Miracle-dz/UniFAR-FTUs huggingface dataset:
A formant tracking LP model for speech processing

Formant estimation becomes complicated in the presence of correlated background noise such as car and train noise as the spectrum of noise from revolving mechanical sources have their own spectral peaks that affect the number and positions of the observed peaks in noisy speech spectrum. This paper investigates the modeling and estimation of spectral parameters at formants of noisy speech in the presence of car and train noise. Formant estimation using two-dimensional hidden Markov models (2D-HMM) is reviewed and employed to study the influence of noise on observations of formants. The first set of experimental results presented show the influence of car and train noise on the distribution and the estimates of the formant trajectories. Due to the shapes of the spectra of speech and car/train noise, the 1 st formant is most affected by noise and the last formant is least affected. The effects of inclusion of formant features in speech recognition at different SNRs are presented. It is shown that formant features provide better performance at low SNRs compared to MFCC features. Finally, for robust estimation of noisy speech, a formant tracking method based on combination of LP-spectral subtraction and Kalman filter is presented. Average formant tracking errors at different SNRs are computed and the results show that after noise reduction the formant tracking errors of 1 st formant are reduced by 60%. The de-noised formant tracking LP models can be used for recognition and/or enhancement of noisy speech. This paper concentrates on formant estimation for speech in noisy car/train environments. In section 2 the extraction of formant features and the probabilistic modeling of variation of formant features are reviewed. Section 3 investigates the effect of noise on formant models. In section 4 a method, for robust estimation of formants of noisy speech, based on a combination of LP-spectral subtraction and Kalman filter is
Cliffhanger: Scaling Performance Cliffs in Web Memory Caches

Web-scale applications are heavily reliant on memory cache systems such as Memcached to improve throughput and reduce user latency. Small performance improvements in these systems can result in large end-to-end gains. For example, a marginal increase in hit rate of 1% can reduce the application layer latency by over 35%. However, existing web cache resource allocation policies are workload oblivious and first-come-first-serve. By analyzing measurements from a widely used caching service, Memcachier, we demonstrate that existing cache allocation techniques leave significant room for improvement. We develop Cliffhanger, a lightweight iterative algorithm that runs on memory cache servers, which incrementally optimizes the resource allocations across and within applications based on dynamically changing workloads. It has been shown that cache allocation algorithms underperform when there are performance cliffs, in which minor changes in cache allocation cause large changes in the hit rate. We design a novel technique for dealing with performance cliffs incrementally and locally. We demonstrate that for the Memcachier applications, on average, Cliffhanger increases the overall hit rate 1.2%, reduces the total number of cache misses by 36.7% and achieves the same hit rate with 45% less memory capacity.
Equivalence between RAM-based neural networks and probabilistic automata

In this letter, the computational power of a class of random access memory (RAM)-based neural networks, called general single-layer sequential weightless neural networks (GSSWNNs), is analyzed. The theoretical results presented, besides helping the understanding of the temporal behavior of these networks, could also provide useful insights for the developing of new learning algorithms.
Social Network Analysis for Judgment Aggregation Paper 487

Judgment aggregation investigates the problem of how to aggregate several individuals’ judgments on some logically connected propositions into a consistent collective judgment. The majority of work in judgment aggregation is devoted to studying impossibility results, but the (social) dependencies that may exist between voters is traditionally not studied. In this paper, we use techniques from social network analysis to study the relations between the individuals participating in a judgment aggregation problem by analysing the similarity between their judgments in terms of social networks. We show that using these techniques we obtain a more finegrained approach than the prototype voter rule in addition to identifying closely related groups of agents sharing some common interests concerning the given issues. We provide both theoretical and empirical results supporting our claims, in addition to analysing the complexity of the approach adopted.
Numerical Modeling on the Stress-Strain Response and Fracture of Modeled Recycled Aggregate Concrete

According to the nanoindentation tests, the constitutive relationship of the Interfacial Transition Zones (ITZs) in Recycled Aggregate Concrete (RAC) is proposed with a plastic-damage constitutive model. Based on the meso/micro-scale constitutive relations of mortar matrix, numerical studies were undertaken on Modeled Recycled Aggregate Concrete (MRAC) under uniaxial loadings to predict mechanical behavior, particularly the stress-strain response. The tensile stress tends to concentrate in the ITZs region, which leads to the development of microcracks. After the calibration and validation with experimental results, the effects of the mechanical properties of ITZs and new mortar matrix on the stress-strain response and fracture of MRAC were analyzed. The FEM modeling is capable of simulating the complete stress-strain relationship of MRAC, as well as the overall fracture pattern. It reveals that the mechanical properties of new mortar matrix and the corresponding new ITZ play a significant role in the overall stress-strain response and fracture process of MRAC.
Environmental Determinants of Vibrio cholerae Biofilm Development

ABSTRACT Vibrio cholerae is a versatile bacterium that flourishes in diverse environments, including the human intestine, rivers, lakes, estuaries, and the ocean. Surface attachment is believed to be essential for colonization of all of these natural environments. Previous studies have demonstrated that the vps genes, which encode proteins required for exopolysaccharide synthesis and transport, are required for V. cholerae biofilm development in Luria-Bertani broth. In this work, we showed that V. cholerae forms vps-dependent biofilms and vps-independent biofilms. The vps-dependent and -independent biofilms differ in their environmental activators and in architecture. Our results suggest that environmental activators of vps-dependent biofilm development are present in freshwater, while environmental activators of vps-independent biofilm development are present in seawater. The distinct environmental requirements for the two modes of biofilm development suggest that vps-dependent biofilm development and vps-independent biofilm development may play distinct roles in the natural environment.
Greed is Good if Randomized: New Inference for Dependency Parsing

Dependency parsing with high-order features results in a provably hard decoding problem. A lot of work has gone into developing powerful optimization methods for solving these combinatorial problems. In contrast, we explore, analyze, and demonstrate that a substantially simpler randomized greedy inference algorithm already suffices for near optimal parsing: a) we analytically quantify the number of local optima that the greedy method has to overcome in the context of first-order parsing; b) we show that, as a decoding algorithm, the greedy method surpasses dual decomposition in second-order parsing; c) we empirically demonstrate that our approach with up to third-order and global features outperforms the state-of-the-art dual decomposition and MCMC sampling methods when evaluated on 14 languages of non-projective CoNLL datasets. 1
Competitive and fair throughput for co-existing networks under adversarial interference

This paper initiates the formal study of a fundamental problem: How to efficiently allocate a shared communication medium among a set of K co-existing networks in the presence of arbitrary external interference? While most literature on medium access focuses on how to share a medium among nodes, these approaches are often either not directly applicable to co-existing networks as they would violate the independence requirement, or they yield a low throughput if applied to multiple networks. We present the randomized medium access (MAC) protocol COMAC which guarantees that a given communication channel is shared fairly among competing and independent networks, and that the available bandwidth is used efficiently. These performance guarantees hold in the presence of arbitrary external interference or even under adversarial jamming. Concretely, we show that the co-existing networks can use a Ω(ε2 min{ε, 1 poly(K)})-fraction of the non-jammed time steps for successful message transmissions, where ε is the (arbitrarily distributed) fraction of time which is not jammed.
A New In Situ Microscopy Approach to Study the Degradation and Failure Mechanisms of Time‐Dependent Dielectric Breakdown: Set‐Up and Opportunities

The time dependent dielectric breakdown (TDDB) in copper/ultra‐low‐k on‐chip interconnect stacks of integrated circuits has become one of the most critical reliability concerns in recent years. In this paper, a novel experimental in situ microscopy approach using transmission X‐ray microscopy (TXM) and scanning transmission electron microscopy (STEM) is proposed to study TDDB degradation and failure mechanisms. It combines electrical testing and imaging techniques. Low‐dose bright field (BF) STEM inserting a small condenser aperture is chosen to reduce the beam damage of the dielectric material, while the electron spectroscopic imaging technique is used for the chemical analysis to detect the migration path of Cu atoms. This new experimental approach will contribute to an improved understanding of the TDDB effect.
Understanding Substrate Features Influenced by Pretreatments that Limit Biomass Deconstruction by Enzymes

Conflict between dwindling reserves of fossil energy and growing consumption presents significant challenges to our society. To shift society's dependence away from petroleum to renewable energy, biorefineries are needed that employ advances in genetics, biotechnology, process chemistry, and engineering to convert biomass to valuable fuels and other products. Lignocellulosic biomass is the only sufficiently abundant source of renewable carbon that can support large-scale sustainable and economic production of transportation fuels and industrial chemicals. However, lignocellulosic biomass is naturally resistant to microbial or enzymatic deconstruction, a characteristic collectively termed biomass recalcitrance, and overcoming this resistance is the main hurdle to realizing low costs. A better understanding of relationships among biomass recalcitrance, pretreatment, and enzymatic hydrolysis can accelerate this quest. Against this background, this thesis seeks to understand the influence of pretreatment on substrate features and how such features affect subsequent enzymatic hydrolysis. First, application of a high throughput pretreatment and co-hydrolysis system (HTPH) was extended to dilute acid pretreatment to facilitate development of a novel two-stage pretreatment strategy that sought to achieve high cellulose digestibility and total sugar yields while keeping hemicellulose degradation low. Along with this, the feasibility of recycling liquid for low temperature pretreatment was demonstrated. Inspired by the HTPH screening results, a new method was developed for simple and rapid quantification of the hemicellulose sugar content in biomass. Finally, compositional and structural features of solids produced by application of two-stage pretreatment to switchgrass and three leading pretreatment technologies to corn stover were compared to provide new insights into control of biomass recalcitrance, as well as how pretreatments with much different deconstruction patterns impact enzymatic hydrolysis.
High-performance medical image registration using new optimization techniques

Optimization of a similarity metric is an essential component in intensity-based medical image registration. The increasing availability of parallel computers makes parallelizing some registration tasks an attractive option to increase speed. In this paper, two new deterministic, derivative-free, and intrinsically parallel optimization methods are adapted for image registration. DIviding RECTangles (DIRECT) is a global technique for linearly bounded problems, and multidirectional search (MDS) is a recent local method. The performance of DIRECT, MDS, and hybrid methods using a parallel implementation of Powell's method for local refinement, are compared. Experimental results demonstrate that DIRECT and MDS are robust, accurate, and substantially reduce computation time in parallel implementations
Vote and Aggregation in Combinatorial Domains with Structured Preferences

In many real-world collective decision problems, the set of alternatives is a Cartesian product of finite value domains for each of a given set of variables. The prohibitive size of such domains makes it practically impossible to represent preference relations explicitly. Now, AI has been developing languages for representing preferences on such domains in a succinct way, exploiting structural properties such as conditional preferential independence. Here we reconsider voting and aggregation rules in the case where voters' preferences have a common preferential independence structure, and address the decompossition a voting rule or an aggregation function following a linear order over variables.
‘Does Religion Cause Health?’: Differing Interpretations and Diverse Meanings

The question, ‘Does religion (or spirituality) cause physical health benefits? ’ may be given at least four diverging interpretations in terms of causal path diagrams. In common usage, the question may be interpreted to indicate that religion causally influences health by: (1) any mechanism, including well-established factors such as social support and improved health behaviors; (2) additional mechanisms, such as enhanced positive psychological states (e.g. faith, hope, inner peace) acting through psychoneuroimmunologic or psychoneuroendocrinologic pathways; (3) offering psychological strength for acquiring or maintaining positive health behaviors; or (4) causally influencing health by distant healing or intercessory prayer. We review historical confusion between these interpretations, arguing that disentangling them is important for collaborative health care, promotion and research.
Analysis of low-resource acoustic model self-training

Previous work on self-training of acoustic models using unlabeled data reported significant reductions in WER assuming a large phonetic dictionary was available. We now assume only those words from ten hours of speech are initially available. Subsequently, we are then given a large vocabulary and then quantify the value of repeating selftraining with this larger dictionary. This experiment is used to analyze the effects of self-training on categories of words. We report the following findings: (i) Although the small 5k vocabulary raises WER by 2% absolute, self-training is equally effective as using a large 75k vocabulary. (ii) Adding all 75k words to the decoding vocabulary after self-training reduces the WER degradation to only 0.8% absolute. (iii) Self-training most benefits those words in the unlabeled audio but not transcribed by a wide margin.
Aalborg Universitet Does size matter ? Investigating the impact of mobile phone screen size on users ’ perceived usability , effectiveness and

Given the wide adoption of smartphones, an interesting debate is taking place regarding their optimal screen size and specifically whether possible portability issues counterbalance the obvious benefits of a larger screen. Moreover, the lack of scientific evidence about the concrete impact of mobile phones’ screen size on usability raises questions both to practitioners and researchers. In this paper, we investigate the impact of a mobile phone’s screen size on users’ effectiveness, efficiency and perceived usability as measured using System Usability Scale (SUS). An experiment was conducted with 60 participants, which interacted with the same information seeking application on three different devices of the same brand that differed on their screen size. A significant effect of screen size on efficiency was derived, leading to an important finding that users who interact with larger than 4.3in screens are more efficient during information seeking tasks. Author
Identifying important factors in deterministic investment problems using design of experiments

For large investment projects, sensitivity analysis is an important tool to determine which factors need further analysis and/or can jeopardize the future of a project. In practice, reliable information on the joint probability distribution of factors affecting the investment is mostly lacking, so a stochastic analysis is not possible. The paper analyzes how and to what extent statistical design of experiments in combination with regression meta modeling can be helpful in finding important factors in deterministic models; information that is useful to decision makers.
wildCENSE: GPS based animal tracking system

wildCENSE is a Wireless Sensor Network (WSN) system which attempts to monitor the behaviour and migration patterns of Barasingha (Swamp Deer). The system would collect the micro-climatic as well as positional information of the animal and communicate it to a base station through flooding of data using peer-to-peer network. The base station, using a gateway, will upload all the collected data to a database server on Internet and portray the information using browser based visualization software. Each node would monitor five parameters namely position (using GPS), temperature, humidity, head orientation and ambient light. Also, the node will have a real time clock for the synchronization of the network and to keep timing information. An external data flash memory would be used to record the data collected from sensors and other peer nodes. A radio transceiver would transmit the data to the base station by using a peer to peer communication protocol. A solar energy harvesting system for recharging nodepsilas power supply batteries is being added to prolong the lifetime of nodes. The system would be integrated in the form of a collar that can be easily fitted on the neck of animal.
Enlarged-taper tailored Fiber Bragg grating with polyvinyl alcohol coating for humidity sensing

In this paper, a novel optical fiber sensor based on an enlarged-taper tailored fiber Bragg grating (FBG) is proposed and experimentally demonstrated for the measurement of relative humidity. The enlarged-taper works as a multifunctional joint that not only excites cladding modes but also recouples the cladding modes reflected by the FBG back into the leading single mode fiber. Due to the fact that cladding modes have a strong evanescent field penetrating into the ambient medium, the intensity of the reflected cladding modes is greatly influenced by the refractive index (RI) of the ambient medium. Polyvinyl alcohol (PVA) film is plated on the fiber surface by dip-coating technique, as a humidity-to-refractive index transducer, whose RI variance from 1.49 to 1.34 when the ambient humidity increases from 20%RH to 95%RH. The relative humidity response of the sensing structure is investigated in our home-made humidity chamber with a commercial hygrometer. By monitoring the intensity of the reflected cladding modes, the RH variance can be demodulated. Experimental results show that RH sensitivity depends on the RH value, and a sensitivity up to 1.2 dB/%RH can be achieved within the RH range of 30-90%. A fast and reversible time response has also been investigated. Such a probe-type and reusable fiber-optic RH sensor is a very promising technology for biochemical sensing applications, e.g., breath analysis, chemical reaction monitoring.
Disorders of calcium and phosphate metabolism in childhood and adolescence.

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Video change detection in web processing service

In this paper, we design and implement architecture for video change detection system using Web Processing Service (WPS) based on 52° North in Sensor Web Enablement (SWE). In this system, sensors obtain video data and Sensor Observation Service (SOS) provides video data to WPS and WPS processes this data. To realize a flexible WPS, a standards-based and service-oriented WPS architecture is designed. The architecture consists of three layers: Processing Engine layer, Algorithms Center layer, Date Service Middleware layer. The core layer in our system is Algorithms Center layer. To prove the proposed method, we take an implementation of video change detection as an example, and detail the video change detection algorithm wrapped in WPS. The test results demonstrate that the proposed approach is feasible and stable.
Sum-rate distortion bound for suboptimal multiterminal source coding applied in medical wireless sensor networks

In this paper, we derive the suboptimal sum-rate distortion bound for encoding correlated Gaussian sources for which the uniform scalar quantizers are used to convert the signals from analog to the discrete form. The correlated discrete signals are further compressed using the lossless Slepian and Wolf encoder. The gap between the suboptimal bound and the inner/outer bounds is also investigated. The result shows that the gap is at most 0.255 bits/sample/source which is far less than the universal bound of 2.45 bits/sample given in [1] for the three tree-structured Gaussian sources.
Constraining rooting depths in tropical rainforests using satellite data and ecosystem modeling for accurate simulation of gross primary production seasonality

Accurate parameterization of rooting depth is difficult but important for capturing the spatio‐temporal dynamics of carbon, water and energy cycles in tropical forests. In this study, we adopted a new approach to constrain rooting depth in terrestrial ecosystem models over the Amazon using satellite data [moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI)] and Biome‐BGC terrestrial ecosystem model. We simulated seasonal variations in gross primary production (GPP) using different rooting depths (1, 3, 5, and 10 m) at point and spatial scales to investigate how rooting depth affects modeled seasonal GPP variations and to determine which rooting depth simulates GPP consistent with satellite‐based observations. First, we confirmed that rooting depth strongly controls modeled GPP seasonal variations and that only deep rooting systems can successfully track flux‐based GPP seasonality at the Tapajos km67 flux site. Second, spatial analysis showed that the model can reproduce the seasonal variations in satellite‐based EVI seasonality, however, with required rooting depths strongly dependent on precipitation and the dry season length. For example, a shallow rooting depth (1–3 m) is sufficient in regions with a short dry season (e.g. 0–2 months), and deeper roots are required in regions with a longer dry season (e.g. 3–5 and 5–10 m for the regions with 3–4 and 5–6 months dry season, respectively). Our analysis suggests that setting of proper rooting depths is important to simulating GPP seasonality in tropical forests, and the use of satellite data can help to constrain the spatial variability of rooting depth.