Miracle-dz/UniFAR-FTUs huggingface dataset:
Binaural Sound Reproduction via Distributed Loudspeaker Systems

The basic principle of binaural sound reproduction technique is to reconstruct the same sound pressures at a listener's eardrums that would have caused there by a real sound source to be simulated. Consequently, the listener cannot distinguish between the real sound source and the generated virtual sound source. If a pair of loudspeakers is used, the appropriate ear signals are delivered to the listener by inverting the transmission paths between the two loudspeakers and the two ears. This process, known as "crosstalk cancellation", can be considered as an inversion of a [2 × 2] matrix of transfer functions. Previous work undertaken in this area was concentrated on the use of a conventional stereo set-up where the loudspeakers span an angle of 60° as seen from the listener. As opposed to a stereo set-up, by using two closely spaced loudspeakers, the performance had proven more robust with respect to misalignment or movement of the listener's head. However, one disadvantage of this approach is the source strengths required for the crosstalk cancellation at low frequencies. In terms of matrix algebra, the crosstalk cancellation problem is said to be "ill-conditioned" at these frequencies. Based on a free-field model of the problem, it can be shown that ill-conditioning depends on frequency and the loudspeaker span of the system, respectively. For instance, for a smaller span the system inversion is ill-conditioned at low frequencies, whereas for larger source spans the conditioning is worse at higher frequencies. This connection resulted in the idea to vary the source span as a function of frequency in order to maintain the best possible conditioning over the whole frequency range. A practical solution of this new approach is to use multiple pairs of loudspeakers for each frequency range with corresponding source spans in order to eventually cover the whole audible frequency range. This diploma thesis will discuss the potential for such an approach. Theory, practical implementation, and testing of such systems will be described in detail. Many sound localisation experiments were conducted in order to subjectively validate the system's performance. Results show a significant improvement, in particular with respect to azimuth localisation for virtual images well to the sides.
Medium-Voltage SiC-Based Converter Laminated Bus Insulation Design and Assessment

Laminated bus has been widely used in power converters for decades. Thicker insulation increases parasitic inductance, weight, and size of the bus and decreases cooling performance and power density. Thus, it is desired to have thinner insulation for achieving the desired lifetime. The emerging medium-voltage (MV) SiC-based converters require fast switching transients and high power density, which make the optimal laminated bus design challenging. Since the internal partial discharges (PDs) can significantly degrade insulation, they should be eliminated in the design stage to ensure optimal performance. However, insulation overdesign does not necessarily help remove PD from internal defects and air pockets in the laminated bus. In order to obtain a PD-free laminated bus with thin insulation, this article uses representative structures to show a path for removing internal PDs. This paper presents a systematic approach to remove air pockets and reduce the impact of the unavoidable defects in adhesive layers through theoretical and experimental studies. In addition, a proper material selection and processing method are derived to reduce the size and number of internal defects while leading to a more favorable electric field distribution. The PD experimental results suggest the effectiveness of the proposed approach.
Biosensors on chip: A critical review from an aspect of micro/nanoscales

Abstract Biosensors are a very well cherished research topic and have found an inseparable status from clinical diagnostics in specific and society at large. As the name suggests, biosensors or biological sensors are devices which detect the presence of biological entities or their constituents and derivatives. The field started decades ago and has matured quite well since its inception. The most important performance factors that are associated with biosensors are sensitivity, specificity, and limit of detection. The remaining efforts of the biosensor research domain focus on miniaturization aspects of the sensors. The growing advancements in this field have evolved the technology of biosensors to cater to full-scale diagnosis on microchips, bedside diagnostics, reduced cost, and increased speed of diagnostics. Biosensors are characterized through many different aspects; for example, one way is to classify them on the basis of the type of bio-recognition step that they would utilize or another way can be based on the type of detection scheme that they may integrate, etc. Depending on the bio-recognition layer’s properties, biosensors can be cell based, nucleic acid probe based, antibody/antigen based, or aptamer based, while depending on the type of detection scheme, biosensors can be viewed as colorimetric sensors, optical sensors, electrochemical sensors, mechanical sensors, etc. There are some other parallel areas of research like microfluidics and microelectromechanical systems where one of the main applications lies in the biosensor domain. This review article discusses the various aspects of biosensors, from their design, realization, to testing, along with various detection strategies. The assembly includes fabrication strategies particularly for microchip technology-based biosensing solutions, microchannels, integration to microfluidics, etc., while categorization deals with various kinds and applications of different biosensors.
Profiling Oman education data using data mining approach

Nowadays, with a large amount of data generated by many application services in different learning fields has led to the new challenges in education field. Education portal is an important system that leads to a better development of education field. This research paper presents an innovative data mining techniques to understand and summarizes the information of Oman’s education data generated from the Ministry of Education Oman “Educational Portal”. This research embarks into performing student profiling of the Oman student database. This study utilized the k-means clustering technique to determine the students’ profiles. An amount of 42484-student records from Sultanate of Oman has been extracted for this study. The findings of this study show the practicality of clustering technique to investigating student’s profiles. Allowing for a better understanding of student’s behavior and their academic performance. Oman Education Portal contain a large amounts of user activity and interaction data. Analyses of this large data can be meaningful for educator to improve the student performance level and recognize students who needed additional attention.Nowadays, with a large amount of data generated by many application services in different learning fields has led to the new challenges in education field. Education portal is an important system that leads to a better development of education field. This research paper presents an innovative data mining techniques to understand and summarizes the information of Oman’s education data generated from the Ministry of Education Oman “Educational Portal”. This research embarks into performing student profiling of the Oman student database. This study utilized the k-means clustering technique to determine the students’ profiles. An amount of 42484-student records from Sultanate of Oman has been extracted for this study. The findings of this study show the practicality of clustering technique to investigating student’s profiles. Allowing for a better understanding of student’s behavior and their academic performance. Oman Education Portal contain a large amounts of user activity and interaction data. Analyses of.
Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance

Decisions made by human-AI teams (e.g., AI-advised humans) are increasingly common in high-stakes domains such as healthcare, criminal justice, and finance. Achieving high team performance depends on more than just the accuracy of the AI system: Since the human and the AI may have different expertise, the highest team performance is often reached when they both know how and when to complement one another. We focus on a factor that is crucial to supporting such complementary: the human’s mental model of the AI capabilities, specifically the AI system’s error boundary (i.e. knowing “When does the AI err? ”). Awareness of this lets the human decide when to accept or override the AI’s recommendation. We highlight two key properties of an AI’s error boundary, parsimony and stochasticity, and a property of the task, dimensionality. We show experimentally how these properties affect humans’ mental models of AI capabilities and the resulting team performance. We connect our evaluations to related work and propose goals, beyond accuracy, that merit consideration during model selection and optimization to improve overall human-AI team performance.
The population of galaxies that contribute to the H i mass function

We look at the contribution of different galaxy populations to the atomic hydrogen (H i) mass function (HIMF) and the H i density parameter, ΩH i, in the local Universe. Our analysis is based on a sample of 7857 H i selected galaxies selected from a volume common to the Sloan Digital Sky Survey (SDSS) and Arecibo Fast Legacy ALFA (40 per cent catalogue – α.40) surveys. We define different populations of galaxies in the colour (u − r)–magnitude (Mr) plane and compute the HIMF for each of them. Additionally we compute the HIMF for dark galaxies; these are undetected in SDSS and represent $\sim 2{{\ \rm per\ cent}}$ of the total sample. We find that the luminous red population dominates the total HIMF for $\log _{10}(M_{\rm{H\,{\small I}}}h^2_{70}/M_{\odot }) \ge 10.4$. The full red population – luminous and faint – represents about $\sim 17{{\ \rm per\ cent}}$ of the ΩH i budget, while that of the dark population is $\sim 3{{\ \rm per\ cent}}$. The HIMF about the knee, $\log _{10}(M_{\rm{H\,{\small I}}}h^2_{70}/M_{\odot }) \in [8,10.4]$, is dominated by the faint and luminous blue populations, the latter dominating at larger masses in this interval. Their total contribution to ΩH i is $\sim 55\! -\! 70{{\ \rm per\ cent}}$, the variation depending on the definition of population. The dominant populations at the low-mass end, $\log _{10}(M_{\rm{H\,{\small I}}}h^2_{70}/M_{\odot }) \le 8.0$ are the faint blue and faint bluer populations, the latter’s dominance being sensitive to its definition. The full blue (blue–bluer luminous and faint) population represents $\sim 80{{\ \rm per\ cent}}$ of ΩH i. A bimodal HIMF suggested by our results is however not seen since the amplitude of the HIMF of the luminous red population is small compared to that of the luminous blue population.
Urinary Human Epididymis Secretory Protein 4 as a Useful Biomarker for Subclinical Acute Rejection Three Months after Kidney Transplantation

Kidney transplantation is the treatment of choice for patients with advanced chronic kidney disease (CKD) and end stage renal disease (ESRD). However, acute rejection (AR) is a common complication in kidney transplantation and is associated with reduced graft survival. Current diagnosis of AR relies mainly on clinical monitoring including serum creatinine, proteinuria, and confirmation by histopathologic assessment in the biopsy specimen of graft kidney. Although an early protocol biopsy is indispensable for depicting the severity of pathologic lesions in subclinical acute rejection (subAR), it is not acceptable in some cases and cannot be performed because of its invasive nature. Therefore, we examined the detection of noninvasive biomarkers that are closely related to the pathology of subAR in protocol biopsies three months after kidney transplantation. In this study, the urinary level of microtubule-associated protein 1 light chain 3 (LC3), monocyte chemotactic protein-1 (MCP-1), liver-type fatty acid-binding protein (L-FABP), neutrophil gelatinase-associated lipocalin (NGAL), and human epididymis secretory protein 4 (HE4) were measured three months after kidney transplantation. Urine samples of 80 patients undergoing kidney transplantation between August 2014 to September 2016, were prospectively collected after three months. SubAR was observed in 11 patients (13.8%) in protocol biopsy. The urinary levels of LC3, MCP-1, NGAL, and HE4 were significantly higher in patients with subAR than in those without, while those of L-FABP did not differ between the two groups. Multivariate regression models, receiver-operating characteristics (ROC), and areas under ROC curves (AUC) were used to identify predicted values of subAR. Urinary HE4 levels were able to better identify subAR (AUC = 0.808) than the other four urinary biomarkers. In conclusion, urinary HE4 is increased in kidney transplant recipients of subAR three months after kidney transplantation, suggesting that HE4 has the potential to be used as a novel clinical biomarker for predicting subAR.
A Reminiscence of “Mastermind”: Iris/Periocular Biometrics by “In-Set” CNN Iterative Analysis

Convolutional neural networks (CNNs) have emerged as the most popular classification models in biometrics research. Under the discriminative paradigm of pattern recognition, CNNs are used typically in one of two ways: 1) <italic>verification</italic> mode (“ <italic>are samples from the same person? </italic> ”), where pairs of images are provided to the network to distinguish between <italic>genuine</italic> and <italic>impostor</italic> instances and 2) <italic>identification</italic> mode (“ <italic>whom is this sample from? </italic> ”), where appropriate feature representations that map images to identities are found. This paper postulates a novel mode for using CNNs in biometric identification, by learning models that answer the question “ <italic>is the query’s identity among this set? </italic> ”. The insight is a reminiscence of the classical <italic>Mastermind</italic> game: by iteratively analyzing the network responses when multiple random samples of <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> gallery elements are compared to the query, we obtain weakly correlated matching scores that, altogether, provide solid cues to infer the most likely identity. In this setting, identification is regarded as a variable selection and regularization problem, with sparse linear regression techniques being used to infer the matching probability with respect to each gallery identity. As main strength, this strategy is highly robust to <italic>outlier</italic> matching scores, which are known to be a primary error source in biometric recognition. Our experiments were carried out in full versions of two well-known irises near-infrared (CASIA-IrisV4-Thousand) and periocular visible wavelength (UBIRIS.v2) datasets, and confirm that recognition performance can be solidly boosted-up by the proposed algorithm, when compared with the traditional working modes of CNNs in biometrics.
Resolution Adaptive Volume Sculpting

We propose a sculpture metaphor based on a multiresolution volumetric representation. It allows the user to model both precise and coarse features while maintaining interactive updates and display rates. The modelled surface is an iso-surface of a scalar field, which is sampled on an adaptive hierarchical grid that dynamically subdivides or undivides itself. Field modifications are transparent to the user: The user feels as if he were directly interacting with the surface via a tool that either adds or removes "material. " Meanwhile, the tool modifies the scalar field around the surface, its size and shape automatically guiding the underlying grid subdivision. In order to give an interactive feedback whatever the tool's size, tools are applied in an adaptive way, the grid being always updated from coarse to fine levels. This maintains interactive rates even for large tool sizes. It also enables the user to continuously apply a tool, with an immediate coarse-scale feedback of the multiple actions being provided. A dynamic level-of-detail (LOD) mechanism ensures that the iso-surface is displayed at interactive rates regardeless of the zoom value; surface elements, generated and stored at each level of resolution, are displayed depending on their size on the screen. The system may switch to a coarser surface display during user actions, thus always ensuring interactive visual feedback. Two applications illustrate the use of this system: First, complex shapes with both coarse and fine features can be sculpted from scratch. Second, we show that the system can be used to edit models that have been converted from a mesh representation.
AUTOMATED, OUTPUT-ONLY EXTRACTION OF FULL-FIELD, VERY HIGH SPATIAL RESOLUTION DYNAMIC PARAMETERS FROM VIDEOS OF OPERATING STRUCTURES

Modal analysis is essential for dynamic modeling and analysis of seismic excited structures. Experimental or operational modal analysis traditionally requires physically-attached wired or wireless sensors for vibration measurement of structures. The sensor instrumentation could result in mass-loading on lightweight structures, and is costly and time-consuming for large civil structures, especially for longterm applications (e.g., structural health monitoring and seismic dynamic analysis) that require significant maintenance and labors for cabling (wired) or energy supply (wireless). In addition, these sensors are discrete point-wise, providing low spatial sensing resolution that is hardly sufficient for larger structures. Non-contact optical methods such as scanning laser vibrometers provide high-resolution sensing capacity without the mass loading effect; however, they operate sequential measurement that requires considerable acquisition time. As an alternative non-contact method, digital video cameras are relatively low-cost, agile, and provide high spatial resolution, simultaneous, measurements. Combined with vision based algorithms (e.g., image correlation or template matching, optical flow, etc.), video camera based measurements have been successfully used for experimental and operational vibration measurement and modal analysis, such as the digital image correlation (DIC) and the point-tracking techniques. However, they typically require speckle pattern or high-contrast markers instrumented on the surface of structures, which raises the instrumentation issue when the measurement area is large or inaccessible. This work explores advanced computer vision and video processing algorithms to develop a novel video measurement and vision based output-only modal analysis method that removes the need of structural surface preparation in existing vision based methods. By manipulating the motion encoded in the video measurements only using multi-scale image decomposition and unsupervised machine learning techniques, the proposed method efficiently and accurately extract modal frequencies, very high spatial (pixel) resolution mode shapes, and damping ratios of the structure. The method is validated by laboratory experiments on bench-scale structures including a building structure and a cantilever beam. Video demos of these experimental results are on http://www.lanl. gov/projects/national-security-educationcenter/engineering/research-projects/blind-modal-id.php.
Differentiation of economic and financial cycles and the logic of China’s monetary policy reform

In recent years, with the gradual differentiation of economic and financial cycles, it has been increasingly difficult for monetary policies to remain balanced in stabilizing both economy and finance. Taking the period of 1999–2017 as a sample, the purpose of this paper is to find whether the synergy between the growth cycle and the price cycle is constantly improving in the economic cycle is more appropriate. ,The key to stabilizing the economic cycle lies in the monetary policy and it should abandon the goal of boosting growth in a timely manner and turn into the goal of maintaining steady growth. At present, quantitative monetary policy is still more effective than price-oriented monetary policy in smoothing the economic cycle. ,The impact of quantitative regulation on the financial cycle is more neutral, whereas price regulation will increase the volatility of price and financial cycles in the course of smoothing the growth cycle. In view of the continuous differentiation between the economic and financial cycles, it is realistic and reasonable to accelerate the establishment of a sound dual-pillar regulatory framework of “monetary policy and macro-prudential policy. ”,The macro-prudential policy is specially used to smooth the financial cycle, so as to reduce the burden and increase the efficiency of the monetary policy on regulating economic cycle. Moreover, the transformation of monetary policy to price-oriented regulation must keep pace with the construction of the dual-pillar regulation framework and complement each other to prevent undesirable consequences in the financial sector. On the other hand, monetary policy still needs to rely on quantitative regulation in the future. The research in this paper also provides a new perspective for understanding the internal and external reform of China’s monetary policy in recent years.
Continuation of Nesterov’s Smoothing for Regression With Structured Sparsity in High-Dimensional Neuroimaging

Predictive models can be used on high-dimensional brain images to decode cognitive states or diagnosis/prognosis of a clinical condition/evolution. Spatial regularization through structured sparsity offers new perspectives in this context and reduces the risk of overfitting the model while providing interpretable neuroimaging signatures by forcing the solution to adhere to domain-specific constraints. Total variation (TV) is a promising candidate for structured penalization: it enforces spatial smoothness of the solution while segmenting predictive regions from the background. We consider the problem of minimizing the sum of a smooth convex loss, a non-smooth convex penalty (whose proximal operator is known) and a wide range of possible complex, non-smooth convex structured penalties such as TV or overlapping group Lasso. Existing solvers are either limited in the functions they can minimize or in their practical capacity to scale to high-dimensional imaging data. Nesterov’s smoothing technique can be used to minimize a large number of non-smooth convex structured penalties. However, reasonable precision requires a small smoothing parameter, which slows down the convergence speed to unacceptable levels. To benefit from the versatility of Nesterov’s smoothing technique, we propose a first order continuation algorithm, CONESTA, which automatically generates a sequence of decreasing smoothing parameters. The generated sequence maintains the optimal convergence speed toward any globally desired precision. Our main contributions are: gap to probe the current distance to the global optimum in order to adapt the smoothing parameter and the To propose an expression of the duality convergence speed. This expression is applicable to many penalties and can be used with other solvers than CONESTA. We also propose an expression for the particular smoothing parameter that minimizes the number of iterations required to reach a given precision. Furthermore, we provide a convergence proof and its rate, which is an improvement over classical proximal gradient smoothing methods. We demonstrate on both simulated and high-dimensional structural neuroimaging data that CONESTA significantly outperforms many state-of-the-art solvers in regard to convergence speed and precision.