Miracle-dz/UniFAR-FTUs huggingface dataset:
Brute-forcing hierarchical functionals for paralinguistics: A waste of feature space?

While the " 'quasi-state-of-the-art'" towards acoustic emotion recognition relies on multivariate time-series analysis of e.g. pitch, energy, or MFCC by statistical functionals as moments or extrema, only few respect statistical noise by outliers due to too long segments as turns. Such noise can be overcome by hierarchical functionals as means of extrema over smaller units as words or chunks. Segmentation of such units however usually relies on transcription. We therefore discuss hierarchical functionals based on automatic segmentation and their systematic generation as opposed to common expert-driven selection. To cope with rapidly growing feature spaces iquest5k, we discuss data-driven two-stage compression based on SVM- SFFS. Extensive test-runs are carried out on two known emotion and behavior corpora, and show superiority of the suggested approach.
Effect of Work Function on Double Gate MOSFET

The Double gate (DG) silicon on insulator (SOI) metal oxide field effect transistor (MOSFET) is the leading contender for sub 100nm devices. This paper presents a systematic study of effect of work function of metal gates on symmetric double-gate (DG) MOSFET. Two-dimensional simulation tools are used to investigate the effect of work function of gates on Double gate MOSFET. From simulation we observed that by changing the work function of the metal gates of Double gate MOSFET we can change the threshold voltage. Hence by using this technique we can set the appropriate threshold voltage of DG MOSFET at same voltage and we can decrease the leakage current and short channel effects.
Energy Management and Smart Grids

The paper outlines energy management concepts and the smart grid evolution. The necessity of considering energy management as a crucial innovation in load supplying to permit a more powerful penetration of renewable energy usage at the building and city level and to perform energy savings and CO2 emissions reduction is pointed out. The driving factors to enhance the current power distribution are presented, and the benefits concerning smart grids are underlined. In the paper, a specific energy management analysis is reported by considering all the electric value chain, and the demand-side management and distributed on site control actions are described. To verify the benefit of energy management control actions, a house simulator and a grid simulator are here presented and the results discussed in three different scenarios. Moreover, in the paper, the evaluation of ecological benefits are reported, and a cost benefit analysis of the energy management system is performed. Results pointed out that with the standard control actions, the system is not economic for the end user, and only by using energy management systems with renewable energy, in site production remunerative energy savings can be reached. Finally the evolution of smart grids is presented, focusing on potential benefits and technical problems. The active grids, microgrids and virtual utility are described, and final consideration on hypothetical scenarios is presented in the conclusion.
Reveal Hidden Information in the Music Scores : Composer Attribution CS 229 Final Report

In this project, machine learning classi cation methods were applied to a music attribution challenge related to Josquin de Prez, a famous composer of the Renaissance period with many works misattributed to him. To solve the attribution problem, we trained classi ers using two kinds of music scores: scores veri ed to be composed by Josquin and scores composed by other contemporary musicians. These scores were retrieved from the Stanford Josquin Research Project. Features were extracted from the scores with knowledge of music theory and techniques of text mining. The optimized classi ers accurately distinguish whether a music score is composed by Josquin with a precision of 95% and a recall of 90%. An unsecure set of music works was analyzed by the optimized classi ers. From the successful results, our feature extraction and classi cation scheme may nd useful applications in more general composer attribution problems.
Optimal Database Locks for Efficient Integrity Checking

In concurrent database systems, correctness of update transactions refers to the equivalent effects of the execution schedule and some serial schedule over the same set of transactions. Integrity constraints add further semantic requirements to the correctness of the database states reached upon the execution of update transactions. Several methods for efficient integrity checking and enforcing exist. We show in this paper how to apply one such method to automatically extend update transactions with locks and simplified consistency tests on the locked entities. All schedules produced in this way are conflict serializable and preserve consistency. For certain classes of databases we also guarantee that the amount of locked database entities is minimal.
A reconfigurable fabric supporting full C/C++ input

Reconfigurable architectures have been widespread used to improve the performance of embedded applications. As most applications of this domain (e.g. video and audio standards) are traditionally specified in high-level programming languages (e.g. C/C++, Java, etc), their optimization process through a hardware accelerator relies on a source code translation from high- to low- level programming languages (e.g. VHDL and Verilog) to program the reconfigurable fabric. However, there is no automatic process to perform such translation. Moreover, the lower the level of the programming language is, the harder it is to manually specify the algorithm, which can greatly affect the hard time-to market imposed by the embedded market. In this paper, we present an easy-programmed reconfigurable fabric that accelerates embedded applications in a total transparent fashion. We propose the use of a run-time binary translation hardware that translates C/C++ source code to the reconfigurable fabric code, without human intervention. Experimental results show great speedups w.r. t. a general-purpose processor and advantageous tradeoff between performance and software productivity w.r. t. a traditional FPGA.
Functional double dissociation within the entorhinal cortex for visual scene-dependent choice behavior

How visual scene memory is processed differentially by the upstream structures of the hippocampus is largely unknown. We sought to dissociate functionally the lateral and medial subdivisions of the entorhinal cortex (LEC and MEC, respectively) in visual scene-dependent tasks by temporarily inactivating the LEC and MEC in the same rat. When the rat made spatial choices in a T-maze using visual scenes displayed on LCD screens, the inactivation of the MEC but not the LEC produced severe deficits in performance. However, when the task required the animal to push a jar or to dig in the sand in the jar using the same scene stimuli, the LEC but not the MEC became important. Our findings suggest that the entorhinal cortex is critical for scene-dependent mnemonic behavior, and the response modality may interact with a sensory modality to determine the involvement of the LEC and MEC in scene-based memory tasks. DOI: http://dx.doi. org/10.7554/eLife.21543. 001
Lattice Boltzmann method for adiabatic acoustics

The lattice Boltzmann method (LBM) has been proved to be a useful tool in many areas of computational fluid dynamics, including computational aero-acoustics (CAA). However, for historical reasons, its applications in CAA have been largely restricted to simulations of isothermal (Newtonian) sound waves. As the recent kinetic theory-based reformulation establishes a theoretical framework in which LBM can be extended to recover the full Navier–Stokes–Fourier (NS) equations and beyond, in this paper, we show that, at least at the low-frequency limit (sound frequency much less than molecular collision frequency), adiabatic sound waves can be accurately simulated by the LBM provided that the lattice and the distribution function ensure adequate recovery of the full NS equations.
Behavioral/systems/cognitive Functional Interplay between Posterior Parietal and Ipsilateral Motor Cortex Revealed by Twin-coil Transcranial Magnetic Stimulation during Reach Planning toward Contralateral Space

Posterior parietal cortex (PPC) has connections with motor and premotor cortex, thought to transfer information relevant for planning movements in space. We used twin-coil transcranial magnetic stimulation (tcTMS) methods to show that the functional interplay between human right PPC and ipsilateral motor cortex (M1) varies with current motor plans. tcTMS during the reaction time of a reach task revealed facilitatory influences of right PPC on right M1 only when planning a (contralateral) leftward rather than rightward reach, at two specific time intervals (50 and 125 ms) after an auditory cue. The earlier reach-direction-specific facilitatory influence from PPC on M1 occurred when subjects were blindfolded or when the targets were presented briefly, so that visual feedback corrections could not occur. PPC–M1 interplay was similar within the left hemisphere but was specific to (contralateral) rightward planned reaches, with peaks at 50 and 100 ms. Functional interplay between human parietal and motor cortex is enhanced during early stages of planning a reach in the contralateral direction.
Blind noise variance estimation for OFDMA signals

We present two new noise variance estimation methods for OFDMA signals transmitted through an unknown multipath fading channel. We focus on blind estimation as it does not require any pilot sequences and is therefore applicable to contexts, such as cognitive radio for instance, where little prior signal knowledge is available. The two estimators are respectively based on the time-frequency sparsity of OFDMA signals and on the redundancy induced by the cyclic prefix. Numerical simulations compare the performance of the two algorithms and highlight their complementarity.
Trust for Location-Based Authorisation

We propose a concept for authorisation using the location of a mobile device and the enforcement of location- based policies. Mobile devices enhanced by trusted computing capabilities operate an autonomous and secure location trigger and policy enforcement entity. Location determination is two-tiered, integrating cell-based triggering at handover with precision location measurement by the device.
Joint Material and Illumination Estimation from Photo Sets in the Wild

Faithful manipulation of shape, material, and illumination in 2D Internet images would greatly benefit from a reliable factorization of appearance into material (i.e. diffuse and specular) and illumination (i.e. environment maps). On the one hand, current methods that produce very high fidelity results, typically require controlled settings, expensive devices, or significant manual effort. To the other hand, methods that are automatic and work on 'in the wild' Internet images, often extract only low-frequency lighting or diffuse materials. In this work, we propose to make use of a set of photographs in order to jointly estimate the non-diffuse materials and sharp lighting in an uncontrolled setting. Our key observation is that seeing multiple instances of the same material under different illumination (i.e. environment), and different materials under the same illumination provide valuable constraints that can be exploited to yield a high-quality solution (i.e. specular materials and environment illumination) for all the observed materials and environments. Similar constraints also arise when observing multiple materials in a single environment, or a single material across multiple environments. Technically, we enable this by a novel scalable formulation using parametric mixture models that allows for simultaneous estimation of all materials and illumination directly from a set of (uncontrolled) Internet images. The core of this approach is an optimization procedure that uses two neural networks that are trained on synthetic images to predict good gradients in parametric space given observation of reflected light. We evaluate our method on a range of synthetic and real examples to generate high-quality estimates, qualitatively compare our results against state-of-the-art alternatives via a user study, and demonstrate photo-consistent image manipulation that is otherwise very challenging to achieve.
Approximating and Intersecting Surfaces from Points

Point sets become an increasingly popular shape representation. Most shape processing and rendering tasks require the approximation of a continuous surface from the point data. We present a surface approximation that is motivated by an efficient iterative ray intersection computation. On each point on a ray, a local normal direction is estimated as the direction of smallest weighted co-variances of the points. The normal direction is used to build a local polynomial approximation to the surface, which is then intersected with the ray. The distance to the polynomials essentially defines a distance field, whose zero-set is computed by repeated ray intersection. Requiring the distance field to be smooth leads to an intuitive and natural sampling criterion, namely, that normals derived from the weighted co-variances are well defined in a tubular neighborhood of the surface. For certain, well-chosen weight functions we can show that well-sampled surfaces lead to smooth distance fields with non-zero gradients and, thus, the surface is a continuously differentiable manifold. We detail spatial data structures and efficient algorithms to compute ray-surface intersections for fast ray casting and ray tracing of the surface.
Solving Principal Component Pursuit in Linear Time via $l_1$ Filtering

In the past decades, exactly recovering the intrinsic data structure from corrupted observations, which is known as robust principal component analysis (RPCA), has attracted tremendous interests and found many applications in computer vision. Recently, this problem has been formulated as recovering a low-rank component and a sparse component from the observed data matrix. It is proved that under some suitable conditions, this problem can be exactly solved by principal component pursuit (PCP), i.e., minimizing a combination of nuclear norm and $l_1$ norm. Most of the existing methods for solving PCP require singular value decompositions (SVD) of the data matrix, resulting in a high computational complexity, hence preventing the applications of RPCA to very large scale computer vision problems. In this paper, we propose a novel algorithm, called $l_1$ filtering, for \emph{exactly} solving PCP with an $O(r^2(m+n))$ complexity, where $m\times n$ is the size of data matrix and $r$ is the rank of the matrix to recover, which is supposed to be much smaller than $m$ and $n$. Moreover, $l_1$ filtering is \emph{highly parallelizable}. It is the first algorithm that can \emph{exactly} solve a nuclear norm minimization problem in \emph{linear time} (with respect to the data size). Experiments on both synthetic data and real applications testify to the great advantage of $l_1$ filtering in speed over state-of-the-art algorithms.
Vehicle Detection Based on the and– or Graph for Congested Traffic Conditions

In urban traffic video monitoring systems, traffic congestion is a common scene that causes vehicle occlusion and is a challenge for current vehicle detection methods. To solve the occlusion problem in congested traffic conditions, we have proposed an effective vehicle detection approach based on an and -or graph (AOG) in this paper. Our method includes three steps: constructing an AOG for representing vehicle objects in the congested traffic condition; training parameters in the AOG; and, finally, detecting vehicles using bottom-up inference. In AOG construction, sophisticated vehicle feature selection avoids using the easily occluded vehicle components but takes highly visible components into account. The vehicles are well represented by these selected vehicle features in the presence of a congested condition with serious vehicle occlusion. Furthermore, a hierarchical decomposition of the vehicle representation is proposed during AOG construction to further reduce the impact of vehicle occlusion. After AOG construction, all parameters in the AOG are manually learned from the training images or set and further applied to the bottom-up vehicle inference. There are two innovations of our method, i.e., the usage of the AOG in vehicle detection under congested traffic conditions and the special vehicle feature selection for vehicle representation. To fully test our method, we have done a quantitative experiment under a variety of traffic conditions, a contrast experiment, and several experiments on congested conditions. The experimental results illustrate that our method can effectively deal with various vehicle poses, vehicle shapes, and time-of-day and weather conditions. In particular, our approach performs well in congested traffic conditions with serious vehicle occlusion.
Wireless Power Transfer: Survey and Roadmap

Wireless power transfer (WPT) technologies have been widely used in many areas, e.g., the charging of electric toothbrush, mobile phones, and electric vehicles. This paper introduces fundamental principles of three WPT technologies, i.e., inductive coupling-based WPT, magnetic resonant coupling-based WPT, and electromagnetic radiation-based WPT, together with discussions of their strengths and weaknesses. Main research themes are then presented, i.e., improving the transmission efficiency and distance, and designing multiple transmitters/receivers. The state-of-the-art techniques are reviewed and categorised. Several WPT applications are described. Open research challenges are then presented with a brief discussion of potential roadmap.
Effects of coating roughness and biofouling on ship resistance and powering

Abstract Predictions of full-scale ship resistance and powering are made for antifouling coating systems with a range of roughness and fouling conditions. The estimates are based on results from laboratory-scale drag measurements and boundary layer similarity law analysis. In the present work, predictions are made for a mid-sized naval surface combatant at cruising speed and near maximum speed. The results indicate that slime films can lead to significant increases in resistance and powering, and heavy calcareous fouling results in powering penalties up to 86% at cruising speed. The present estimates show good agreement with results from full-scale ship power trials.
Photo Recall: Using the Internet to Label Your Photos

We describe a system for searching your personal photos using an extremely wide range of text queries, including dates and holidays ("Halloween"), named and categorical places ("Empire State Building" or "park"), events and occasions ("Radiohead concert" or "wedding"), activities ("skiing"), object categories ("whales"), attributes ("outdoors"), and object instances ("Mona Lisa"), and any combination of these -- all with no manual labeling required. We accomplish this by correlating information in your photos -- the timestamps, GPS locations, and image pixels -- to information mined from the Internet. This includes matching dates to holidays listed on Wikipedia, GPS coordinates to places listed on Wikimapia, places and dates to find named events using Google, visual categories using classifiers either pre-trained on ImageNet or trained on-the-fly using results from Google Image Search, and object instances using interest point-based matching, again using results from Google Images. We tie all of these disparate sources of information together in a unified way, allowing for fast and accurate searches using whatever information you remember about a photo. We quantitatively evaluate several aspects of our system and show excellent performance in all respects. Please watch a video demonstrating our system in action on a large range of queries at http://youtu.be/Se3bemzhAiY.
Application-driven bandwidth guarantees in datacenters

Providing bandwidth guarantees to specific applications is becoming increasingly important as applications compete for shared cloud network resources. We present CloudMirror, a solution that provides bandwidth guarantees to cloud applications based on a new network abstraction and workload placement algorithm. An effective network abstraction should enable applications to easily and accurately specify their requirements, while simultaneously enabling the infrastructure to provision resources efficiently for deployed applications. Prior research has approached the bandwidth guarantee specification by using abstractions that resemble physical network topologies. We present a contrasting approach of deriving a network abstraction based on application communication structure, called Tenant Application Graph or TAG. CloudMirror also incorporates a new workload placement algorithm that efficiently meets bandwidth requirements specified by TAGs while factoring in high availability considerations. Extensive simulations using real application traces and datacenter topologies show that CloudMirror can handle 40% more bandwidth demand than the state of the art (e.g., the Oktopus system), while improving high availability from 20% to 70%.
A Fast $O(N)$  Multiresolution Polygonal Approximation Algorithm for GPS Trajectory Simplification

Recent advances in geopositioning mobile phones have made it possible for users to collect a large number of GPS trajectories by recording their location information. However, these mobile phones with built-in GPS devices usually record far more data than needed, which brings about both heavy data storage and a computationally expensive burden in the rendering process for a Web browser. To address this practical problem, we present a fast polygonal approximation algorithm in 2-D space for the GPS trajectory simplification under the so-called integral square synchronous distance error criterion in a linear time complexity. The underlying algorithm is designed and implemented using a bottom-up multiresolution method, where the input of polygonal approximation in the coarser resolution is the polygonal curve achieved in the finer resolution. For each resolution (map scale), priority-queue structure is exploited in graph construction to construct the initialized approximated curve. Once the polygonal curve is initialized, two fine-tune algorithms are employed in order to achieve the desirable quality level. Experimental results validated that the proposed algorithm is fast and achieves a better approximation result than the existing competitive methods.
Computer Vision applied to autonomous robots using Raspberry Pi

The aim of this project is to discover new techniques to combine software and hardware components to be applied on an autonomous robot. The appearance of new cheap computational modules like Raspberry Pi and high-resolution cheap cameras makes it possible to combine both, applied to computer vision. This purpose has been achieved by exploring ways of object detection and navigation control using computer vision, low-cost computing devices and sending the path wirelessly via fast wireless network communication. This would favor the adoption of new low-cost autonomous robots in peoples everyday life. The system that I purpose in this paper is based in 3 hardware devices; one actuated robot with a Raspberry Pi, another equipped with another Raspberry Pi and a web camera and one with a smartphone running a user control app. The result is a fully working navigation system. This project is developed under the context of an educational final project and aimed also to reuse the implementation as a practicum material for a future use of the students.