Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Decoding 3.1 Encoding and Decoding

In chapters 1 and 2, we considered the problem of predicting neural responses to known stimuli. The nervous system faces the reverse problem, determining what is going on in the real world from neuronal spiking patterns. It is interesting to attempt such computations ourselves, using the responses of one or more neurons to identify a particular stimulus or to extract the value of a stimulus parameter. We will assess the accuracy with which this can be done primarily by using optimal decoding techniques, regardless of whether the computations involved seem biologically plausible. Biophysically realistic implementations for some of these techniques are discussed in chapter 7. Optimal decoding allows us to determine limits on the accuracy and reliability of neuronal encoding. In addition, it is useful for estimating the information content of neuronal spike trains, as discussed in the next chapter. As Rieke, Warland, de Ruyter van Steveninck and Bialek (1997) have noted, the encoding and decoding of neural responses are related through a basic identity of probability theory, Bayes theorem. Let r represent the response of a neuron or a population of neurons to a stimulus s. Throughout this chapter, r = (r 1 , r 2 ,. . , r N) for N neurons is a list of neuronal firing rates. However, for the present discussion it could be any set of parameters describing the neuronal response, such as a list of spike times. P[s] and P[r] denote the probabilities that stimulus s appears and that response r is recorded, respectively. To study neural encoding and decoding, we need, in addition, two conditional probabilities: the probability of evoking a response r given that the stimulus s was presented, denoted by P[r|s]; and the probability that a given stimulus s was presented given that a particular response r was recorded, P[s|r]. The encoding map is characterized by the probabilities P[r|s] which reflect features of the encoding process as discussed in chapter 1. The decoding map is defined by the probabil
Investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

Transcriptome-wide association studies (TWAS), an integrative framework using expression quantitative trait loci (eQTLs) to construct proxies for gene expression, have emerged as a promising method to investigate the biological mechanisms underlying associations between genotypes and complex traits. However, challenges remain in interpreting TWAS results, especially regarding their causality implications. In this paper, we describe a new computational framework, probabilistic TWAS (PTWAS), to detect associations and investigate causal relationships between gene expression and complex traits. We use established concepts and principles from instrumental variables (IV) analysis to delineate and address the unique challenges that arise in TWAS. PTWAS utilizes probabilistic eQTL annotations derived from multi-variant Bayesian fine-mapping analysis conferring higher power to detect TWAS associations than existing methods. Additionally, PTWAS provides novel functionalities to evaluate the causal assumptions and estimate tissue- or cell-type specific causal effects of gene expression on complex traits. These features make PTWAS uniquely suited for in-depth investigations of the biological mechanisms that contribute to complex trait variation. Using eQTL data across 49 tissues from GTEx v8, we apply PTWAS to analyze 114 complex traits using GWAS summary statistics from several large-scale projects, including the UK Biobank. Our analysis reveals an abundance of genes with strong evidence of eQTL-mediated causal effects on complex traits and highlights the heterogeneity and tissue-relevance of these effects across complex traits. We distribute software and eQTL annotations to enable users performing rigorous TWAS analysis by leveraging the full potentials of the latest GTEx multi-tissue eQTL data.
Sublinear- Time Algorithms for Compressive Phase Retrieval

In the compressive phase retrieval problem, the goal is to reconstruct a sparse or approximately k-sparse vector $x\in \mathbb{R}^{n}$ given access to $y=\vert \Phi x\vert$, where $\vert v\vert$ denotes the vector obtained from taking the absolute value of $v\in \mathbb{R}^{n}$ coordinate-wise. In this paper we present sublinear-time algorithms for different variants of the compressive phase retrieval problem which are akin to the variants of the classical compressive sensing problem considered in theoretical computer science. Our algorithms use pure combinatorial techniques and achieve almost optimal number of measurements.
Segmentation of multi-spectral images using a combined classifier approach

Analysis of multi-spectral data requires new image segmentation methods using both available domains – spatial and spectral. The paper presents a new approach for the multi-spectral image segmentation based completely on statistical pattern recognition methods. The method combines results obtained in the spatial and spectral domains by combined classifier approach. The algorithm performance is investigated on the multi-spectral images of detergent powders acquired by the method of scanning electron microscopy. A method for the segmentation of images with a similar spectral structure is devised. The stability and quality of the segmentation result is also discussed.
RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images Expert Systems With Applications

Since the advent of COVID-19, the number of deaths has increased exponentially, boosting the requirement for various research studies that may correctly diagnose the illness at an early stage. Using chest X-rays, this study presents deep learning-based algorithms for classifying patients with COVID illness, healthy controls, and pneumonia classes. Data gathering, pre-processing, feature extraction, and classification are the four primary aspects of the approach. The pictures of chest X-rays utilized in this investigation came from various publicly available databases. The pictures were filtered to increase image quality in the pre-processing stage, and the chest X-ray images were de-noised using the empirical wavelet transform (EWT). Following that, four deep learning models were used to extract features. The first two models, Inception-V3 and Resnet-50, are based on transfer learning models. The Resnet-50 is combined with a temporal convolutional neural network (TCN) to create the third model. The fourth model is our suggested RESCOVIDTCNNet model, which integrates EWT, Resnet-50, and TCN. Finally, an artificial neural network (ANN) and a support vector machine were used to classify the data (SVM). Using five-fold cross-validation for 3-class classification, our suggested RESCOVIDTCNNet achieved a 99.5 percent accuracy. Our prototype can be utilized in developing nations where ra- diologists are in low supply to acquire a diagnosis quickly.
A Ubiquitous Knowledge-based System to Enable RFID Object Discovery in Smart Environments

A process for causing to vibrate at determined frequencies pieces having a relatively large surface, notably plane pieces, wherein a volume comprising the piece or pieces to be caused to vibrate is achieved, said piece or pieces are elastically connected to the walls of said volume wherein a fluid is running so that it supports the surface or surfaces to be caused to vibrate and vibrations are periodically generated in said fluid so as to generate shock-waves in succession which, in turn, cause the vibration at a frequency corresponding to that of the piece or pieces elastically mounted.
Function-Based Haptic Interaction in Cyberworlds

Polygon and point based models dominate in virtual reality. These models also affect haptic rendering algorithms which are often based on collision with polygons. We use mathematical functions to define and implement geometry (curves, surfaces and solid objects), visual appearance (3D colors and geometric textures) and various tangible physical properties (elasticity, friction, viscosity, and force fields). The function definitions are given as analytical formulas (explicit, implicit and parametric), function scripts and procedures. Since the defining functions are very small we can efficiently use them in collaborative virtual environments to exchange between the participating clients. We proposed an algorithm for haptic rendering of virtual scenes including mutually penetrating objects with different sizes and arbitrary location of the observer without a prior knowledge of the scene to be rendered. The algorithm is based on casting multiple haptic rendering rays from the Haptic Interaction Point (HIP), and it builds a stack to keep track on all colliding objects with the HIP. The algorithm uses collision detection based on implicit function representation of the object surfaces. The proposed approach allows us to be flexible when choosing the actual rendering platform. The function-defined objects and parts constituting them can be used together with other common definitions of virtual objects such as polygon meshes, point sets, voxel volumes, etc. We implemented an extension of X3D and VRML which allows for defining complex geometry, appearance and haptic effects in virtual scenes by functions and common polygon-based models, with various object sizes, mutual penetrations, arbitrary location of the observer and variable precision.
Decentralized adaptive fuzzy control of robot manipulators

This paper develops a decentralized adaptive fuzzy control scheme for robot manipulators via a combination of genetic algorithm and gradient method. The controller for each link consists of a feedforward fuzzy torque-computing system and a feedback fuzzy PD system. The feedforward fuzzy system is trained and optimized off-line by an improved genetic algorithm, that is to say, not only the parameters but also the structure of the fuzzy system are self-organized. Because genetic algorithm can operate successfully without the system model, no exact inverse dynamics of the robot system are required. The feedback fuzzy PD system, on the other hand, is tuned on-line using gradient method. In this way, the proportional and derivative gains are adjusted properly to keep the closed-loop system stable. The proposed controller has the following merits: (1) it needs no exact dynamics of the robot systems and the computation is time-saving because of the simple structure of the fuzzy systems; and (2) the controller is insensitive to various dynamics and payload uncertainties in robot systems. These are demonstrated by analyses of the computational complexity and various computer simulations.
Morphological Sampling

This paper presents a sampling strategy for gray-level functions based on mathematical morphology. Flexible sampling strategies are required for the construction of multiresolution image representations like pyramids and quad-trees. The sampling operator introduced here is a dilation. A number of different algorithms to reconstruct an image from its sampled version are presented. It is shown that a reconstruction which dominates the original image is obtained by application of the adjoint erosion.
An evaluation of intrusive instrumental intelligibility measures

Instrumental intelligibility metrics are commonly used as an alternative to intelligibility listening tests. This paper evaluates 12 existing monaural intrusive instrumental intelligibility metrics: SII, HEGP, CSII, HASPI, NCM, QSTI, STOI, ESTOI, MIKNN, SIMI, SIIB, and sEPSM. The intelligibility data used in the evaluation were obtained from ten listening tests described in the literature. The stimuli included speech that was distorted by additive noise, reverberation, competing talkers, pre-processing enhancement, and post-processing enhancement. STOI, which is arguably the most popular intelligibility metric, achieved a correlation with listening test scores on average of ρ = 0.80, and its successor, ESTOI, achieved ρ = 0.86. The metrics with the highest overall performance were SIIB (ρ = 0.92) and HASPI (ρ = 0.89). The results show that many intelligibility metrics perform poorly on data sets that were not used during their development, thus caution should be taken when using intelligibility metrics to replace listening tests, especially in situations where the accuracy of the metric has not been verified.
Using Formal Concept Analysis with an Incremental Knowledge Acquisition System for Web Document Management

It is necessary to provide a method to store Web information effectively so it can be utilised as a future knowledge resource. A commonly adopted approach is to classify the retrieved information based on its content. A technique that has been found to be suitable for this purpose is Multiple Classification Ripple-Down Rules (MCRDR). The MCRDR system constructs a classification knowledge base over time using an incremental learning process. This incremental method of acquiring classification knowledge suits the nature of Web information because it is constantly evolving and being updated. However, despite this advantage, the classification knowledge of the MCRDR system is not often utilised for browsing the classified information. This is because it does not directly organise the knowledge in a way that is suitable for browsing. As a result, often an alternate structure is utilised for browsing the information which is usually based on a user’s abstract understanding of the information domain. This study investigated the feasibility of utilising the classification knowledge acquired through the use of the MCRDR system as a resource for browsing information retrieved from the WWW. A system was implemented that used the concept lattice-based browsing scheme of Formal Concept Analysis (FCA) to support the browsing of documents based on the MCRDR classification knowledge. The feasibility of utilising classification knowledge as a resource for browsing documents was evaluated statistically. This was achieved by comparing the concept lattice-based browsing approach to a standard one that utilises abstract knowledge of a domain as a resource for browsing the same documents.
Electronic Research

Veriﬁcation and validation is an important part of software development and accounts for signiﬁcant amounts of the costs associated with such a project. For developers of life or mission critical systems, such as software being developed for space applications, a balance must be reached between ensuring the quality of the system by extensive and rigorous testing and reducing costs and allowing the company to compete. Ensuring the quality of any system starts with a quality development process. To evaluate both the software development process and the product itself, measurements are needed. A balance must be then struck between ensuring the best possible quality of both process and product on the one hand, and reducing the cost of performing requirements on the other. A number of measurements have already been deﬁned and are being used. For some of these, data collection can be automated as well, further lowering costs associated with implementing them. In practice, however, there may be situations where existing measurements are unsuitable for a variety of reasons. This paper describes a framework for creating low cost, ﬂexible measurements in areas where initial information is scarce. The framework, called The Measurements Exploration Framework, is aimed in particular at the Space Software development industry and was developed is such an environment.
Leaf Area, Fresh Weight and Dry Weight Prediction Models for Ornamental Plants Ficus benjamina (cv. Starlight)

Measurements of leaf growth indices namely leaf area, fresh weight and dry weight are of value in physiological studies and plant growth estimation. The use of prediction models to estimate leaf area, fresh weight and dry weight is simple, rapid and non-destructive. Several mathematical functions have been formulated for estimating leaf area, fresh weight and dry weight of various crops but almost there is no information for Ficus benjamina. This work was aimed to propose leaf area (LA), fresh weight (FW) and dry weight (DW) prediction models for Ficus benjamina (cv. Starlight) leafy ornamental pot plant using leaf length (L) and width (W). 1000 leaves were collected randomly from greenhouse grown plants and 700 of cuts were used for prediction models. LA was measured with a digital area meter (DELTA-T, Co. Durham, UK), related FW and DW also were weighted and leaf dimensions were determined by the ruler. For each studying growth index LA, FW and DW the predictive abilities of three regression equations (linear, polynomial and power) were compared with different independent variables for each equation. Leaf length × width provided a good estimation of leaf area and fresh weight of the leaves of Ficus benjamina. It was also concluded that leaves the dry weight of Ficus benjamina can be estimated or simulated as a power function of L×W or L+W with reasonable accuracy. Moreover, a reasonable relationship between leaf fresh weight and leaf area was found too.
The Use of Informativity in the Development of Robust Metaviromics-based Examinations

The field of metagenomics has developed insight into many of the complex microbial communities responsible for maintaining life on this planet. Sequencing efforts often uncover novel genetic content; this is most evident for viral metagenomics, in which upwards of 90% of all sequences demonstrate no sequence similarity with present databases. For the small fraction which can be identified, the top BLAST hit is often posited as being representative of the phage taxon. However, as previous research has shown, the top BLAST hit is sometimes misinterpreted. Furthermore, the appearance of a particular gene homolog is frequently not representative of the presence of the particular taxon in question. To circumvent these limitations, we have developed a new method for the analysis of metaviromic datasets. BLAST hits are weighted, integrating the sequence identity and length of alignments as well as a phylogenetic signal. A genic rather than genomic approach is presented in which each gene is evaluated with respect to its information content. Through this quantifiable metric, predictions of viral community structure can be made with greater confidence. As a proof-of-concept, the approach presented here was implemented and applied to seven metaviromes. While providing a more robust means of evaluating metaviromic data, the tool is versatile and can easily be customized to investigations of any environment or biome.
All Attacks ( Random + Targeted ) Accuracy Scope Scan / Random Attacks

We present Shadow Honeypots, a novel hybrid architecture that combines the best features of honeypots and anomaly detection. At a high level, we use a variety of anomaly detectors to monitor all traffic to a protected network/service. Traffic that is considered anomalous is processed by a “shadow honeypot” to determine the accuracy of the anomaly prediction. The shadow is an instance of the protected software that shares all internal state with a regular (“production”) instance of the application, and is instrumented to detect potential attacks. Attacks against the shadow are caught, and any incurred state changes are discarded. Legitimate traffic that was misclassified will be validated by the shadow and will be handled correctly by the system transparently to the end user. The outcome of processing a request by the shadow is used to filter future attack instances and could be used to update the anomaly detector. Our architecture allows system designers to fine-tune systems for performance, since false positives will be filtered by the shadow. Contrary to regular honeypots, our architecture can be used both for server and client applications. We demonstrate the feasibility of our approach in a proof-of-concept implementation of the Shadow Honeypot architecture for the Apache web server and the Mozilla Firefox browser. We show that despite a considerable overhead in the instrumentation of the shadow honeypot (up to 20% for Apache), the overall impact on the system is diminished by the ability to minimize the rate of false-positives.
Frictions for Mindful Interactions : The Case for Microboundaries Conference or Workshop Item

Design frictions, a term found in popular media articles about user experience design, refer to points of difficulty occurring during interaction with technology. Such articles often argue that these frictions should be removed from interaction flows in order to reduce the risk of user frustration and disengagement. In this paper we argue that, in many scenarios, designing friction into interactions through the introduction of microboundaries, can, in fact, have positive effects. Design frictions can disrupt “mindless” automatic interactions, prompting moments of reflection and more “mindful” interaction. The potential advantages of intentionally introduced frictions are numerous: from reducing the likelihood of errors in data-entry tasks, to supporting health-behaviour change.
Grounding via Scanning: Cooking up Roles from Scratch

At the heart of nearly every model in cognitive science and arti cial intelligence is the notion of roles and their llers. Roles appear as explicit slots in frame systems (Minsky, 1975), as particular positions in predicate calculus representations, as role-speci c groups in some connectionist models (McClelland & Kawamoto, 1986), and as distributed patterns in role space in other connectionist models (Smolensky, 1990). But where do roles come from? In most systems, this is not treated as an issue; objects are simply assigned fully formed structured representations. But for some problems the decomposition of an object into a set of roles and the assigning of llers to these roles appears to be half of the battle. For instance, systems attempting to make analogies between two domains are already half nished before they start if they are given sets of roles and associated llers for the items being compared (Holyoak & Thagard, 1989). We believe that problems such as analogy can be addressed adequately only when the structure of representations is \grounded" in perception and action (Harnad, 1991). Recent e orts to ground semantics in visual input (Bartell & Cottrell, 1991; Regier, 1990) have focused on what distinguishes predicates such as fast from slow or above from below. These approaches have not looked at questions of how objects are assigned to di erent roles or how a system would cope with predicates taking di erent numbers of arguments. Consider a system which is given a visual display containing a number of objects and the task of describing what it sees. It must do several things. It must nd one or more objects, assign labels of some sort to them, recognize that a relation exists between two or more of the objects (if it does), assign a label to the relation, and assign the objects to the roles of the relation. The assignment of objects to roles depends on the relative salience of the objects; in each relation the most salient object becomes the trajector (Langacker, 1987), realized linguistically as the subject; the other objects are landmarks, realized as the direct object or some other argument. This view recognizes the non-equivalence of pairs such as the square is above the circle and the circle is below the square; they di er with respect to the perceived relative salience of the two objects. One possibility for how this might be done is as follows. The system scans the visual input for objects, identifying them as it locates them. It keeps track of the sequence of scanning actions it makes, and when two or more objects are perceived in succession, the memory for the recent