Miracle-dz/UniFAR-FTUs huggingface dataset:
FERM: A FEature-space Representation Measure for Improved Model Evaluation

Understanding whether a particular dataset and task are well represented by a deep learning model can be as crucial as the model’s prediction accuracy in many applications. Currently, best prediction performance for large, modern datasets is often achieved by complex and difficult to interpret deep learning models. As deep learning model size and complexity increases compared to the size of the training dataset, the capacity of the model to overfit to inappropriate features and perform poorly or unreliably also increases. Unreliability may not be obvious in traditional performance measures during evaluation so it is important to also consider how well the model is representing the current data distribution. There has previously been little work focusing on measuring this space. We introduce several measures that we collectively name FERM: A FEature-space Representation Measure for determining how well the current feature space representation models the current data distribution and task. We compared our new measures and potential candidates from other related research areas. We demonstrated that our new method, along with two others, have excellent potential to be used for measuring how well a trained model is currently representing a dataset and task. These findings have many implications for deep learning research and applications, including, evaluating when the current model is no longer representing new data well to reduce the frequency of computationally expensive retraining of models, assessing for hard to evaluate failure modes such as model biases that result in particular input samples being poorly represented, guidance on the best hyperparameters to use when updating models with limited new data.
Moduli in General $SU(3)$-Structure Heterotic Compactifications

In this thesis, we study compactifiations of ten-dimensional heterotic supergravity at O(α'), focusing on the moduli of such compactifications. We begin by studying supersymmetric compactifications to four-dimensional maximally symmetric space, commonly referred to as the Strominger system. The compactifications are of the form M10 = M4 x X, where M4 is four-dimensional Minkowski space, and X is a six-dimensional manifold of what we refer to as heterotic SU(3)-structure. We show that this system can be put in terms of a holomorphic operator D on a bundle Q = Ta X ⊕ End(TX) ⊕ End(V ) ⊕ TX, defined by a series of extensions. Here V is the E8 x E8 gauge-bundle, and TX is the tangent bundle of the compact space X. We proceed to compute the infinitesimal deformation space of this structure, given by TM = H(0,1)(Q), which constitutes the infinitesimal spectrum of the lower energy four-dimensional theory. In doing so, we find an over counting of moduli by H(0,1)(End(TX)), which can be reinterpreted as O(α') field redefinitions. In the next part of the thesis, we consider non-maximally symmetric compactifications of the form M10 = M3 x Y , where M3 is three-dimensional Minkowski space, and Y is a seven-dimensional non-compact manifold with a G2-structure. We write X → Y → r, where X is a six dimensional compact space of half- at SU(3)-structure, non-trivially fibered over r. These compactifications are known as domain wall compactifications. By focusing on coset compactifications, we show that the compact space X can be endowed with non-trivial torsion, which can be used in a combination with %alpha;'-effects to stabilise all geometric moduli. The domain wall can further be lifted to a maximally symmetric AdS vacuum by inclusion of non-perturbative effects in a heterotic KKLT scenario. Finally, we consider domain wall compactifications where X is a Calabi-Yau. We show that by considering such compactifications, one can evade the usual no-go theorems for flux in Calabi-Yau compactifications, allowing flux to be used as a tool in such compactifications, even when X is Kahler. The ultimate success of these compactifications depends on the possibility of lifting such vacua to maximally symmetric ones by means of e.g. non-perturbative effects.
Design and analysis of wideband U‐slot patch antenna with U‐shaped parasitic elements

A single layer single probe‐fed wideband microstrip antenna is presented and investigated. By cutting a U‐slot in the rectangular patch, and by incorporating two identical U‐shaped parasitic patches around both the radiating edges and the nonradiating edges of the rectangular patch, three resonant frequencies are excited to form the wideband performance. Details of the antenna design is presented. The measured and simulated results are in good agreement, the measured impedance bandwidth is 1.44 GHz ( 4.82‐6.26 GHz), or 26.2% centered at 5.5 GHz, which covers WLAN 5.2 GHz ( 5.15‐5.35 GHz), WLAN 5.8 GHz ( 5.725‐5.825 GHz), and WIMAX 5.5 GHz ( 5.25‐5.85 GHz) bands. The measured peak gains at the three resonant frequencies are 7.73 dB, 7.01 dB, and 4.55 dB, respectively. An equivalent circuit model which is based on the transmission line theory, the asymmetric coupled microstrip lines theory, and the π‐network theory is established. This equivalent circuit model is used to give an insight into the wideband mechanism of the proposed antenna, and is also used to explain why the three resonant frequencies shift at the variations of different parameters from a physical point of view. The error analysis is given to demonstrate the validity of the equivalent circuit model.
Towards Current-Mode Analog Implementation of Deep Neural Network Functions

This paper proposes a current-mode analog circuit design that operates in the subthreshold region to implement various Deep Neural Network (DNN) functions. The implemented circuit blocks include binary weight multiplier layer, Rectified Linear Unit (ReLU), and approximate Softmax layer. The proposed designs were implemented using 180nm CMOS technology with a 1.5V power supply. Furthermore, the impact of the proposed design on accuracy was simulated using the MNIST dataset. Using a four layers Convolutional Neural Network (CNN) with an 8 bits resolution, the design achieved an accuracy of 99.02% with 68.21uW power consumption, which is 35.65% lower than the existing analog DNN design.
A broadband transceiver ASIC for X-nuclei NMR spectroscopy in 0.13 µm BiCMOS

In this paper, we present a fully-integrated transceiver ASIC (NMR-on-a-chip) for multi-nuclei nuclear magnetic resonance (NMR) measurements with operating frequencies up to 770 MHz. The NMR-on-a-chip transceiver co-integrates a low-IF receiver, consisting of a low-noise amplifier, quadrature down-conversion mixers followed by intermediate-frequency variable gain amplifiers, a power amplifier, and a frequency generation block. The ASIC is realized in a 0.13 µm SiGe BiCMOS technology and occupies a total chip area of 1100×900 µm2. The power amplifier has a maximum peak-to-peak output current of ITX,pp = 210 mA. The receiver chain has a maximum conversion gain of ARX = 66 dB with a minimum input-referred noise of ${v_{{\text{n,min}}}} = 610{\text{ pV}}/\sqrt {{\text{Hz}}} $. The chip performance is also verified in the target NMR application, in which the presented system achieves a state-of-the-art normalized spin sensitivity of $8.3 \times {10^{17}}{\text{spins}}/\sqrt {Hz} \cdot {{\text{T}}^2}{\text{m}}$.
Synthetic magnetogram calculations from magnetosphere-ionosphere coupling models

Synthetic Magnetogram Calculations from Magnetosphere-Ionosphere Coupling Models by Paul Alejandro Ontiveros Global MHD models, ring-current models, convection models, and a growing number of coupled models calculate the large-scale currents in the magnetosphere-ionosphere (MI) system. Traditionally, these models have been tested against single-point magnetic field measurements made by individual spacecraft. This often does not provide a clear picture of the pattern of model-data discrepancies. The global network of ground-based magnetograms constitutes a large source of data that is currently being underutilized for validation and analysis of these models. This is primarily due to the fact that a good code does not exist that accurately makes the connection between the models' output and ground magnetometer data. To address this, a numerical code has been developed to compute realistic ground magnetic field perturbations, based upon input from these models, and is described in this thesis. The code includes the effects of all large-scale current systems in the MI system. It also computes the effects from the inner magnetosphere currents by integrating the Biot-Savart integral over a spherical grid centered on the Earth. A scalar potential representation is used for the magnetic field due to the currents in the outer magnetosphere and solar wind. The effects of the ground induction currents are also included with a scalar potential by including the effects of an infinitely conducting sphere below the surface. Aside from the code's ability to compute realistic magnetic field disturbances at individual magnetometers, it is possible to compile synthetic geomagnetic indices and synthetic global disturbance maps. The software is intended as a general-use tool and was built to be flexible so that it can be integrated with a wide variety of large-scale MI coupling models. Initial synthetic magnetogram results are presented based on SWMF and RCM storm simulations. Synthetic Dst was computed for the SWMF simulation results, while synthetic magnetograms, several geomagnetic indices, and an LT-UT map were computed for the RCM simulation results. The magnetogram output is compared to observations to analyze both MI coupling codes.
Customer Order Behavior Classification Via Convolutional Neural Networks in the Semiconductor Industry

In the operational processes of demand planning and order management, it is crucial to understand customer order behavior to provide insights for supply chain management processes. Here, advances in the semiconductor industry have emerged through the extraction of important information from vast amounts of data. This new data and information availability paves the way for the development of improved methods to analyze and classify customer order behavior (COB). To this end, we develop a novel, sophisticated yet intuitive image-based representation for COBs using two-dimensional heat maps. This heat map representation contributes significantly to the development of a novel COB classification framework. In this framework, we utilize data enrichment via synthetical training samples to train a CNN model that performs the classification task. Integrating synthetically generated data into the training phase allows us to strengthen the inclusion of rare pattern variants that we identified during initial analysis. Moreover, we show how this framework is used in practice at Infineon. We finally use actual customer data to benchmark the performance of our framework and show that the baseline CNN approach outperforms all available state-of-the-art benchmark models. Additionally, our results highlight the benefit of synthetic data enrichment.
Exploration of abnormal dynamic spontaneous brain activity in patients with high myopia via dynamic regional homogeneity analysis

Aim Patients with high myopia (HM) reportedly exhibit changes in functional brain activity, but the mechanism underlying such changes is unclear. This study was conducted to observe differences in dynamic spontaneous brain activity between patients with HM and healthy controls (HCs) via dynamic regional homogeneity (dReHo) analysis. Methods Resting-state functional magnetic resonance imaging (rs-fMRI) scans were performed on 82 patients with HM and 59 HCs who were closely matched for age, sex, and weight. The dReHo approach was used to assess local dynamic activity in the human brain. The association between mean dReHo signal values and clinical symptoms in distinct brain areas in patients with HM was determined via correlation analysis. Results In the left fusiform gyrus (L-FG), right inferior temporal gyrus (R-ITG), right Rolandic operculum (R-ROL), right postcentral gyrus (R-PoCG), and right precentral gyrus (R-PreCG), dReHo values were significantly greater in patients with HM than in HCs. Conclusion Patients with HM have distinct functional changes in various brain regions that mainly include the L-FG, R-ITG, R-ROL, R-PoCG, and R-PreCG. These findings constitute important evidence for the roles of brain networks in the pathophysiological mechanisms of HM and may aid in the diagnosis of HM.
High-Resolution Infrared Spectroscopy of the Brown Dwarf ϵ Indi Ba

We report on the analysis of high-resolution infrared spectra of the newly discovered brown dwarf ϵ Ind Ba. This is the closest known brown dwarf to the solar system, with a distance of 3.626 pc. Spectra covering the ranges of λλ2. 308-2.317 μm and λλ1. 553-1.559 μm were observed at a resolution of λ/Δλ = R = 50,000. The physical parameters of effective temperature and surface gravity are derived for ϵ Ind Ba by comparison with model spectra calculated from atmospheres computed using unified cloudy models. The results are Teff = 1500 ± 100 K, log g = 5.2 ± 0.3 (in units of cm s-2), placing it in the critical boundary between the late L and early T dwarfs. The high spectral resolution also allows us to measure an accurate projected rotational velocity, with v sin i = 28 ± 3 km s-1. Combined with a published luminosity for ϵ Ind Ba [with log(L/L☉) = -4.71], the derived parameters result in a "spectroscopic" mass estimate of ~30MJ, a radius of ~0.062 R☉, and a maximum rotational period of ~3.0 hr. A compilation and comparison of effective temperatures derived from spectroscopy using model atmospheres versus those derived from luminosities and theoretical Mbol-radius relations reveal a systematic disagreement in the Teff scale. The source of this disagreement is unknown.
Possible Periodic Components in the Flickering of CH Cyg and MWC 560

CH Cyg and MWC 560 are very peculiar symbiotic binaries consisting of an M giant and a white dwarf companion. The systems have many features in common. In particular, both show occasional eruptions with sub-Eddington luminosity, followed by the appearance of high-velocity jets. Recently, Mikolajewski, Mikolajewska & Tomov (1995) have proposed a model of activity for these systems with a rapidly rotating magnetic white dwarf accreting from the M giant’s wind. Two states of activity are possible: propeller and accretor. Although, both stars spend most time in the propeller state, occasional propeller-accretor (and vice versa) transitions may give rise to bipolar outflows and rapid jet formations. Mikolajewski et al. (1995) also proposed to distinguish a new subclass of interacting binaries — propeller stars — with CH Cyg and MWC 560 being the prototypes.
Three types of pulsed signal trains emitted by Indo-Pacific humpback dolphins (Sousa chinensis) in Beibu Gulf, South China Sea

Pulsed signal trains comprising clicks, buzzes, and burst-pulses play important roles in the life activities of odontocetes, but they have not been distinguished in Indo-Pacific humpback dolphins. Underwater vocalizations of this species were recorded from 27 September to 2 October 2019 in the Beibu Gulf, South China Sea. Pulsed signal trains were detected with variations in the pulsed signal number (range of 6–76), mean inter-pulse interval (IPIs_m: 0.1–315 ms), and mean duration (D ranged from tens to thousands of milliseconds). Principal component analysis and hierarchical cluster analysis based on six acoustic parameters in the pulsed signal trains identified three categories of trains designated as clicks, burst-pulses, and buzzes. Buzzes and burst-pulses (different from those described in previous research) were detected for the first time in Indo-Pacific humpback dolphins in China. The results indicated that the IPIs_m was longest for clicks but shortest for buzzes, and the D values were longer for both clicks and burst-pulses than buzzes. The three train types could be identified based on the IPIs_m, with threshold values of 4.9 and 15.5 ms. The significant variations in the three vocalization types were related to surface behaviors, and buzzes could have a special function in foraging by this species, thereby requiring further research. These findings may facilitate future quantitative evaluations of the echolocation performance in wild Indo-Pacific humpback dolphins and provide important guidance regarding acoustic observations and the identification of this species.
Prediction of the backwater level due to bridge constriction in waterways

This article proposes a mathematical method to calculate the backwater level at bridge constrictions for compound channels. Such a model is necessary, as many of the previously developed simple formulas yield inaccurate results. Parametric studies on a single‐opening semicircular arch, single‐opening semielliptical arch and single‐opening straight deck bridge were also conducted using the commercial software HEC‐RAS to investigate the influences of different factors on backwater level. The results indicated that the Froude number, opening ratio, discharge and roughness coefficient impact the backwater level considerably. Hence, a simple mathematical method was developed based on these parameters and validated using experimental data in a symmetrical two‐stage channel with different roughness coefficients for different types of bridge models. The results obtained using the proposed formula corresponded well with the experimental data and the results obtained using the energy method, which is accurate and the most commonly used method to calculate backwater level.
Robust Model Predictive Control Framework for Energy-Optimal Adaptive Cruise Control of Battery Electric Vehicles

The autonomous vehicle following problem has been extensively studied for at least two decades with the rapid development of intelligent transport systems. In this context, this paper proposes a robust model predictive control (RMPC) method that aims to find the energy-efficient following velocity of an ego battery electric vehicle and to guarantee a safe rear-end distance in the presence of disturbances and modelling errors. The optimisation problem is formulated in the space domain so that the overall problem can be convexified in the form of a semi-definite program, which ensures a rapid solving speed and a unique solution. Simulations are carried out to provide numerical comparisons with a nominal model predictive control (MPC) scheme. It is shown that the RMPC guarantees robust constraint satisfaction for the closed-loop system whereas constraints may be violated when the nominal MPC is in use. Moreover, the impact of the prediction horizon length on optimality is investigated, showing that a finely tuned horizon could produce significant energy savings.
Widespread cortical thinning, excessive glutamate and impaired linguistic functioning in schizophrenia: A cluster analytic approach

Introduction Symptoms of schizophrenia are closely related to aberrant language comprehension and production. Macroscopic brain changes seen in some patients with schizophrenia are suspected to relate to impaired language production, but this is yet to be reliably characterized. Since heterogeneity in language dysfunctions, as well as brain structure, is suspected in schizophrenia, we aimed to first seek patient subgroups with different neurobiological signatures and then quantify linguistic indices that capture the symptoms of “negative formal thought disorder” (i.e., fluency, cohesion, and complexity of language production). Methods Atlas-based cortical thickness values (obtained with a 7T MRI scanner) of 66 patients with first-episode psychosis and 36 healthy controls were analyzed with hierarchical clustering algorithms to produce neuroanatomical subtypes. We then examined the generated subtypes and investigated the quantitative differences in MRS-based glutamate levels [in the dorsal anterior cingulate cortex (dACC)] as well as in three aspects of language production features: fluency, syntactic complexity, and lexical cohesion. Results Two neuroanatomical subtypes among patients were observed, one with near-normal cortical thickness patterns while the other with widespread cortical thinning. Compared to the subgroup of patients with relatively normal cortical thickness patterns, the subgroup with widespread cortical thinning was older, with higher glutamate concentration in dACC and produced speech with reduced mean length of T-units (complexity) and lower repeats of content words (lexical cohesion), despite being equally fluent (number of words). Conclusion We characterized a patient subgroup with thinner cortex in first-episode psychosis. This subgroup, identifiable through macroscopic changes, is also distinguishable in terms of neurochemistry (frontal glutamate) and language behavior (complexity and cohesion of speech). This study supports the hypothesis that glutamate-mediated cortical thinning may contribute to a phenotype that is detectable using the tools of computational linguistics in schizophrenia.
Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea

Background OSA is an independent risk factor for several systemic diseases. Compared with mild OSA, patients with moderate-to-severe OSA have more severe impairment in the function of all organs of the body. Due to the current limited medical condition, not every patient can be diagnosed and treated in time. To enable timely screening of patients with moderate-to-severe OSA, we selected easily accessible variables to establish a risk prediction model. Method We collected 492 patients who had polysomnography (PSG), and divided them into the disease-free mild OSA group (control group), and the moderate-to-severe OSA group according to the PSG results. Variables entering the model were identified by random forest plots, univariate analysis, multicollinearity test, and binary logistic regression method. Nomogram were created based on the binary logistic results, and the area under the ROC curve was used to evaluate the discriminative properties of the nomogram model. Bootstrap method was used to internally validate the nomogram model, and calibration curves were plotted after 1,000 replicate sampling of the original data, and the accuracy of the model was evaluated using the Hosmer-Lemeshow goodness-of-fit test. Finally, we performed decision curve analysis (DCA) of nomogram model, STOP-Bang questionnaire (SBQ), and NoSAS score to assess clinical utility. Results There are 6 variables entering the final prediction model, namely BMI, Hypertension, Morning dry mouth, Suffocating awake at night, Witnessed apnea, and ESS total score. The AUC of this prediction model was 0.976 (95% CI: 0.962–0.990). Hosmer-Lemeshow goodness-of-fit test χ2 = 3.3222 (P = 0.1899 > 0.05), and the calibration curve was in general agreement with the ideal curve. The model has good consistency in predicting the actual occurrence of moderate-to-severe risk, and has good prediction accuracy. The DCA shows that the net benefit of the nomogram model is higher than that of SBQ and NoSAS, with has good clinical utility. Conclusion The prediction model obtained in this study has good predictive power for moderate-to-severe OSA and is superior to other prediction models and questionnaires. It can be applied to the community population for screening and to the clinic for prioritization of treatment.