nils-herrmann/clean_scirepeval huggingface dataset:
Experience‐dependent cortical processing of complex sounds by rats

Auditory cortex is thought to play a critical role in the processing of species‐specific vocalizations and other acoustically complex sounds. Although evolutionary processes strongly constrain cortical sensitivities to sound, cortical processing is not fixed by biology, but rather is shaped by the auditory experiences of each individual. Auditory cortical neurons in adult rats respond selectively to spectrotemporal features of complex sounds. These selective responses are predictive of rats’ behaviorally measured perceptual sensitivities. With extensive training, the abilities of rats to discriminate frequency‐modulated sounds improve. Recordings from cortical neurons in trained rats show increased sensitivities to features of the sounds used in training. These results demonstrate that discrimination training with biologically irrelevant complex sounds can change how cortical neurons process those sounds. Changes in cortical processing of complex sounds can also be induced by controlling activity in neuro...
Temporal structure in neuronal activity during working memory in macaque parietal cortex

Many cortical structures have elevated firing rates during working memory, but it is not known how the activity is maintained. To investigate whether reverberating activity is important, we studied the temporal structure of local field potential (LFP) activity and spiking from area LIP in two awake macaques during a memory-saccade task. Using spectral analysis, we found spatially tuned elevated power in the gamma band (25–90 Hz) in LFP and spiking activity during the memory period. Spiking and LFP activity were also coherent in the gamma band but not at lower frequencies. Finally, we decoded LFP activity on a single-trial basis and found that LFP activity in parietal cortex discriminated between preferred and anti-preferred direction with approximately the same accuracy as the spike rate and predicted the time of a planned movement with better accuracy than the spike rate. This finding could accelerate the development of a cortical neural prosthesis.
Latent variable models for neural data analysis

The brain is perhaps the most complex system to have ever been subjected to rigorous scientific 
investigation. The scale is staggering: over 10^11 neurons, each making an average of 10^3 synapses, 
with computation occurring on scales ranging from a single dendritic spine, to an entire cortical 
area. Slowly, we are beginning to acquire experimental tools that can gather the massive amounts 
of data needed to characterize this system. However, to understand and interpret these data will 
also require substantial strides in inferential and statistical techniques. This dissertation attempts 
to meet this need, extending and applying the modern tools of latent variable modeling to problems 
in neural data analysis. It is divided into two parts. The first begins with an exposition of the general techniques of 
latent variable modeling. A new, extremely general, optimization algorithm is proposed - called 
Relaxation Expectation Maximization (REM) - that may be used to learn the optimal parameter 
values of arbitrary latent variable models. This algorithm appears to alleviate the common problem 
of convergence to local, sub-optimal, likelihood maxima. REM leads to a natural framework for 
model size selection; in combination with standard model selection techniques the quality of fits may 
be further improved, while the appropriate model size is automatically and efficiently determined. 
Next, a new latent variable model, the mixture of sparse hidden Markov models, is introduced, and 
approximate inference and learning algorithms are derived for it. This model is applied in the second 
part of the thesis. The second part brings the technology of part I to bear on two important problems in experimental 
neuroscience. The first is known as spike sorting; this is the problem of separating the spikes 
from different neurons embedded within an extracellular recording. The dissertation offers the first 
thorough statistical analysis of this problem, which then yields the first powerful probabilistic solution. 
The second problem addressed is that of characterizing the distribution of spike trains recorded 
from the same neuron under identical experimental conditions. A latent variable model is proposed. 
Inference and learning in this model leads to new principled algorithms for smoothing and clustering 
of spike data.
Extracellular recording from multiple neighboring cells: a maximum-likelihood solution to the spike-separation problem

In recent years considerable attention among extracellular electrophysiologists has focused on the problem of simultaneously recording the activity of multiple neurons in behaving animals. Such recordings, it is hoped, will provide much-needed insight into the dynamics of neural ensemble computation and coding. Of particular interest are recordings from neighboring neurons, for example cells that lie within a single column of neocortex Such cells are likely to share functional roles and to possess the anatomical interConnectivity needed for ensemble coding, making them plausible participants in local computational and signaling circuits.
Extra cesllular recording from multiple neighboring cells: response properties in parietal cortex

Multiple single unit extracellular recordings were made using tetrodes in macaque posterior parietal cortex while the animal was performing a visual memory saccade task. Recordings were made over a 2×2 mm area at both superficial and deep locations in one hemisphere. Signals were analyzed using an Expectation-Maximization algorithm for spike separation based on spike peak height or the first two principle components of spike shape. 27 sites were selected for analysis based on task response and clarity of separation, yielding 85 total neurons with a mode of 3 cells per site. The response criteria and stereotaxic location used were consistent with identifying neurons within the lateral intraparietal area (LIP).
Tetrodes for monkeys

We have adapted the Recce-O’Keefe tetrode for use in monkey cortex. Eleven penetrations have been made over macaque area MT to evaluate the mechanical and electrical properties of our design. Experiments have been made with 15 and 25 μm insulated nichrome wire running through oil-filled 33 gauge stainless steel guide tubes. Electrically, the tetrodes compare favorably with traditional tungsten electrodes for single-unit isolation, and are superior for multi-unit recordings. Preliminary histology shows straight tracks at up to 12 mm tetrode extension.
On the Separation of Signals from Neighboring Cells in Tetrode Recordings

We discuss a solution to the problem of separating waveforms produced by multiple cells in an extracellular neural recording. We take an explicitly probabilistic approach, using latent-variable models of varying sophistication to describe the distribution of waveforms produced by a single cell. The models range from a single Gaussian distribution of waveforms for each cell to a mixture of hidden Markov models. We stress the overall statistical structure of the approach, allowing the details of the generative model chosen to depend on the specific neural preparation.
Color-metric tensor for catadioptric systems

Catadioptric cameras developed recently provide images with large field of view. Nevertheless, due to the use of mirrors, these images contain significant radial distortions that are necessary to handle when processing them. In this paper, we present the use of differential geometry for the construction of a hybrid structure tensor suited to the multicomponent catadioptric images. This structure tensor allows, at the same time, to take into account the geometry of the mirror and the information provided by the multidimensional nature of these images. Smoothing process and edge detection experiments illustrate the potential of our proposed approach and show higher quality of adaptive processing.
Car detection using deformable part models with composite features

A two stages car detection method using deformable part models with composite feature sets (DPM/CF) is proposed to recognize cars of various types and from multiple viewing angles. In the first stage, a HOG template is matched to detect the bounding box of the entire car of a certain type and viewed from a certain angle (called a t/a pair), which yields a region of interest (ROI). In the second stage, various part detectors using either HOG or the convolution neural network (CNN) features are applied to the ROI for validation. An optimization procedure based on latent logistic regression is adopted to select the optimal part detector's location, window size, and feature to use. Extensive experimental results indicate the proposed DPM/CF system can strike a balance between detection accuracy and training complexity.
Learning deep filter banks in parallel for texture recognition

Texture is an important visual clue for various classification and segmentation tasks in the scene understanding challenge. Today, successful deployment of deep learning algorithms for texture recognition leads to tremendous precisions on standard datasets. In this paper, we propose a new learning framework to train deep neural networks in parallel and with variable depth for texture recognition. Our framework learns scales, orientations and resolutions of texture filter banks. Due to the learning of parameters not the filters themselves, computational costs are highly reduced. It is also capable of extracting very deep features through distributed computing architectures. Our experiments on publicly available texture datasets show significant improvements in the recognition performance over other deep local descriptors in recently published benchmarks.
Scene-based low delay HEVC encoding framework based on transparent composite modeling

To improve the rate-distortion (RD) performance of low delay (LD) High Efficiency Video Coding (HEVC) encoding while enabling scene-based non-linear editing feature (NLEF), in this paper, we present a new scene-based LD HEVC encoding framework in which scene change (SC) detection and coding are conducted jointly. A frame in a sequence to be encoded is deemed a SC if there is a sudden change in the residual energy. SCs are detected using the residual image parameters identified from the newly proposed Laplacian Transparent Composite Model (LPTCM). When a SC is detected, it is designated as an I frame; the quantization parameters for this frame and its subsequent frames before the next SC are determined. Compared to the default LD configuration, experimental results show that an average gain of 0.24 dB in RD performance and reduced overall encoding time are achieved while providing the scene-based NLEF.
LDR images generation with JPEG-XT decoded HDR images

This paper studies the influence of JPEG-XT on LDR generation using TMOs'. JPEG-XT encodes HDR images into a two layer scheme, encoding a LDR version of the image in a base layer, and the residual HDR information in an enhancement layer. The question addressed here is to understand if this model allows to extract a new LDR representation using a different TMO, independently of the TMO used to generate the LDR image encoded in the base layer. In particular, the use of the Drago, Mai, Mantiuk, Reinhard, and Reinhard & Devlin TMOs' will be considered together with the three main profiles defined on the JPEG-XT standard. Hence, images generate with a TMO applied to the decoded HDR image are compared with the generated from the original HDR image using the popular FSIM metric. Moreover, in cases with similar perceptual quality, the bit rate increase, compared with the JPEG encoding is also studied for the three JPEG-XT profiles.
Hand-held low-light photography with exposure bracketing

Taking good photos in low-light conditions using a smart-phone camera is quite challenging. In this paper, we propose a method to produce a sharp well-exposed image under dim light scenarios using exposure bracketing. These images captured from a hand-held camera can be viewed as a set of blurred (high exposure) and noisy images (low exposure). We first describe an algorithm for estimating the sharp latent image and depth map from a noisy-blurred pair by employing a gradient prior derived from the noisy image. We also show how our approach can be extended for extracting the high dynamic range information embedded in differently exposed images. Our framework can comfortably account for mis-alignments, depth variations and space-variant blurring.
Smile detection in the wild with hierarchical visual feature

Smile detection in the wild is an interesting and challenging problem. This paper presents an efficient approach with hierarchical visual feature to handle this problem. In our approach, Gabor filters with multi-scale, multi-orientation are first applied to extract facial textures namely Gabor faces from the input face image. After this, Histograms of Oriented Gradients (HOG) are employed to encode these extracted Gabor faces to capture and characterize the facial appearance characteristics. We further adopt a pooling strategy to transform the multiple HOG features into a global visual feature called Gabor-Hog. Finally, SVM is trained to perform the classification. The experiments conducted on the GENKI4K database show that the proposed visual feature is robust to distinguish a smile face from a no-smile face. Our method also achieves a promising performance compared with the other state-of-the-art methods.
CNN based region proposals for efficient object detection

In computer vision, object detection is addressed as one of the most challenging problems as it is prone to localization and classification error. The current best-performing detectors are based on the technique of finding region proposals in order to localize objects. Despite having very good performance, these techniques are computationally expensive due to having large number of proposed regions. In this paper, we develop a high-confidence region-based object detection framework that boosts up the classification performance with less computational burden. In order to formulate our framework, we consider a deep network that activates the semantically meaningful regions in order to localize objects. These activated regions are used as input to a convolutional neural network (CNN) to extract deep features. With these features, we train a set of class-specific binary classifiers to predict the object labels. Our new region-based detection technique significantly reduces the computational complexity and improves the performance in object detection. We perform rigorous experiments on PASCAL, SUN, MIT-67 Indoor and MSRC datasets to demonstrate that our proposed framework outperforms other state-of-the-art methods in recognizing objects.
Direct inference on compressive measurements using convolutional neural networks

Compressive imagers, e.g. the single-pixel camera (SPC), acquire measurements in the form of random projections of the scene instead of pixel intensities. Compressive Sensing (CS) theory allows accurate reconstruction of the image even from a small number of such projections. However, in practice, most reconstruction algorithms perform poorly at low measurement rates and are computationally very expensive. But perfect reconstruction is not the goal of high-level computer vision applications. Instead, we are interested in only determining certain properties of the image. Recent work has shown that effective inference is possible directly from the compressive measurements, without reconstruction, using correlational features. In this paper, we show that convolutional neural networks (CNNs) can be employed to extract discriminative non-linear features directly from CS measurements. Using these features, we demonstrate that effective high-level inference can be performed. Experimentally, using hand written digit recognition (MNIST dataset) and image recognition (ImageNet) as examples, we show that recognition is possible even at low measurement rates of about 0.1.
Towards unsupervised open-set person re-identification

Most existing person re-identification (ReID) methods assume the availability of extensively labelled cross-view person pairs and a closed-set scenario (i.e. all the probe people exist in the gallery set). These two assumptions significantly limit their usefulness and scalability in real-world applications, particularly with large scale camera networks. To overcome the limitations, we introduce a more challenging yet realistic ReID setting termed OneShot-OpenSet-RelD, and propose a novel Regularised Kernel Subspace Learning model for ReID under this setting. Our model differs significantly from existing ReID methods due to its ability of effectively learning cross-view identity-specific information from unlabelled data alone, and its flexibility of naturally accommodating pairwise labels if available.
Knowing who to listen to: Prioritizing experts from a diverse ensemble for attribute personalization

Learning attribute models for applications like Zero-Shot Learning (ZSL) and image search is challenging because they require attribute classifiers to generalize to test data that may be very different from the training data. A typical scenario is when the notion of an attribute may differ from one user to another, e.g. one user may find a shoe formal whereas another user may not. In this case, the distribution of labels at test time is different from that at training time. We argue that due to the uncertainty in what the test distribution might be, committing to one attribute model during training is not advisable. We propose a novel framework for attribute learning which involves training an ensemble of diverse models for attributes and identifying experts from them at test time given a small amount of personalized annotations from a user. Our approach for attribute personalization is not specific to any classification model and we show results using Random Forest and SVM ensembles. We experiment with 2 datasets: SUN Attributes and Shoes and show significant improvements over baselines.
A novel automatic segmentation of healthy and diseased retinal layers from OCT scans

This paper introduces a novel framework for segmenting retinal layers from optical coherence tomography (OCT) images. In order to account for the noise and inhomogeneity of OCT scans, especially for diseased ones, the proposed framework is based on unique joint model that combines shape, intensity, and spatial information, and is able to segment 12 distinct retinal layers. First, the shape prior is built using a subset of co-aligned training OCT images. The alignment process is initialized using an innovative method that employs multi-resolution edge tracking which defines control points on the tracked retinal boundaries. The shape model is then adapted during the segmentation process using visual appearance characteristics that are described using pixel-wise image intensities and their spatial interaction features. In order to more accurately model the empirical grey level distribution of OCT images, a linear combination of discrete Gaussians (LCDG) is used that has positive and negative components. Also, in order to accurately account for noise, the model is integrated with a second-order Markov Gibbs random field (MGRF) spatial interaction model. The proposed approach was tested on 200 normal and diseased OCT scans (e.g. Age macular degeneration (AMD), diabetic retinopathy), having their ground truth delineated by retina specialists, then measured using the Dice similarity coefficient (DSC), agreement coefficient (AC1), and average deviation (AD) metrics. The accuracy achieved by the segmentation approach clearly demonstrates the promise it holds for robust segmentation of retinal layers which would aid in the early diagnosis of different retinal abnormalities.
Figaro, hair detection and segmentation in the wild

Hair is one of the elements that mostly characterize people appearance. Being able to detect hair in images can be useful in many applications, such as face recognition, gender classification, and video surveillance. To this purpose we propose a novel multi-class image database for hair detection in the wild, called Figaro. We tackle the problem of hair detection without relying on a-priori information related to head shape and location. Without using any human-body part classifier, we first classify image patches into hair vs. non-hair by relying on Histogram of Gradients (HOG) and Linear Ternary Pattern (LTP) texture features in a random forest scheme. Then we obtain results at pixel level by refining classified patches by a graph-based multiple segmentation method. Achieved segmentation accuracy (85%) is comparable to state-of-the-art on less challenging databases.
Color texture representation using circular-processing based Hue-LBP for histo-pathology image analysis

Texture is considered one of most significant information sources in histo-pathology image analysis. To take advantage of information on color texture in digital histo-pathology, this work analyzes inherent characteristics of the hue component in the cylindrical color space, and introduces an effective color texture descriptor based on the LBP paradigm. Unlike existing LBP variants designed for linear data, the proposed descriptor, namely Hue-LBP, addresses the angular and periodic nature of hue and shows that color variation in the hue channel can be quantified by an angular variable in the range of [0,180]. By introducing the concept of color similarity as a metric to measure color variation, we obtain a histogram to describe local color texture patterns. Experimentation on histo-pathology image classification suggests that the proposed Hue-LBP is discriminative as it is capable of describing texture information conveyed by the hue components.
A weighted difference of anisotropic and isotropic total variation for relaxed Mumford-Shah image segmentation

We propose to incorporate a weighted difference of anisotropic and isotropic total variation (TV) norms into a relaxed formulation of the two phase Mumford-Shah (MS) model for image segmentation. We show results exceeding those obtained by the MS model when using the standard TV norm to regularize partition boundaries. In particular, examples illustrating the qualitative differences between the proposed model and the standard MS one are shown. A fast numerical method is introduced to minimize the proposed model utilizing the difference-of-convex algorithm (DCA) and the primal dual hybrid gradient (PDHG) method.
Pursuing face identity from view-specific representation to view-invariant representation

How to learn view-invariant facial representations is an important task for view-invariant face recognition. The recent work  discovered that the brain of the macaque monkey has a face-processing network, where some neurons are view-specific. Motivated by this discovery, this paper proposes a deep convolutional learning model for face recognition, which explicitly enforces this view-specific mechanism for learning view-invariant facial representations. The proposed model consists of two concatenated modules: the first one is a convolutional neural network (CNN) for learning the corresponding viewing pose to the input face image; the second one consists of multiple CNNs, each of which learns the corresponding frontal image of an image under a specific viewing pose. This method is of low computational cost, and it can be well trained with a relatively small number of samples. The experimental results on the MultiPIE dataset demonstrate the effectiveness of our proposed convolutional model in contrast to three state-of-the-art works.