Miracle-dz/UniFAR-FTUs huggingface dataset:
A Generalized Denoising Method with an Optimized Loss Function for Automated Bird Sound Recognition

In natural environments, bird sounds are often accompanied by background noise, so denoising becomes crucial to automated bird sound recognition. Recently, thanks to neural network embeddings, the deep clustering method has achieved better performances than traditional denoising methods, like filter-based methods, due to its ability to solve the problem when noise is in the same frequency range as bird sounds. In this paper, we propose a generalized denoising method based on deep clustering, which can process more complex recordings with less distortion. Also, we optimize the original affinity loss function to get a novel loss function to ensure the embedding vectors with the minimum distance belong to the same source, named Joint Center Loss (JCL), which can both increase the inter-class variance and decrease the intra-class variance of embeddings. Experiments are conducted on the gated convolutional neural network architecture and the bidirectional long short term memory architecture respectively with different loss functions. Given the signal-noise ratio being -3dB, the recognition accuracy increases relatively by 9.5% with the proposed denoising method in the best case, and the Relative Root Mean Square Error (RRMSE) increases relatively by 14.2% by using JCL, compared with the original affinity loss function AL.
Closed-Form Non-Singular Constant-Curvature Continuum Manipulator Kinematics

Continuum manipulators describe robotic arms which achieve motion by bending continuously along their length. This paper presents a kinematic parameterization of such arms, which avoids a problematic singularity common to previous approaches. It provides a closed-form and computationally efficient description of the kinematics of joint sections that are assumed to make circular arcs. This approach applies to a broad class of manipulator designs and does not depend on the number or position of actuators (e.g. tendons or artificial muscles). This parameterization has enabled real-time dynamics modeling of robotic manipulators composed of continuum joints.
Machine Learning Data Center Workloads Using Generative Adversarial Networks

In this paper we study the applicability of generative adversarial networks (GANs) for the description and generation of workloads for data centers. GANs are advanced neural networks that can learn complex likelihood functions and can sample from them. The field of workload modeling is concerned with describing and generating realistic workloads for performance evaluation of computer systems, in this paper, specifically for data centers. The characterization of the workload of modern data centers is crucial in order to study the effect of changing workloads on the performance of such data centers. Previously, a number of statistical fitting techniques have been used to characterize data center workloads. This paper explores whether GANs are sufficiently capable to automatically learn such characterisations from multidimensional data sets. We describe the design and evaluation of a GAN, thereby using real-world data center traces. The learned model is evaluated by comparison to previously proposed fitting techniques. We find that the resulting GAN is very well able to reproduce a realistic data center workload. Furthermore, the approach does not require (a priori) knowledge or assumptions about the underlying models themselves, which can be seen as an advantage. It is shown that the learning approach does reach comparable quality to other fitting techniques, although still at much higher computational costs.
A Model Reference Voltage Mode Control for Ripple Cancellation Buck Converter of Wide-Operation Range

In this paper, the design considerations of a discontinuous-conduction-mode buck converter with an output ripple cancellation circuit, which enables extra attenuation at a particular frequency, was presented as the power-stage topology. Meanwhile, a model reference voltage mode control (MRVMC), which preserves the stability margin at various working conditions, was proposed as loop control method for the power-stage. Finally, experimental verification was conducted to illustrate the effectiveness of the theoretical findings and design approaches.
Axioms for Learning from Pairwise Comparisons

To be well-behaved, systems that process preference data must satisfy certain conditions identiﬁed by economic decision theory and by social choice theory. In ML, preferences and rankings are commonly learned by ﬁtting a probabilistic model to noisy preference data. The behavior of this learning process from the view of economic theory has previously been studied for the case where the data consists of rankings. In practice, it is more common to have only pairwise comparison data, and the formal properties of the associated learning problem are more challenging to analyze. We show that a large class of random utility models (including the Thurstone–Mosteller Model), when estimated using the MLE, satisfy a Pareto efﬁciency condition. These models also satisfy a strong monotonicity property, which implies that the learning process is responsive to input data. On the other hand, we show that these models fail certain other consistency conditions from social choice theory, and in particular do not always follow the majority opinion. Our results inform existing and future applications of random utility models for societal decision making.
Unified Thread- and Data-Mapping for Multi-Threaded Multi-Phase Applications on SPM Many-Cores

Scratchpad Memories (SPMs) are more scalable than caches as they offer better performance with lower power and area overheads. This scalability advocates their suitability as on-chip memory in many-cores. However, SPM many-cores delegate the responsibility of thread- and data-mapping to the software. The mapping is especially challenging in the case of multi-threaded multi-phase applications. Threads from these applications exhibit both inter- and intra-phase data-sharing patterns. These patterns intricately intertwine thread- and data- mapping across phases. The accompanying qualitative mapping is the key to extract application performance on SPM many-cores.State-of-the-art framework for SPM many-cores performs thread- and data-mapping independently. Furthermore, it can only operate with single-phase multi-threaded applications. We are the first to propose in this work, a unified thread- and data-mapping framework for NoC-based SPM many-cores when executing multi-threaded multi-phase applications. Experimental evaluations show, on average, 1.36x performance improvement compared to the state-of-the-art framework for multi-threaded multi-phase applications.
Something’s Going on Here

Research on individual-difference factors predicting belief in conspiracy theories has proceeded along several independent lines that converge on a profile of conspiracy believers as individuals who are relatively untrusting, ideologically eccentric, concerned about personal safety, and prone to perceiving agency in actions and profundity in bullshit. The present research represents the first attempt at an integrative approach to testing the independent contributions of these diverse factors to conspiratorial thinking. Two studies (N = 1,253) found that schizotypy, dangerous-world beliefs, and bullshit receptivity independently and additively predict endorsement of generic (i.e., nonpartisan) conspiracy beliefs. Results suggest that “hyperactive” agency detection and political orientation (and related variables) might also play a role. The studies found no effects of situational threats (mortality salience or a sense of powerlessness) – though it remains to be seen whether real-world instantiations of situational threats might move some people to seek refuge in conspiratorial ideation.
Return migration of Atlantic Salmon in the River Tana: Phases of migratory behaviour

From a total of 174 multi-sea-winter Atlantic salmon radio tagged in the Tanafjord (northern Norway, 70° N) during 1992 and 1993, 48 Atlantic salmon were followed from entering the River Tana until spawning. Three phases were identified: (1) migratory, direct or stepwise migration to, or close to the position held at spawning; (2) search, movements both up and down river at or close to the position held at spawning; (3) holding, a period without movements prior to spawning. During the migratory phase, Atlantic salmon migrated directly to near the spawning area, or stopped between one and nine shorter periods during the upstream migration. Number of stops increased with increasing migratory distance in 1993, but no such correlation was found in 1992. The highest migratory speeds were recorded in the lower parts of the river. A distinct change in migratory pattern was found in 67% of the Atlantic salmon near or at the area held at spawning. Most common was a search phase of erratic movements with more than one down river movement. After the movement terminated, 96% of the Atlantic salmon had a period when no or little movement was recorded until spawning (on average 55 days in 1992 and 51 days in 1993). There was no preference for staying at, up or down river from the spawning area during this holding period. Early ascending Atlantic salmon migrated to spawning areas further from the mouth than the later arriving Atlantic salmon in 1993, but not in 1992. The proportion of time spent on the migratory phase increased, while the proportion of time spent on the holding phase decreased with increasing distance to the spawning area.
A Compact TM01-TE11 Mode Converter Designed with Octagon Printed Periodic Substrates

A compact TM01-TE11 mode converter designed with periodic substrates is presented in this paper. On each substrate an octagonal shape is printed on input side, of which the sides are connected with rectangular strips placed at an angle of 45⁰. Dielectric substrate FR4 (with εr = 4.4 and 0.025 loss tangent) is chosen for the proposed design. The fractional bandwidth of 4.92% (2.95 - 3.1 GHz) and maximum conversion efficiency up to 98.1% is achieved at the operating frequency of 3.05 GHz. Mode conversion efficiency is more than 90% over a bandwidth of 150 MHz at the operating frequency. The compact size and light weight of the proposed mode converter makes it suitable for airborne and portable High Power Microwave applications.
Efficient Hardware-Assisted Crash Consistency in Encrypted Persistent Memory

The persistent memory (PM) requires maintaining the crash consistency and encrypting data, to ensure data recoverability and data confidentiality. The enforcement of these two goals does not only put more burden on programmers but also degrades performance. To address this issue, we propose a hardware-assisted encrypted persistent memory system. Specifically, logging and data encryption are assisted by hardware. Furthermore, we apply the counter-based encryption and the cipher feedback (CFB) mode encryption to data and log respectively, reducing the encryption overhead. Our primary experimental results show that the transaction throughput of the proposed design outperforms the baseline design by up to 34.4%.
Conspecific body weight, food intake, and rumination time affect food processing and forage behavior

Abstract Much research has been directed at the chemical characteristics of plants that influence forage digestibility but there is less investigation into relationships between chewing and foraging in herbivores. The role of body size on forage processing, via chewing, and ingestive behavior is poorly understood in spite of associations between conspecific body size of ruminants and botanical and nutritional composition of their diets. I examined the implications from a relationship between body weight and absolute forage intake (kg/day) and no relationship between body weight and rumination time (h/day) on chewing behavior and feeding time (h/day). Cattle were selected as the model species because the diets presented offered benefits to digestion from increased chewing and because of the number of studies that measured body weight, daily food intake, dietary nutritional characteristics, feeding time, and rumination characteristics on the same individuals. Data were extracted from 24 studies of cattle (n ≤ 74). Absolute food intake scaled allometrically (0.75) with body weight but rumination time was unrelated to body weight, meaning smaller animals had more rumination time for a unit weight of ingesta. Correspondingly, small cattle chewed the weight-specific ingesta more and fed longer than large animals, probably because of a reduction in time of rumen turnover. These findings might be revealing as to how differences in digestion and foraging are associated with body size among conspecific animals of wild ruminant species.
Secrecy performance Analysis for Underlay Cognitive Radio Network with Optimal Antenna Selection and Generalized Receiver Selection

We have examined the physical layer security of an underlay cognitive radio network (CRN) consisting of <tex>$L$</tex> legitimate receivers (<tex>$S_{T_{X}}$</tex>), a secondary transmitter (<tex>$S_{T_{X}}$</tex>), an eavesdropper (Eve), and a primary user (PU). All <tex>$S_{R_{X}}$</tex> and the PU are equipped with a single antenna while the <tex>$S_{T_{X}}$</tex> and Eve are implemented with multiple antennas. The optimal antenna selection scheme (OAS) is adopted at the <tex>$S_{T_{X}}$</tex>, which maximizes the secrecy capacity while a generalized receiver selection scheme is adopted at the <tex>$S_{R_{X}}$</tex>. The <tex>$S_{T_{X}}$</tex> sends information to the <tex>$S_{R_{X}}$</tex> with the <tex>$l^{th}$</tex> best signal to noise ratio (SNR). The concise expressions for exact and asymptotic secrecy outage probability (SOP) for an arbitrary number of <tex>$S_{R_{X}}$</tex>, number of transmit antenna <tex>$N_{A}$</tex>, and number of eavesdropper antenna <tex>$N_{E}$</tex> have derived.
Long-term Continuous Assessment of SRAM PUF and Source of Random Numbers

The qualities of Physical Unclonable Functions (PUFs) suffer from several noticeable degradations due to silicon aging. In this paper, we investigate the long-term effects of silicon aging on PUFs derived from the start-up behavior of Static Random Access Memories (SRAM). Previous research on SRAM aging is based on transistor-level simulation or accelerated aging test at high temperature and voltage to observe aging effects within a short period of time. In contrast, we have run a long-term continuous power-up test on 16 Arduino Leonardo boards under nominal conditions for two years. In total, we collected around 175 million measurements for reliability, uniqueness and randomness evaluations. Analysis shows that the number of bits that flip with respect to the reference increased by 19.3% while min-entropy of SRAM PUF noise improves by 19.3% on average after two years of aging. The impact of aging on reliability is smaller under nominal conditions than was previously assessed by the accelerated aging test. The test we conduct in this work more closely resembles the conditions of a device in the field, and therefore we more accurately evaluate how silicon aging affects SRAM PUFs.
Towards Transforming Natural Language Queries into SPARQL Queries

Ontology Based Data Access (OBDA) improves knowledge sharing and reuse and grants a set of options to enhance the power of knowledge management. In OBDA systems, user queries can be expressed in SPARQL. This prevents ordinary users from formulating their own queries. For such lay users, it should be possible to express queries in their own languages and terms instead of being forced to learn SPARQL. To enable this, a transformation from a natural language to SPARQL is needed. To cope with such translation, several challenges, such as natural language processing, ontology handling, string matching, and SPARQL query building should be addressed. In this PhD study, we will use natural language processing techniques and investigate the role of user involvement in the query translation process. Also, string-matching algorithms will be used. We will start with the processing of the natural query. Then the mapping between the query entities and corresponding entities in the datasets. The user interaction validating and confirming this mapping should take place. Finally, building the SPARQL query. The proposed approach will be evaluated based on a bench mark by means of query efficiency and accuracy.
An Effective Feature Selection With Generative Adversarial Network (GAN) Model For Stock Market Prediction

: At present days, the prediction of stock prices is very complicated as it depends on various factors. Several researches have been done to foresee the stock prices which will be helpful for users to identify the direction of stock price movement. Recently, machine learning and bio-inspired algorithms are employed for precise Stock market prediction (SMP). Though classification methods perform wells on SMP, the presence of numerous factors in the stock process decreases the efficiency of the applied classification algorithm. So, feature selection methods are applied to reduce the computation complexity and enhance the classification accuracy of SMP. This paper projects a novel new feature selection based classification model for forecasting the stock prices in an effective way. The proposed algorithm employs particle swarm optimization (PSO) algorithm to select the features properly which will enhance the classifier performance. In addition, we employ a novel architecture of Generative Adversarial Network (GAN) based classifier for forecasting the closing price of stocks. The presented model is validated using Borsa Istanbul Index (BIST) dataset. The simulation outcome showed that the projected model is the compared methods under several aspects.
Cornac: A Comparative Framework for Multimodal Recommender Systems

Cornac is an open-source Python framework for multimodal recommender systems. In addition to core utilities for accessing, building, evaluating, and comparing recommender models, Cornac is distinctive in putting emphasis on recommendation models that leverage auxiliary information in the form of a social network, item textual descriptions, product images, etc. Such multimodal auxiliary data supplement user-item interactions (e.g., ratings, clicks), which tend to be sparse in practice. To facilitate broad adoption and community contribution, Cornac is publicly available at https://github.com/PreferredAI/cornac , and it can be installed via Anaconda or the Python Package Index (pip). Not only is it well-covered by unit tests to ensure code quality, but it is also accompanied with a detailed documentation 1 , tutorials, examples, and several built-in benchmarking data sets.
Audio and Video Transmission Using Visible Light Transmission

Visible Light Communication (VLC) is the term which was the method that uses visible light for Data transmissions. Due to the insufficient Radio Frequency(RF) resources, and the limitations in bandwidth, the demand for faster data transmission becomes a big problem to be solved. This problem is rectified by the emerging Li-Fi technology which provides faster data transmission with a more secure environment. Therefore, VLC became an emerging technology and was included in 5g. This paper focuses on audio and video transmissions in VLC methodology using Li-Fi (light fidelity) module. A real-time transmitter and receiver system to check the performance and verify the audio and video transmission using Li-Fi under varying conditions such as distance, intensity, and quality. The main Objective is to design a transmitter and receiver using a Li-Fi setup for audio and video transfer and to test the setup under varying conditions. The use of VLC has gained particular interest due to its fast data rates and traffic overloading techniques. The main advantage of VLC is quick data rates with minimum power consumption, and small setup cost makes it as an emerging technology that can be included and used in upcoming technologies such as 5g networks.
Cross-Sectional Gender Comparison of Gray Matter Concentration in Prodromal Huntington’s Disease

Huntington’s disease (HD) is a neurodegenerative disorder caused by an abnormal repetition of the cytosine-adenine-guanine (CAG) trinucleotide in the HTT gene. This polyglutamine mutation causes behavioral, cognitive, and motor abnormalities such as chorea. Additionally, HD is characterized by striatal atrophy prior to symptom onset. This experiment investigated gender contributions in pre-diagnosed or prodromal HD stages as well as striatal effect on motor dysfunction. All prodromal HD participants were positive for the genetic mutation. This crosssectional study correlated striatal volume between men and women at different stages of prodromal HD. It was expected to see greater gender differences with increasing prodromal HD stages due to past HD gender studies. Structural Magnetic Resonance (sMRI) data were collected from more than a thousand cans and decomposed into specific regions via independent component analysis. Regions within the striatum became gray matter profiles of interest. Statistical analyses were conducted to quantify a relationship between gender and stage as well as striatal effect on motor dysfunction. Gender did not correlate with striatal volume at different prodromal stages yet striatal volume correlated with motor abnormalities as expected. Additionally, striatal volume decreased with increasing prHD stages. The results supported previous findings associating striatal volume and prHD stages, yet gender contributions were not identified. A study limitation is the use of a cross sectional analysis; interactions between gender and rate of striatal atrophy have yet to be studied longitudinally in a prodromal HD cohort. Indexing prodromal progression factors could enhance therapeutic targets by isolating the earliest-affected regions of the brain in both sexes, which is vital for future treatment.