Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of High-Speed Videoendoscopy for Bayesian Inference on Reduced Order Vocal Fold Models

The ability to use our voice occurs through a complex bio-mechanical process known as phonation. The study of this process is interesting, not only because of the complex physical phenomena involved, but also because of the presence of phonation disorders that can make the everyday task of using ones voice difficult. Clinical studies of phonation aim to help diagnose such disorders using various measurement techniques, such as microphone recordings, video of the vocal folds, and perceptual sound quality measures. In contrast, scientific investigations of phonation have focused on understanding the physical phenomena behind phonation using simplified physical and numerical models constructed using representative population based parameters. A particularly useful type of model, reduced-order numerical models, are simplified representations of the vocal folds with low computational complexity that allow broad parameter changes to be investigated. To bring the physical understanding of phonation from these models into clinical usage, it is necessary to have patient specific parameters. Due to the difficulty of measuring vocal fold parameters and other structures in phonation directly, inverse analysis techniques must be employed. These techniques estimate the parameters of a model, by finding model parameters that lead to outputs of the model which compare well with measured outputs. With the measured outputs being patient specific measurements, these techniques can produce patient specific model parameters. However, this is complicated by the fact that measurements are uncertain, which leads to uncertainty in inferred parameters. The uncertainty in the parameters provides a way to judge how confident clinicians should be in using them. Large measurements errors could result in high uncertainties (and vice versa), which should guide clinicians on whether or not to believe the estimated parameters. Bayesian inference is an inverse analysis technique, that can take into account the inherent uncertainty in measurements in a probabilistic framework. Applying Bayesian inference to reduced-order models and clinical measurements allows patient specific model parameters with associated uncertainties to be inferred. A promising clinical measurement for use in Bayesian inference is high-speed videoendoscopy, in which high-speed video is taken of the vocal folds in motion. This captures the time varying motion of the vocal folds, which allows many quantitative measurements to be derived from the resulting video, for example the glottal width (distance between the vocal folds) or glottal area (area between the vocal folds). High-speed videoendoscopy is subject to variable imaging parameters, in particular the frame rate, spatial resolution, and tilted views of the camera can all modify the resulting video of the vocal folds, changing the uncertainty in the derived measurements. To investigate the effect of these three imaging parameters on Bayesian inference applied to high-speed video endoscopy, a simulated
Formalisation of Additive Combinatorics in Isabelle/HOL (Invited Talk)

In this talk, I will present an overview of recent formalisations, in the interactive theorem prover Isabelle/HOL, of significant theorems in additive combinatorics, an area of combinatorial number theory. The formalisations of these theorems were the first in any proof assistant to my knowledge. For each of these theorems, I will discuss selected aspects of the formalisation process, focussing on observations on our treatment of certain mathematical arguments when translated into Isabelle/HOL and our overall formalisation experience with Isabelle/HOL for this area of mathematics. 2012 ACM Subject Classification Mathematics of computing → Combinatorics; Theory of computation → Logic and verification
Studi Numerik Steady RANS Aliran Fluida di Dalam Asymmetric Diffuser

Research on fluid flow becomes a necessity to develop technology and for the welfare of human beings on earth. One of them is study of fluid flow in the diffuser. The example of diffuser application is used as a flue gas duct in the car or motorcycle. In addition, diffuser is also applied in air conditioning systems. Diffuser is a construction that able to control the behavior of the fluid. The increasing of cross section area in the diffuser will generate a positive pressure gradient or also called adverse pressure gradient (APG). The greater APG that happens, the greater energy required by the fluid to fight it, because APG will lead to separation. This study aimed to evaluate the numerical fluid flow in the asymmetric diffuser with divergence angle (θ) = 10 ° (upper wall) and widening one vertical side (α) of 20 ° (front wall). The Reynolds number is 8.7 x 104 by high inlet diffuser and the maximum velocity at the inlet diffuser. Turbulence models used are standard k-ɛ, realizable k-e, and shear stress transport (SST) k-ω. Numerical study of steady RANS used Fluent 6.3. 26 software. Results of numerical visualizations show that huge vortex established in diffuser, that’s why performance of diffuser is not optimal. In addition the location of separation point shown by SST k-ω is earlier than other turbulence models (standard k-e and realizable k-e).
Unifying Question Answering and Text Classification via Span Extraction

Even as pre-trained language encoders such as BERT are shared across many tasks, the output layers of question answering and text classification models are significantly different. Span decoders are frequently used for question answering and fixed-class, classification layers for text classification. We show that this distinction is not necessary, and that both can be unified as span extraction. A unified, span-extraction approach leads to superior or comparable performance in multi-task learning, low-data and supplementary supervised pretraining experiments on several text classification and question answering benchmarks.
Differences in Growth Hormone and Prolactin Secretion Associated with Environmental Temperature and Energy Intake

Summary The combined effects of environmental tem perature and level of energy intake on plasma concen trations of growth hormone (GH) and prolactin (PRL) have been investigated in 14 week old pigs acclimated to 35 or 10 °C on a high (H) or low (L) energy intake (H = 2L). Measurements were made at 15 min intervals between 08.00 and 18.00 hours, after feeding at 17.00 hours on the previous day. Mean values of GH were greater in pigs on the L than H intake and there was a tendency for values to be higher at 35 than 10 °C. However, there was wide in dividual vaiation within each treatment group and the dif ferences were not statistically significant. Mean PRL con centrations were greater at 35 than 10 °C (P < 0.05). It is concluded that circulating levels of plasma GH do not have a major role in maintaining the differences in growth and morphology of young pigs kept in widely different en vironmental conditions. However, these differences could be related at least in part to the GH-like properties of PRL.
A HYBRID NUMERICAL/EXPERIMENTAL STUDY OF THE AERODYNAMIC NOISE PREDICTION

An accurate noise prediction is important in order to reduce noise emission significantly and to prevent expensive after-design treatments. This study aims to examine the aerodynamics and aeroacoustics performance of an open system consisting of an axial fan and a heat exchanger where hybrid method incorporating CFD (Computational Fluid Dynamics) and CAA (Computational Aeroacoustics) is used to predict the noise behavior. The hybrid model method used consists of three steps. Firstly, the flow is computed by means of flow-computed fluids and the pressure fluctuations are obtained. This is followed by the acquisition of acoustic signals from these fluctuations and the attainment of a sound pressure level approach with the FW-H (Ffowcs Williams & Hawkings) model. Unsteady flow field of the air channel case was obtained by using different turbulence models. The SAS model is capable of resolving largescale turbulent structures without the time and grid-scale resolution restrictions of LES (Large Eddy Simulations), often allowing the use of existing grids created for URANS simulations. For this reason, two different turbulence models, namely URANS (Unsteady Reynolds Averaged Navier Stokes) model, SAS (Scale Adaptive Simulations) model have been applied. Acoustic sources were computed based on the pressure fluctuations and sound pressure level and frequency dependent graphics were plotted with Fast Fourier Transform. On the other hand, acoustic measurements were performed in a semi-anechoic chamber for both of them. When the experimental and numerical results were compared with the previously determined receiver points, the accuracy rate was obtained as SAS, URANS respectiv ely.
POSIX Lexing with Bitcoded Derivatives

Sulzmann and Lu describe a lexing algorithm that calculates Brzozowski derivatives using bitcodes annotated to regular expressions. Their algorithm generates POSIX values which encode the information of how a regular expression matches a string – that is, which part of the string is matched by which part of the regular expression. This information is needed in the context of lexing in order to extract and to classify tokens. The purpose of the bitcodes is to generate POSIX values incrementally while derivatives are calculated. They also help with designing an “aggressive” simplification function that keeps the size of derivatives finitely bounded. Without simplification the size of some derivatives can grow arbitrarily big, resulting in an extremely slow lexing algorithm. In this paper we describe a variant of Sulzmann and Lu’s algorithm: Our variant is a recursive functional program, whereas Sulzmann and Lu’s version involves a fixpoint construction. We (i) prove in Isabelle/HOL that our variant is correct and generates unique POSIX values (no such proof has been given for the original algorithm by Sulzmann and Lu); we also (ii) establish finite bounds for the size of our derivatives.
Search for narrow high-mass resonances in proton-proton collisions at 8 TeV decaying to a Z and a Higgs boson

We present a search in the CMS detector for an exotic high-mass and narrow resonance decaying into a Z and a Higgs from standard model in the final state with a pair of tau leptons and a pair of quarks. We analyze 19.7/fb of integrated luminosity of proton-proton collisions at √ s = 8 TeV from LHC. In the resonance mass range of interest (0.8− 2.5 TeV), the Z and Higgs bosons are produced with large momentum compared with their masses, which implies that the final products of the two quarks and the two tau leptons must be detected within a small angular separation. Jet substructure techniques are used to identify the boosted Z boson decaying hadronically and a modified approach is considered to reconstruct very collimated pairs of tau leptons from boosted Higgs decay. From a combination of all possible decay modes of the tau lepton, heavy spin1 resonances production cross sections are excluded at 95% C.L. in a range between 0.9 and 27.8 fb, depending on the resonance mass.
Underwater minirobots actuated by hybrid driving method

Underwater minirobots have attracted significant interest due to their value in complex application scenarios. Typical underwater minirobots are driven mainly by a soft or rigid actuator. However, soft actuation is currently facing challenges, including inadequate motional control accuracy and the lack of a continuous and steady driving force, while conventional rigid actuation has limited actuation efficiency, environmental adaptability, and motional flexibility, which severely limits the accomplishment of complicated underwater tasks. In this study, we developed underwater minirobots actuated by a hybrid driving method (HDM) that combines combustion-based actuators and propeller thrusters to achieve accurate, fast, and flexible underwater locomotion performance. Underwater experiments were conducted to investigate the kinematic performance of the minirobots with respect to the motion modes of rising, drifting, and hovering. Numerical models were used to investigate the kinematic characteristics of the minirobots, and theoretical models developed to unveil the mechanical principle that governs the driving process. Satisfactory agreement was obtained from comarisons of the experimental, numerical, and theoretical results. Finally, the HDM was compared with selected hybrid driving technologies in terms of acceleration and response time. The comparison showed that the minirobots based on HDM were generally superior in transient actuation ability and reliability.
Some properties of nonlinear Lanchester equations with an application in military

ABSTRACT There have been many research literature on traditional direct fire combat modelling. Recently, network centric warfare (NCW) is an active research topic, in which information plays more important role than in the traditional warfare. It can be easily agreed that the use of information affects the combat results greatly. However, it is not straightforward to measure the effect of the information, thus decision making involving the impact of information during combat is a non-trivial task. In this study, we propose a simple model for NCW modified from the original Lanchester differential equation, which can be used as a basic model for analysing characteristics of NCW. We derive some useful properties of the model in a special case. In order to solve the optimal fire allocation decision under this model in general case, we propose an algorithm based on reinforcement learning, followed by numerical examples.
Semi-Supervised First-Person Activity Recognition in Body-Worn Video

Author(s): Chen, Honglin; Li, Hao; Song, Alexander; Haberland, Matt; Akar, Osman; Dhillon, Adam; Zhou, Tiankuang; Bertozzi, Andrea L; Brantingham, P Jeffrey | Abstract: Body-worn cameras are now commonly used for logging daily life, sports, and law enforcement activities, creating a large volume of archived footage. This paper studies the problem of classifying frames of footage according to the activity of the camera-wearer with an emphasis on application to real-world police body-worn video. Real-world datasets pose a different set of challenges from existing egocentric vision datasets: the amount of footage of different activities is unbalanced, the data contains personally identifiable information, and in practice it is difficult to provide substantial training footage for a supervised approach. We address these challenges by extracting features based exclusively on motion information then segmenting the video footage using a semi-supervised classification algorithm. On publicly available datasets, our method achieves results comparable to, if not better than, supervised and/or deep learning methods using a fraction of the training data. It also shows promising results on real-world police body-worn video.
Orchestrating Large-Scale SpGEMMs using Dynamic Block Distribution and Data Transfer Minimization on Heterogeneous Systems

Sparse general matrix-matrix multiplication (SpGEMM) is a major kernel in various emerging applications, such as database management systems, deep learning, graph analysis, and recommendation systems. Since SpGEMM requires extensive computation, many SpGEMM techniques have been implemented based on graphics processing units (GPUs) to exploit massive data parallelism completely. However, traditional SpGEMM techniques usually do not fully utilize the GPU because most non-zero elements of the target sparse matrices exist in a few hub nodes, and non-hub nodes barely have non-zero elements. The data-related characteristics (power law) result in a significant degradation in performance because of the load imbalance between the GPU cores and the low utilization of each core. Many attempts have been made through recent implementations to solve this problem using smart pre-/post-processing. However, the net performance hardly improves and sometimes even deteriorates owing to the large overheads. Additionally, non-hub nodes are inherently not suitable for GPU computing, even after optimization. Furthermore, the performance is no longer dominated by kernel execution, but by data transfers such as device-to-host (D2H) data transfers and file I/Os, owing to the rapid growth in the computing power of GPUs and input data size.Therefore, this work proposes a Dynamic Block Distributor (DBD), a novel full-system-level SpGEMM orchestration framework for heterogeneous systems, improving the overall performance by enabling an efficient CPU-GPU collaboration and further minimizing the overhead in data transfer between all the system elements. This framework first divides the target matrix into smaller blocks and then offloads the computation of each block to an appropriate computing unit between a GPU and CPU based on its workload type and the status of resource utilization at runtime. It also minimizes the overhead in data transfer with simple but suitable techniques, such as Row Collecting, I/O Overlapping, and I/O Binding. Our experiments showed that this framework increased the execution latency of SpGEMM, which included both the kernel execution and D2H transfers, by 3.24x on average, and the overall execution time by 2.07x on average, compared to that of the baseline cuSPARSE library.
PPA and Scaling Potential of Backside Power Options in N2 and A14 Nanosheet Technology

This paper evaluates Power-Performance-Area (PPA) tradeoffs and integration challenges of three types of backside power connections: Through Silicon Via in the Middle Of Line (TSVM), Self-Aligned Front-to-Back via (BPR) and Backside contact (BSC) for nanosheets at N2 and A14 nodes. From TSVM to BPR to BSC, solid PPA gain s are shown for High Density Logic, at the expense of increased process complexity. While TSVM remains competitive in N27-Track high-performance technology, BSC shows maximal gain s in A145-Track high density node.
OpenEmbedding: A Distributed Parameter Server for Deep Learning Recommendation Models using Persistent Memory

In this paper, we present OpenEmbedding, a distributed parameter server system for deep learning recommendation models (DLRM) workloads. In order to support rapid growth in the number of features and the model size (Terabytes are common) of DLRM workloads, OpenEmbedding takes advantage of emerging persistent memory (PMem) to address scalability and reliability issues in training DLRMs. Compared to DRAM, PMem can have much lower per-GB cost, higher density, and non-volatility, while with slightly low access performance to DRAM. OpenEmbedding uses DRAM as cache and PMem as storage for the sparse features and develops a simple but effective pipeline processing approach to optimize the access latency of the sparse features in PMem. For reliability, we develop a lightweight synchronous checkpointing scheme that is specially co-designed with the pipelined cache to reduce the run-time overhead of checkpointing. Our evaluations on a real-world industry workload consisting of billions of parameters demonstrate 1) the effectiveness of our PMem-aware optimizations, 2) checkpointing mechanism with near-zero run-time overhead to the training performance and 3) fast recovery with up to 3.97× speedup compared to the state-of-the-art. OpenEmbedding has been deployed in hundreds of scenarios in industry within 4Paradigm, and is open-sourced1.
A Self-play and Sentiment-Emphasized Comment Integration Framework Based on Deep Q-Learning in a Crowdsourcing Scenario : Extended Abstract

Crowdsourcing is a sourcing model where individuals or organizations obtain goods and services from a large, relatively open and often rapidly evolving group of internet users. The most common way that crowdsourcing can facilitate machine learning is to annotate instances with labels [1] . However, the same instance may have inconsistent class labels, in the eyes of various annotators. Therefore, current efforts in crowdsourcing mainly focus on the truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, instances with the integrated labels could facilitate the training on machine learning models. The future direction of crowdsourcing is to apply more fine-grained truth inference methods to different application domains [2] . Consequently, we evolve toward another challenging problem of comment integration. That is, how can we integrate or summarize the core opinions of multiple product comments obtained from users, rather than the discrete labels.
Cooling System Design of a High-Speed Radial-Flux Permanent Magnet Machine for Aerospace Propulsion Applications

Load constraints in aerospace propulsion applications are driving great attention towards research and development of high-speed electric machines. A major focus area for such machines is a cooling system that is reliable and efficient. In this work the cooling system of a high-speed radial-flux surface-mount permanent magnet synchronous motor is presented. The method of modeling the motor and investigating the cooling performance is detailed. The cooling system of the stator of the baseline design consists of encapsulation on the end windings, a cooling duct on the bearings and a cooling jacket housing on the stator laminations. The rotor is cooled by air forced convection by a fan mounted on the shaft. The effect of various design parameters, like coolant flow rate and potting material, on the temperature of the stator and end windings is investigated. The cooling effect obtained by inserting an additional coolant-carrying tube in close proximity to the high heat-generating parts, is also studied.
Design of Optical Communication Headset Based on Visible Light Communication Technology

To overcome the disadvantages of existing infrared earphones and Bluetooth earphones, such as poor stability, low performance and much interference, an optical communication headsetis designed based on visible light communication technology. The photoelectric transmission system, which takes the visible light as the carrier to transmit the audio signal, is composed of the transmitting part and the receiving part. The modulation and demodulation of signal is realized by using a single-chip microcomputer. Experimental results show that the visible light headset system has the advantages of stability, flexibility in operation, high signal strength, and adjustable volume.
Performance Comparison of Open-Circuit Voltage Modelling of Li-ion Batteries at Different C-Rates

The characterization of a battery to estimate its capacity is a crucial step in open-circuit voltage modelling. The battery capacity estimation is essential to determine diagnostic details on the battery and in determining several other battery parameters. Past research has shown that a normalized open-circuit voltage characterization independent of temperature is also dependent on accurate capacity estimation. In most works, the normalized OCV characterization approaches were done at C/30 rates where the entire data collection took approximately 60 hours. The undesirably long data collection process motivated the need to determine the expected accuracy at lower C-rates in realistic conditions. However, little attention was paid in the literature to investigate capacity estimation error at various C-rates. Thus, in this paper, the battery capacity estimation is repeated at seven C-rates: C/2, C/4, C/8, C/16, C/32, C/64 and C/128, to compare their accuracy using data collected from a laboratory-based battery cycler. It was found that with a lower current rate, a maximum of 0.3 Ah error is observed in the charge capacity. An error of 0.16 Ah was observed for the discharge capacity at the lowest C-rate of C/2 A.