Miracle-dz/UniFAR-FTUs huggingface dataset:
Kernelized Capsule Networks

Capsule Networks attempt to represent patterns in images in a way that preserves hierarchical spatial relationships. Additionally, research has demonstrated that these techniques may be robust against adversarial perturbations. We present an improvement to training capsule networks with added robustness via non-parametric kernel methods. The representations learned through the capsule network are used to construct covariance kernels for Gaussian processes (GPs). We demonstrate that this approach achieves comparable prediction performance to Capsule Networks while improving robustness to adversarial perturbations and providing a meaningful measure of uncertainty that may aid in the detection of adversarial inputs.
Si-Based GeSn Photodetectors toward Mid-Infrared Imaging Applications

This paper reports a comprehensive study of Si-based GeSn mid-infrared photodetectors, which includes: 1) the demonstration of a set of photoconductors with Sn compositions ranging from 10.5% to 22.3%, showing the cut-off wavelength has been extended to 3.65 um. The measured maximum D* of 1.1x10^10 cmHz^(1/2)W(-1) is comparable to that of commercial extended-InGaAs detectors; 2) the development of surface passivation technique on photodiode based on in-depth analysis of dark current mechanism, effectively reducing the dark current. Moreover, mid-infrared images were obtained using GeSn photodetectors, showing the comparable image quality with that acquired by using commercial PbSe detectors.
Packaged photonic chip-based soliton microcomb using an ultralow-noise laser

Photonic chip-based soliton microcombs have shown rapid progress and have already been used in many system-level applications, including coherent communications, astrophysical spectrometer calibration and ultrafast ranging. While there has been substantial progress in realizing soliton microcombs that rely on compact laser diodes, culminating in devices that only utilize a semiconductor amplifier or a self-injection-locked laser as a pump source, accessing and generating single soliton states with electronically detectable line rates from a compact laser module has remained challenging. Here we demonstrate a current-initiated, Si3N4 chip-based, 99-GHz soliton microcomb driven directly by an ultra-compact, low-noise laser. This approach does not require any fast laser tuning mechanism, and single soliton states can be accessed by changing the current of the laser diode. A simple, yet reliable, packaging technique has been developed to demonstrate the viability of such a microcomb system in field-deployable applications.
Flat Hybrid Automata as a Class of Reachable Systems: Introductory Theory and Examples

Controlling hybrid systems is mostly very challenging due to the variety of dynamics these systems can exhibit. Inspired by the concept of differential flatness of nonlinear continuous systems and their inherent invertibility property, the present contribution is focused on explicit input trajectory calculation. To this end, a new class of hybrid systems called Flat Hybrid Automata is introduced as a realisation of deterministic, reachable and explicitly invertible hybrid automata. Relevant system properties are derived, an approach for construction and for trajectory calculation is proposed and two demonstrative examples are presented. The results constitute a generalisation of control of invertible hybrid systems which is very useful if, e.g., fast reaction for stabilisation or transitions is relevant.
pT
-dependent particle number fluctuations from principal-component analyses in hydrodynamic simulations of heavy-ion collisions

We carry out a principal component analysis of fluctuations in a hydrodynamic simulation of heavy-ion collisions, and compare with experimental data from the CMS collaboration. The leading and subleading principal components of elliptic and triangular flow reproduce the trends seen in data. By contrast, the principal components of multiplicity fluctuations show an interesting difference in their $p_T$ dependence for simulations compared to experimental data. Specifically, the leading component increases with $p_T$ in hydrodynamics, while it is constant in experiment. In order to understand how the leading and subleading modes arise, we construct a toy model where the principal components have a simple analytic form. We show how the PCA components depend on fluctuations of the average transverse momentum and of the total multiplicity, as well as correlations between the two, and we verify that hydrodynamic simulations agree with the predictions of the toy model. The difference in the momentum trend is likely due to the fact that hydrodynamic models typically have transverse momentum fluctuations that are larger than seen experimentally.
Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions

Semantic segmentation and instance level segmentation made substantial progress in recent years due to the emergence of deep neural networks (DNNs). A number of deep architectures with Convolution Neural Networks (CNNs) were proposed that surpass the traditional machine learning approaches for segmentation by a large margin. These architectures predict the directly observable semantic category of each pixel by usually optimizing a cross-entropy loss. In this work we push the limit of semantic segmentation towards predicting semantic labels of directly visible as well as occluded objects or objects parts, where the network’s input is a single depth image. We group the semantic categories into one background and multiple foreground object groups, and we propose a modification of the standard cross-entropy loss to cope with the settings. In our experiments we demonstrate that a CNN trained by minimizing the proposed loss is able to predict semantic categories for visible and occluded object parts without requiring to increase the network size (compared to a standard segmentation task). The results are validated on a newly generated dataset (augmented from SUNCG) dataset.
A LX (Learner eXperience)-Based Evaluation Method of the Education and Training Programs for Professional Software Engineers

We propose a new design methodology to maximize the training effect in a corporate education and training for professional software engineers. Conventionally, the education and training programs have been designed in a top-down manner based on the long-term strategy on the business and engineering resources development. However, to draw out the learners' high performance from the education and training programs, we need to have an empathy with the learners, and to analyze their expectations and emotions in order to motivate them. Therefore, this paper proposes the learner-centered design methodology of the corporate education and training programs inspired by the design thinking and lean start-up concepts. We define the learning processes in the education and training programs as LX (Learner eXperience), and propose LJM (Learning Journey Map) as the LX evaluation method as an extension of CJM (Customer Journey Map) in UX (User eXperience) design. The LJM enables to evaluate training effect and communicate with stakeholders in the training design expressing the LX quantitatively in a visual form. We applied the proposed design methodology to the education and training programs for professional software engineers in a company to evaluate LX and elicit learner requirements to the programs. We applied the proposed LJM to the education and training program of two levels of the whole program and its LUs (Learning Units), and identified problems in the LX. From the empirical study, we confirm the effectiveness of the proposed methodology.
Improving Exploration in Soft-Actor-Critic with Normalizing Flows Policies

Deep Reinforcement Learning (DRL) algorithms for continuous action spaces are known to be brittle toward hyperparameters as well as \cut{being}sample inefficient. Soft Actor Critic (SAC) proposes an off-policy deep actor critic algorithm within the maximum entropy RL framework which offers greater stability and empirical gains. The choice of policy distribution, a factored Gaussian, is motivated by \cut{chosen due}its easy re-parametrization rather than its modeling power. We introduce Normalizing Flow policies within the SAC framework that learn more expressive classes of policies than simple factored Gaussians. \cut{We also present a series of stabilization tricks that enable effective training of these policies in the RL setting. }We show empirically on continuous grid world tasks that our approach increases stability and is better suited to difficult exploration in sparse reward settings.
Brain-derived Neurotrophic Factor Binding to the p75 Neurotrophin Receptor Reduces TrkA Signaling While Increasing Serine Phosphorylation in the TrkA Intracellular Domain*

We have examined whether the low affinity neurotrophin receptor p75NTR modulates TrkA function by intracellular signaling. Using ligands that selectively bind p75NTR or TrkA, we found that a p75NTR-derived signal reduces TrkA activation. Short term treatment of PC12 cells with ceramide analogues also resulted in reduced NGF-stimulated TrkA activation, suggesting that p75-mediated increases in sphingomyelinase activity may contribute to this modulatory effect. Phosphoamino acid analysis was performed to determine if brain-derived neurotrophic factor- or ceramide-mediated phosphorylation of the TrkA intracellular domain correlated with a reduction in its ligand-induced activation. A specific increase in TrkA phosphoserine content was observed in response to both C2-ceramide and brain-derived neurotrophic factor. These results suggest that ligand binding of p75NTR can activate a signaling cascade that results in reduced TrkA activity through phosphorylation of its intracellular domain.
The Elastic Processing of Data Streams in Cloud Environments: A Systematic Mapping Study

Ongoing efforts exist to exploit cloud elasticity for processing efficiently data streams generated by a variety of data sources. To contribute to these efforts an overview of existing research is required. To the best of our knowledge, a systematic overview of the field is missing. To fill this gap, we conduct a Systematic Literature Map (SLM). This way we offer a high-level overview of the literature on elastic data stream processing. We search four databases, evaluate 564 publications and identify 100 relevant publications. The identified publications show that the majority of work is validated research through proofs-of-concept and very few through case studies, surveys and field experiments. There are several frameworks, approaches and tools proposed, but, fewer metrics, models and processes.
Homogeneous Finsler sphere with constant flag curvature.

We prove that a homogeneous Finsler sphere with constant flag curvature $K\equiv1$ and a prime closed geodesic of length $2\pi$ must be Riemannian. This observation provides the evidence for the non-existence of homogeneous Bryant spheres. It also helps us propose an alternative approach proving that a geodesic orbit Finsler sphere with $K\equiv1$ must be Randers. Then we discuss the behavior of geodesics on a homogeneous Finsler sphere with $K\equiv1$. We prove that many geodesic properties for homogeneous Randers spheres with $K\equiv1$ can be generalized to the non-Randers case.
Modeling Financial Analysts’ Decision Making via the Pragmatics and Semantics of Earnings Calls

Every fiscal quarter, companies hold earnings calls in which company executives respond to questions from analysts. After these calls, analysts often change their price target recommendations, which are used in equity re- search reports to help investors make deci- sions. In this paper, we examine analysts’ decision making behavior as it pertains to the language content of earnings calls. We identify a set of 20 pragmatic features of analysts’ questions which we correlate with analysts’ pre-call investor recommendations. We also analyze the degree to which semantic and pragmatic features from an earnings call complement market data in predicting analysts’ post-call changes in price targets. Our results show that earnings calls are moderately predictive of analysts’ decisions even though these decisions are influenced by a number of other factors including private communication with company executives and market conditions. A breakdown of model errors indicates disparate performance on calls from different market sectors.
Localization in disordered structures: Breakdown of the self-averaging hypothesis.

We find that the relevant quantities describing the localization of electrons, vibrations, and random walks on random fractals are non-self-averaging. There exists a crossover distance ${\mathit{r}}_{\ifmmode\times\else\texttimes\fi{}}$ that increases logarithmically with the number N of configurations considered in the averages. For vibrations and electrons, the localization exponent changes from 1 below ${\mathit{r}}_{\ifmmode\times\else\texttimes\fi{}}$ to ${\mathit{d}}_{\mathrm{min}}$ above ${\mathit{r}}_{\ifmmode\times\else\texttimes\fi{}}$. For random walks, the exponent changes from ${\mathit{d}}_{\mathit{w}}$/(${\mathit{d}}_{\mathit{w}}$-1) to ${\mathit{d}}_{\mathrm{min}}$${\mathit{d}}_{\mathit{w}}$/(${\mathit{d}}_{\mathit{w}}$-${\mathit{d}}_{\mathrm{min}}$), where ${\mathit{d}}_{\mathit{w}}$ and ${\mathit{d}}_{\mathrm{min}}$ are the fractal dimensions of the random walk and the shortest path on the fractal, respectively. Our results explain the controversies regarding the localization exponent.
Data Analytics and BI Framework based on Collective Intelligence and the Industry 4.0

Now a days, business is subject to frequent changes in operations because of the continuous alignment of changes in strategies and business environment due to rapid changes in human and business resources considering the complexity of businesses. Currently, Business Intelligence is way to handle these frequent changes and risks. In the past BI was so complex thing for business to adopt and only big businesses were able to implement this but now BI is becoming simpler and even web based on cloud, so it is easy even for small and medium business to adopt this to do business in a smart way. The purpose of this literature review is develop a framework of BI and Data Analytics using the collective intelligence, new technologies of the Industry 4.0 paradigm in order to provide a service with low cost and intelligence service for small and medium business which don't have budget to pay an expensive license and hiring an expert to can design the analysis to improve productivity and profitability.
Conditional Neural Style Transfer with Peer-Regularized Feature Transform

This paper introduces a neural style transfer model to conditionally generate a stylized image using only a set of examples describing the desired style. The proposed solution produces high-quality images even in the zero-shot setting and allows for greater freedom in changing the content geometry. This is thanks to the introduction of a novel Peer-Regularization Layer that recomposes style in latent space by means of a custom graph convolutional layer aiming at separating style and content. Contrary to the vast majority of existing solutions our model does not require any pre-trained network for computing perceptual losses and can be trained fully end-to-end with a new set of cyclic losses that operate directly in latent space. An extensive ablation study confirms the usefulness of the proposed losses and of the Peer-Regularization Layer, with qualitative results that are competitive with respect to the current state-of-the-art even in the challenging zero-shot setting. This opens the door to more abstract and artistic neural image generation scenarios and easier deployment of the model in. production
Software Ethology: An Accurate and Resilient Semantic Binary Analysis Framework

When reverse engineering a binary, the analyst must first understand the semantics of the binary's functions through either manual or automatic analysis. Manual semantic analysis is time-consuming, because abstractions provided by high level languages, such as type information, variable scope, or comments are lost, and past analyses cannot apply to the current analysis task. Existing automated binary analysis tools currently suffer from low accuracy in determining semantic function identification in the presence of diverse compilation environments. We introduce Software Ethology, a binary analysis approach for determining the semantic similarity of functions. Software Ethology abstracts semantic behavior as classification vectors of program state changes resulting from a function executing with a specified input state, and uses these vectors as a unique fingerprint for identification. All existing semantic identifiers determine function similarity via code measurements, and suffer from high inaccuracy when classifying functions from compilation environments different from their ground truth source. Since Software Ethology does not rely on code measurements, its accuracy is resilient to changes in compiler, compiler version, optimization level, or even different source implementing equivalent functionality. Tinbergen, our prototype Software Ethology implementation, leverages a virtual execution environment and a fuzzer to generate the classification vectors. In evaluating Tinbergen's feasibility as a semantic function identifier by identifying functions in coreutils-8.30, we achieve a high . 805 average accuracy. Compared to the state-of-the-art, Tinbergen is 1.5 orders of magnitude faster when training, 50% faster in answering queries, and, when identifying functions in binaries generated from differing compilation environments, is 30%-61% more accurate.
Intensive five‐drug combination chemotherapy for adult acute non‐lymphocytic leukemia

Twenty‐two newly diagnosed adults with acute non‐lymphocytic leukemia (ANLL) and 18 who had relapsed from a prior complete remission (CR) obtained with intensive chemotherapy were treated with a combination of Adriamycin, cytosine arabinoside, 6‐thioguanine, prednisone, and vincristine. Eighteen (82%) of the patients undergoing initial induction therapy, including 4 of 6 patients over 60 years of age, achieved CR. Twelve patients (67%) undergoing reinduction therapy also achieved CR. Twenty‐three (77%) of the 30 CRs were obtained with only one course of chemotherapy. The median time to CR for those undergoing initial treatment was 32 days, and the median duration of CR is 10+ months. The median survival for all 22 newly diagnosed patients is 15 + months. This intensive five‐drug regimen is highly effective for the remission induction of both newly diagnosed and previously treated patients with ANLL producing a high percentage of CRs with only one course of therapy.
Deep Learning based Cephalometric Landmark Identification using Landmark-dependent Multi-scale Patches

A deep neural network based cephalometric landmark identification model is proposed. Two neural networks, named patch classification and point estimation, are trained by multi-scale image patches cropped from 935 Cephalograms (of Japanese young patients), whose size and orientation vary based on landmark-dependent criteria examined by orthodontists. The proposed model identifies both 22 hard and 11 soft tissue landmarks. In order to evaluate the proposed model, (i) landmark estimation accuracy by Euclidean distance error between true and estimated values, and (ii) success rate that the estimated landmark was located within the corresponding norm using confidence ellipse, are computed. The proposed model successfully identified hard tissue landmarks within the error range of 1.32 - 3.5 mm and with a mean success rate of 96.4%, and soft tissue landmarks with the error range of 1.16 - 4.37 mm and with a mean success rate of 75.2%. We verify that considering the landmark-dependent size and orientation of patches helps improve the estimation accuracy.
A Compressive Sensing Approach to Cluster Extraction

We use techniques from compressive sensing to design a cluster extraction algorithm by treating the cluster indicator vector as a sparse solution to a linear system involving the graph Laplacian. We show that our algorithm, which we call SingleClusterPursuit, has computational complexity $O(d_{\max}n\log(n))$ and verify numerically that it runs quickly and accurately on large data sets, both synthetic and real. We prove that, for a large class of probabilistic models of graphs with clusters, which includes the popular stochastic block model, the fraction of vertices misclassified by SingleClusterPursuit goes to zero as the size of the cluster goes to infinity.
Deep Precomputed Radiance Transfer for Deformable Objects

We propose, DeepPRT, a deep convolutional neural network to compactly encapsulate the radiance transfer of a freely deformable object for rasterization in real-time. With pre-computation of radiance transfer (PRT) we can store complex light interactions appropriate to the shape of a given object at each surface point for subsequent real-time rendering via fast linear algebra evaluation against the viewing direction and distant light environment. However, performing light transport projection into an efficient basis representation, such as Spherical Harmonics (SH), requires a numerical Monte Carlo integration computation, limiting usage to rigid only objects or highly constrained deformation sequences. The bottleneck, when considering freely deformable objects, is the heavy memory requirement to wield all pre-computations in rendering with global illumination results. We present a compact representation of PRT for deformable objects with fixed memory consumption, which solves diverse non-linear deformations and is shown to be effective beyond the input training set. Specifically, a U-Net is trained to predict the coefficients of the transfer function (SH coefficients in this case), for a given animation's shape query each frame in real-time. We contribute deep learning of PRT within a parametric surface space representation via geometry images using harmonic mapping with a texture space filling energy minimization variant. This surface representation facilitates the learning procedure, removing irrelevant, deformation invariant information; and supports standard convolution operations. Finally, comparisons with ground truth and a recent linear morphable-model method is provided.
Programmable Battery as a Computational Crafting Material for Children

A programmable battery is a tiny computer that children can use to control motors and other devices by manipulating buttons on the board. Since it is simpler to use than other programming and computational tools for children, no detailed instruction is needed to start playing and designing with it. As a result, children can concentrate on their creative activities related to the projects in the workshops. This design also demonstrates the possibilities of computational tools as playthings for children.