual systems. In this article we present a hardware model of a selective attention mechanism implemented on a very large-scale integration (VLSI) chip, using analog neuromorphic circuits. The chip exploits a spike-based representation to receive, process, and transmit signals. It can be used as a transceiver module for building multichip neuromorphic vision systems. We describe the circuits that carry out the main processing stages of the selective attention mechanism and provide experimental data for each circuit. We demonstrate the expected behavior of the model at the system level by stimulating the chip with both artificially generated control signals and signals obtained from a saliency map, computed from an image containing several salient features.
A 2D neuromorphic VLSI architecture for modeling selective attention

Selective attention is a mechanism used to sequentially select the spatial locations of salient regions in the sensor's field of view. This mechanism overcomes the problem of flooding limited processing capacity systems with sensory information. It is found in many biological sensory systems and can be a useful engineering tool for artificial visual systems. We present a hardware model of a selective attention mechanism implemented on a VLSI chip, using analog neuromorphic circuits. The chip makes use of a spike based representation for receiving input signals, transmitting output signals and for shifting the selection of the attended input stimulus over time. The chip can be interfaced to neuromorphic sensors and actuators, for implementing multi-chip selective attention systems. We describe the characteristics of the circuits used in the architecture, and present experimental data measured from the system.
Neuromorphic Vision Sensors

Vision is one of the most useful sensory functions, but the real-time processing of the continuous, high-dimensional input signals provided by vision sensors is a major challenge in robot design. Conventional digital vision sensors tend to have excessive power consumption, size, and cost for useful applications. In this TechView, Indiveri and Douglas discuss an alternative strategy, namely neuromorphic vision sensors that are based on biological vision systems. In these sensors, specialized sensory processing functions inspired by biological systems such as fly eyes are integrated in parallel, asynchronous circuits that respond in real time. These sensors offer significant advantages over conventional vision sensors.
Neuromorphic analog VLSI sensor for visual tracking: circuits and application examples

This paper presents a one-dimensional visual sensor, implemented on a single VLSI chip using analog neuromorphic circuits, for selectively detecting and tracking the position of the feature with the highest spatial contrast present in the visual scene. The chip's photoreceptors adapt to stationary backgrounds and can be tuned to respond maximally to specific target velocities. The sensor drastically reduces the amount of data to be transmitted to further processing stages by encoding, in real time, the position of the target in the form of a single continuous-time analog variable. We describe the circuits implementing the sensor and show applications to three examples of tracking tasks: a stand-alone visual tracking system, an active fully analog tracking system, and a mobile platform line-following system.
A reconfigurable neuromorphic VLSI multi-chip system applied to visual motion computation

We present a multi-chip neuromorphic system in which an address event representation is used for inter-chip communication. The system comprises an analog VLSI transient imager with adaptive photoreceptors, an analog VLSI motion receiver chip and a prototyping communication infrastructure which allows for programmability of connections between the elements on the two chips. We describe the properties of the two VLSI chips and of the communication infrastructure. To characterize the whole system, we present examples of connectivity tables which allow it to compute translational motion and expanding motion and show data from the transient detector array and motion receiver chips.
Neuromorphic vision sensors and preprocessors in system applications

A partial review of neuromorphic vision sensors that are suitable for use in autonomous systems is presented. Interfaces are being developed to multiplex the high- dimensional output signals of arrays of such sensors and to communicate them in standard formats to off-chip devices for higher-level processing, actuation, storage and display. Alternatively, on-chip processing stages may be implemented to extract sparse image parameters, thereby obviating the need for multiplexing. Autonomous robots are used to test neuromorphic vision chips in real-world environments and to explore the possibilities of data fusion from different sensing modalities. Examples of autonomous mobile systems that use neuromorphic vision chips for line tracking and optical flow matching are described.
Analog VLSI Model of Locust DCMD Neuron Response for Computation of Object Approach

In this paper, we present an analog VLSI (aVLSI) architecture that implements a functional model of the locust descending contralateral movement detector (DCMD) neuron, based on recently published results. We describe its architecture and analog circuits that implement its critical computational modules. We also provide an analytical closed-form solution for some of the circuits proposed and verify the validity of our analysis both through circuit simulations and experimental data obtained from an aVLSI test chip. This work shows that the circuits here proposed can be used to build hardware devices to model DCMD response curves. Having complete control over the hardware system, neurophysiologists would then be able to measure in real time properties of looming stimuli and outcome of the neuromorphic model, while simultaneously performing the experiment on the real biological system.
Autonomous Vehicle Guidance Using Analog VLSI Neuromorphic Sensors

Analog VLSI circuits implementing aspects of biological systems are attractive for the construction of compact low-power autonomous systems. We describe such a system, consisting of a mobile robot equipped with a neuromorphic sensor implementing a one dimensional silicon retina. Specifically, we demonstrate how the real-time visual pre-processing capabilities of the neuromorphic sensor are instrumental in enabling the system to reliably and autonomously track a continuous edge. We present data both from the neuromorphic sensor and from the overall system, performing a line tracking task.
Winner-Take-All Networks with Lateral Excitation

In this paper we present two analog VLSI circuits thatimplement current mode winner-take-all (WTA) networks with lateralexcitation. We describe their principles of operation and comparetheir performance to previously proposed circuits. The desirableproperties of these circuits, namely compactness, low power consumption,collective processing and robustness to noisy inputs make themideal for system level integration in analog VLSI neuromorphicsystems. As application example, we implemented a circuit thatemploys an adaptive photoreceptor array as the input stage tothe WTA network for edge enhancement.
Analog VLSI motion projects at Caltech

We present different compact analog VLSI motion sensors that compute the 1-D velocity of optical stimuli over a large range and are suitable for integration in focal plane arrays. They have been extensively tested and optimized for robust performance under varying light conditions. Since their output signals are only weakly dependent on contrast, they directly extract optical flow data from an image. Focal plane arrays of such sensors are particularly interesting for application in single-chip systems that perform navigation tasks for moving robots or vehicles, where light weight, low power consumption, and real-time processing are crucial. Several monolithic motion-processing systems based on such velocity sensors have been built and tested. We describe here three chips, designed for the determination of the focus of expansion, the estimation of the time to contact, and the detection of motion discontinuities respectively. The first two systems have been specifically designed for vehicle navigation tasks. The choice of this application domain allows us to make a priori assumptions about the optical flow field that simplifies the structure of the systems and improves their overall performance. The motion-discontinuity-detection system can be more generally used to segment images based on the velocities of its different domains with respect to the camera. It is particularly useful for background-foreground segregation in the case of ego-motion of an autonomous system in a static environment. Tests results of the three systems are presented and their performance is evaluated.
Neuromorphic Vision Chips: intelligent sensors for industrial applications

In the past years researchers at the California Institute of Technology (and in few other research institutions worldwide) have been concentrating their efforts on the design and implementation of analog VLSI neuromorphic systems. Today this emerging technology seems to be mature enough for use in industrial applications. In this paper we give an overview of some of the neuromorphic vision circuits that have been developed at the California Institute of Technology. To point out the advantages and disadvantages of using these types of circuits in industrial applications (with emphasis on the automotive market), we describe a system implemented on a single chip, that detects the direction of heading in the case of translatory ego-motion in a stationary environment. This device contains a variety of analog circuits typically used in neuromorphic systems. We show simulation results applied to vehicle navigation and test data obtained using artificial stimuli in a laboratory environment.
Analog VLSI architectures for motion processing: from fundamental limits to system applications

This paper discusses some of the fundamental issues in the design of highly parallel, dense, low-power motion sensors in analog VLSI. Since photoreceptor circuits are an integral part of all visual motion sensors, we discuss how the sizing of photosensitive areas can affect the performance of such systems. We review the classic gradient and correlation algorithms and give a survey of analog motion-sensing architectures inspired by them. We calculate how the measurable speed range scales with signal-to-noise ratio (SNR) for a classic Reichardt sensor with a fixed time constant. We show how this speed range may be improved using a nonlinear filter with an adaptive time constant, constructed out of a diode and a capacitor, and present data from a velocity sensor based on such a filter. Finally, we describe how arrays of such velocity sensors call be employed to compute the heading direction of a moving subject and to estimate the time-to-contact between the sensor and a moving object.
Analog VLSI Architecture for Computing Heading Direction

| We describe a parallel analog VLSI architecture that integrates optical ow data obtained from arrays of elementary velocity sensors to estimate the direction of heading for pure translational motion sequences. We performed software simulations at di erent levels of abstraction to evaluate the most important qualitative properties of the optical ow eld, determine the best functional operators for the implementation of the architecture and de ne the numerical values of some of the free parameters present in the hardware implementation. Furthermorewe describe the properties of the circuits used in the hardware implementation and the functionality of the elementary motion sensors used to extract the optical ow eld. Measurements from the chips developed and software simulation results are provided to show the validity of the fol-
Analog VLSI architecture for computing heading direction

We describe a parallel analog VLSI architecture that integrates optical flow data obtained from arrays of elementary velocity sensors to estimate the direction of heading for pure translational motion sequences. We performed software simulations at different levels of abstraction to evaluate the most important qualitative properties of the optical flow field, determine the best functional operators for the implementation of the architecture and define the numerical values of some of the free parameters present in the hardware implementation. Furthermore we describe the properties of the circuits used in the hardware implementation and the functionality of the elementary motion sensors used to extract the optical flow field. Measurements from the chips developed and software simulation results are provided to show the validity of the followed approach.
Parallel analog VLSI architectures for computation of heading direction and time-to-contact

We describe two parallel analog VLSI architectures that integrate optical flow data obtained from arrays of elementary velocity sensors to estimate heading direction and time-to-contact. For heading direction computation, we performed simulations to evaluate the most important qualitative properties of the optical flow field and determine the best functional operators for the implementation of the architecture. For time-to-contact we exploited the divergence theorem to integrate data from all velocity sensors present in the architecture and average out possible errors.
The Markov Assumption in Spoken Dialogue Management

The goal of dialogue management in a spoken dialogue system is to take actions based on observations and inferred beliefs. To ensure that the actions optimize the performance or robustness of the system, researchers have turned to reinforcement learning methods to learn policies for action selection. To derive an optimal policy from data, the dynamics of the system is often represented as a Markov Decision Process (MDP), which assumes that the state of the dialogue depends only on the previous state and action. In this paper, we investigate whether constraining the state space by the Markov assumption, especially when the structure of the state space may be unknown, truly affords the highest reward. In a simulation experiment conducted in the context of a dialogue system for interacting with a speech-enabled web browser, models under the Markov assumption did not perform as well as an alternative model which attempts to classify the total reward with accumulating features. We discuss the implications of the study as well as limitations.
Online Adaptation of Influence Diagrams

The present invention relates to a pest control composition comprising an aqueous phase having suspended therein substantially uniform droplets of an invert emulsion comprising an external oil system, an internal system comprising water or an oil-in-water emulsion, and at least one pest control agent present in an amount sufficient to control pests, said agent being contained in at least one of said external or internal systems and the method of controlling pests comprising applying said composition to the locus of said pest infestations.
Monotone DAG Faithfulness: A Bad Assumption

In a recent paper, Cheng, Greiner, Kelly, Bell and Liu (Artificial Intelligence 137:4390; 2002) describe an algorithm for learning Bayesian networks that—in a domain consisting of n variables—identifies the optimal solution using O(n) calls to a mutual-information oracle. This seemingly incredible result relies on (1) the standard assumption that the generative distribution is Markov and faithful to some directed acyclic graph (DAG), and (2) a new assumption about the generative distribution that the authors call monotone DAG faithfulness (MDF). The MDF assumption rests on an intuitive connection between active paths in a Bayesiannetwork structure and the mutual information among variables. The assumption states that the (conditional) mutual information between a pair of variables is a monotonic function of the set of active paths between those variables; the more active paths between the variables the higher the mutual information. In this paper, we demonstrate the unfortunate result that, for any realistic learning scenario, the monotone DAG faithfulness assumption is incompatible with the faithfulness assumption. In fact, by assuming both MDF and faithfulness, we restrict the class of possible Bayesian-network structures to one for which the optimal solution can be identified with O(n) calls to an independence oracle.
Improving the approximation and convergence capabilities of projection pursuit learning

One nonparametric regression technique that has been successfully applied to high-dimensional data is projection pursuit regression (PPR). In this method, the regression surface is approximated by a sum of empirically determined univariate functions of linear combinations of the predictors. Projection pursuit learning (PPL) proposed by Hwanget al. formulates PPR using a two-layer feedforward neural network. One of the main differences between PPR and PPL is that the smoothers in PPR are nonparametric, whereas those in PPL are based on Hermite functions of some predefined highest orderR. While the convergence property of PPR is already known, that for PPL has not been thoroughly studied. In this paper, we demonstrate that PPL networks do not have the universal approximation and strong convergence properties for any finiteR. But, by including a bias term in each linear combination of the predictor variables, PPL networks can regain these capabilities, independent of the exact choice ofR. It is also shown experimentally that this modification improves the generalization performance in regression problems, and creates smoother decision surfaces for classification problems.
Efficient hyperkernel learning using second-order cone programming

The kernel function plays a central role in kernel methods. Most existing methods can only adapt the kernel parameters or the kernel matrix based on empirical data. Recently, Ong et al. introduced the method of hyperkernels which can be used to learn the kernel function directly in an inductive setting. However, the associated optimization problem is a semidefinite program (SDP), which is very computationally expensive, even with the recent advances in interior point methods. In this paper, we show that this learning problem can be equivalently reformulated as a second-order cone program (SOCP), which can then be solved more efficiently than SDPs. Comparison is also made with the kernel matrix learning method proposed by Lanckriet et al. Experimental results on both classification and regression problems, with toy and real-world data sets, show that our proposed SOCP formulation has significant speedup over the original SDP formulation. Moreover, it yields better generalization than Lanckriet et al.'s method, with a speed that is comparable, or sometimes even faster, than their quadratically constrained quadratic program (QCQP) formulation.
Core Vector Regression for very large regression problems

In this paper, we extend the recently proposed Core Vector Machine algorithm to the regression setting by generalizing the underlying minimum enclosing ball problem. The resultant Core Vector Regression (CVR) algorithm can be used with any linear/nonlinear kernels and can obtain provably approximately optimal solutions. Its asymptotic time complexity is linear in the number of training patterns m, while its space complexity is independent of m. Experiments show that CVR has comparable performance with SVR, but is much faster and produces much fewer support vectors on very large data sets. It is also successfully applied to large 3D point sets in computer graphics for the modeling of implicit surfaces.