Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral Graph Attention Network

Variants of Graph Neural Networks (GNNs) for representation learning have been proposed recently and achieved fruitful results in various fields. Among them, graph attention networks (GATs) first employ a self-attention strategy to learn attention weights for each edge in the spatial domain. However, learning the attentions over edges only pays attention to the local information of graphs and greatly increases the number of parameters. In this paper, we first introduce attentions in the spectral domain of graphs. Accordingly, we present Spectral Graph Attention Network (SpGAT) that learn representations for different frequency components regarding weighted filters and graph wavelets bases. In this way, SpGAT can better capture global patterns of graphs in an efficient manner with much fewer learned parameters than that of GAT. We thoroughly evaluate the performance of SpGAT in the semi-supervised node classification task and verified the effectiveness of the learned attentions in the spectral domain.
Pretraining Image Encoders without Reconstruction via Feature Prediction Loss

This work investigates three methods for calculating loss for autoencoder-based pretraining of image encoders: The commonly used reconstruction loss, the more recently introduced deep perceptual similarity loss, and a feature prediction loss proposed here; the latter turning out to be the most efficient choice. Standard auto-encoder pretraining for deep learning tasks is done by comparing the input image and the reconstructed image. Recent work shows that predictions based on embeddings generated by image autoencoders can be improved by training with perceptual loss, i.e., by adding a loss network after the decoding step. So far the autoencoders trained with loss networks implemented an explicit comparison of the original and reconstructed images using the loss network. However, given such a loss network we show that there is no need for the time-consuming task of decoding the entire image. Instead, we propose to decode the features of the loss network, hence the name “feature prediction loss”. To evaluate this method we perform experiments on three standard publicly available datasets (LunarLander-v2, STL-10, and SVHN) and compare six different procedures for training image encoders (pixel-wise, perceptual similarity, and feature prediction losses; combined with two variations of image and feature encoding/decoding). The embedding-based prediction results show that encoders trained with feature prediction loss is as good or better than those trained with the other two losses. Additionally, the encoder is significantly faster to train using feature prediction loss in comparison to the other losses. The method implementation used in this work is available online.11https://github.com/guspih/Perceptual-Autoencoders
2012 IEEE VEHICULAR NETWORKING CONFERENCE (VNC): DEMO SUMMARIES

—A 60 GHz full HD wireless connection for an intra-vehicular multimedia wireless network is demonstrated. The 60 GHz HDMI wireless connection consists of a serializer, a deserializer, a 60 GHz transmitter, and a receiver. The 60 GHz full-HD wireless connection supports a data rate of more than 3 Gb/s covering a 1080p full-HD display.
Investigating the gedanken experiment to destroy the event horizon of a regular black hole

Recently, Li and Bambi proposed a hypothesis that the event horizon of a regular black hole can be destroyed because these objects have no gravitational singularity and therefore they are not protected by the weak cosmic censorship conjecture (WCCC). In this paper, to test their hypothesis, we perform the new version of the gedanken experiments proposed by Sorce and Wald to overcharge a near extremal static electrically regular black hole. After introducing the stability condition of the spacetime and the null energy condition of matter fields, we derive the first-order and second-order perturbation inequalities of the perturbation matter fields based on the Iyer-Wald formalism. As a result, we find that these regular black holes cannot be destroyed under the second-order approximation after these two perturbation inequalities are taken into account, even though they are not protected by the WCCC. Our results indicate that there might be some deeper mechanisms to protect the event horizon of the black holes.
There are no exotic actions of diffeomorphism groups on 1-manifolds

Let $M$ be a manifold, $N$ a 1-dimensional manifold. Assuming $r \neq \dim(M)+1$, we show that any nontrivial homomorphism $\rho: \text{Diff}^r_c(M)\to \text{Homeo}(N)$ has a standard form: necessarily $M$ is $1$-dimensional, and there are countably many embeddings $\phi_i: M\to N$ with disjoint images such that the action of $\rho$ is conjugate (via the product of the $\phi_i$) to the diagonal action of $\text{Diff}^r_c(M)$ on $M \times M \times . $ on $\bigcup_i \phi_i(M)$, and trivial elsewhere. This solves a conjecture of Matsumoto. We also show that the groups $\text{Diff}^r_c(M)$ have no countable index subgroups.
NSBchain: A Secure Blockchain Framework for Network Slicing Brokerage

With the advent of revolutionary technologies, such as virtualization and softwarization, a novel concept for 5G networks and beyond has been unveiled: Network Slicing. Initially driven by the research community, standardization bodies as 3GPP have embraced it as a promising solution to revolutionize the traditional mobile telecommunication market by enabling new business models opportunities. Network Slicing is envisioned to open up the telecom market to new players such as Industry Verticals, e.g., automotive, smart factories, e-health, etc. Given the large number of potential new business players, dubbed as network tenants, novel solutions are required to accommodate their needs in a cost-efficient and secure manner. In this paper, we propose NSBchain, a novel network slicing brokering (NSB) solution, which leverages on the widely adopted Blockchain technology to address the new business models needs beyond traditional network sharing agreements. NSBchain defines a new entity, the Intermediate Broker (IB), which enables Infrastructure Providers (InPs) to allocate network resources to IBs through smart contracts and IBs to assign and re-distribute their resources among tenants in a secure, automated and scalable manner. We conducted an extensive performance evaluation by means of an open-source blockchain platform that proves the feasibility of our proposed framework considering a large number of tenants and two different consensus algorithms.
Software Design and Development of Attitude Determination and Control Subsystem for SSS-1P

The Attitude Determination and Control System is one of the critical boards of any satellite, specially the micro-satellites. The ADCS is the bridge linking sensors data to actuators by several computationally complex algorithms such as Extended Kalman Filter, Detumbling and Tumbling controller and so on. In this thesis, I will focus on the implementation of each of the required algorithms and then make a whole integrated package. This thesis focuses on developing the first version of the on-board computer software for ADCS of SSS-1P and required preliminary tests to assure the minimum performance of the provided package with Software-In-the-Loop and Processor-In-the-Loop tests. In this thesis, I will introduce the fundamental dynamic and kinematic non-linear equations. The linearization for a specific prerational condition and the assumptions to achieve the linearization will be introduced. The model will be linearized for a specific operating condition for the design and development of the controllers. The disturbances including atmospheric drag, solar radiation and so on are also introduced with regarding to the operating Altitude (500 ~ 600 km). The brief introduction of different magnetic field models and their differences will be introduced as well. In addition, the SGP4 predictive model to estimate the position and the velocity of the satellite discussed. In addition, the thesis describes the software development phase, some additional algorithms that I implemented but they are not a part of mathematical models, such as system manager, time manager and so on with pseudo code. The architecture of the software is also described in this section. Finally, the numerical simulations are analysed and the simple compression between tests results from PIL, SIL, and MATLAB simulation are presented. This section will presents the validation and the trustworthy of the final package.
AutoCogniSys: IoT Assisted Context-Aware Automatic Cognitive Health Assessment

Cognitive impairment has become epidemic in older adult population. The recent advent of tiny wearable and ambient devices, a.k. a Internet of Things (IoT) provides ample platforms for continuous functional and cognitive health assessment of older adults. In this paper, we design, implement and evaluate AutoCogniSys, a context-aware automated cognitive health assessment system, combining the sensing powers of wearable physiological (Electrodermal Activity, Photoplethysmography) and physical (Accelerometer, Object) sensors in conjunction with ambient sensors. We design appropriate signal processing and machine learning techniques, and develop an automatic cognitive health assessment system in a natural older adults living environment. We validate our approaches using two datasets: (i) a naturalistic sensor data streams related to Activities of Daily Living and mental arousal of 22 older adults recruited in a retirement community center, individually living in their own apartments using a customized inexpensive IoT system (IRB #HP-00064387) and (ii) a publicly available dataset for emotion detection. The performance of AutoCogniSys attests max. 93% of accuracy in assessing cognitive health of older adults.
LAXARY: A Trustworthy Explainable Twitter Analysis Model for Post-Traumatic Stress Disorder Assessment

Veteran mental health is a significant national problem as large number of veterans are returning from the recent war in Iraq and continued military presence in Afghanistan. While significant existing works have investigated twitter posts-based Post Traumatic Stress Disorder (PTSD) assessment using blackbox machine learning techniques, these frameworks cannot be trusted by the clinicians due to the lack of clinical explainabil-ity. To obtain the trust of clinicians, we explore the big question, can twitter posts provide enough information to fill up clinical PTSD assessment surveys that have been traditionally trusted by clinicians? To answer the above question, we propose, LAXARY (Linguistic Analysis-based Exaplainable Inquiry) model, a novel Explainable Artificial Intelligent (XAI) model to detect and represent PTSD assessment of twitter users using a modified Linguistic Inquiry and Word Count (LIWC) analysis. First, we employ clinically validated survey tools for collecting clinical PTSD assessment data from real twitter users and develop a PTSD Linguistic Dictionary using the PTSD assessment survey results. Then, we use the PTSD Linguistic Dictionary along with machine learning model to fill up the survey tools towards detecting PTSD status and its intensity of corresponding twitter users. Our experimental evaluation on 210 clinically validated veteran twitter users provides promising accuracies of both PTSD classification and its intensity estimation. We also evaluate our developed PTSD Linguistic Dictionary's reliability and validity.
A spectral approach to network design

We present a spectral approach to design approximation algorithms for network design problems. We observe that the underlying mathematical questions are the spectral rounding problems, which were studied in spectral sparsification and in discrepancy theory. We extend these results to incorporate additional linear constraints, and show that they can be used to significantly extend the scope of network design problems that can be solved. Our algorithm for spectral rounding is an iterative randomized rounding algorithm based on the regret minimization framework. In some settings, this provides an alternative spectral algorithm to achieve constant factor approximation for survivable network design, and partially answers a question of Bansal about survivable network design with concentration property. We also show that the spectral rounding results have many other applications, including weighted experimental design and additive spectral sparsification.
Multiscale global sensitivity analysis for stochastic chemical systems

Sensitivity analysis is routinely performed on simplified surrogate models as the cost of such analysis on the original model may be prohibitive. Little is known in general about the induced bias on the sensitivity results. Within the framework of chemical kinetics, we provide a full justification of the above approach in the case of variance based methods provided the surrogate model results from the original one through the thermodynamic limit. We also provide illustrative numerical examples in context of a Michaelis--Menten system and a biochemical reaction network describing a genetic oscillator.
A Label Proportions Estimation Technique for Adversarial Domain Adaptation in Text Classification

Many text classification tasks are domain-dependent, and various domain adaptation approaches have been proposed to predict unlabeled data in a new domain. Domain-adversarial neural networks (DANN) and their variants have been used widely recently and have achieved promising results for this problem. However, most of these approaches assume that the label proportions of the source and target domains are similar, which rarely holds in most real-world scenarios. Sometimes the label shift can be large and the DANN fails to learn domain-invariant features. In this study, we focus on unsupervised domain adaptation of text classification with label shift and introduce a domain adversarial network with label proportions estimation (DAN-LPE) framework. The DAN-LPE simultaneously trains a domain adversarial net and processes label proportions estimation by the confusion of the source domain and the predictions of the target domain. Experiments show the DAN-LPE achieves a good estimate of the target label distributions and reduces the label shift to improve the classification performance.
Hybrid Autoregressive Transducer (HAT)

This paper proposes and evaluates the hybrid autoregressive transducer (HAT) model, a time-synchronous encoder-decoder model that preserves the modularity of conventional automatic speech recognition systems. The HAT model provides a way to measure the quality of the internal language model that can be used to decide whether inference with an external language model is beneficial or not. We evaluate our proposed model on a large-scale voice search task. Our experiments show significant improvements in WER compared to the state-of-the-art approaches1.
A Unified View of Label Shift Estimation

Label shift describes the setting where although the label distribution might change between the source and target domains, the class-conditional probabilities (of data given a label) do not. There are two dominant approaches for estimating the label marginal. BBSE, a moment-matching approach based on confusion matrices, is provably consistent and provides interpretable error bounds. However, a maximum likelihood estimation approach, which we call MLLS, dominates empirically. In this paper, we present a unified view of the two methods and the first theoretical characterization of the likelihood-based estimator. Our contributions include (i) conditions for consistency of MLLS, which include calibration of the classifier and a confusion matrix invertibility condition that BBSE also requires; (ii) a unified view of the methods, casting the confusion matrix as roughly equivalent to MLLS for a particular choice of calibration method; and (iii) a decomposition of MLLS's finite-sample error into terms reflecting the impacts of miscalibration and estimation error. Our analysis attributes BBSE's statistical inefficiency to a loss of information due to coarse calibration. We support our findings with experiments on both synthetic data and the MNIST and CIFAR10 image recognition datasets.
Business (mis)Use Cases of Generative AI

Generative AI is a class of machine learning technology that learns to generate new data from training data. While deep fakes and media-and art-related generative AI breakthroughs have recently caught people's attention and imagination, the overall area is in its infancy for business use. Further, little is known about generative AI's potential for malicious misuse at large scale. Using co-creation design fictions with AI engineers, we explore the plausibility and severity of business misuse cases.
The physical and mechanical properties of hafnium orthosilicate: experiments and first-principles calculations

Hafnium orthosilicate (HfSiO4: hafnon) has been proposed as an environmental barrier coating (EBC) material to protect silicon coated, silicon-based ceramic materials at high temperatures and as a candidate dielectric material in microelectronic devices. It can naturally form at the interface between silicon dioxide (SiO2) and hafnia (HfO2). When used in these applications, its coefficient of thermal expansion (CTE) should match that of silicon and SiC composites to reduce the stored elastic strain energy, and thus risk of failure of these systems. The physical, mechanical, thermodynamic and thermal transport properties of hafnon have been investigated using a combination of both density functional theory (DFT) calculations and experimental assessments. The average linear coefficient of thermal expansion (CTE) calculated using the quasi-harmonic approximation increase from 3.06 10-6 K-1 to 6.36 10-6 K-1, as the temperature increases from 300 to 1500 K, in agreement with both X-ray diffraction lattice parameter and dilatometry measurements. The predicted thermal conductivity from Boltzmann transport theory was approximately 18 W/m.K at 300K. Both hot disk and laser flash measurements gave a thermal conductivity of 13.3 W/m.K. This slightly lower value is indicative of residual disorder in the experimental samples that was absent in the theoretical analysis. First-principles calculations and nanoindentation techniques were used to assess the ambient temperature elastic constants and bulk modulus respectively. The elastic properties obtained by both approaches agreed to within 5% validating the computational approach and its future use for study of the thermomechanical properties of other oxides or silicates.
On Characterizing the Quantum Geometry Underlying Asymptotic Safety

The asymptotic safety program builds on a high-energy completion of gravity based on the Reuter fixed point, a non-trivial fixed point of the gravitational renormalization group flow. At this fixed point the canonical mass-dimension of coupling constants is balanced by anomalous dimensions induced by quantum fluctuations such that the theory enjoys quantum scale invariance in the ultraviolet. The crucial role played by the quantum fluctuations suggests that the geometry associated with the fixed point exhibits non-manifold like properties. In this work, we continue the characterization of this geometry employing the composite operator formalism based on the effective average action. Explicitly, we give a relation between the anomalous dimensions of geometric operators on a background d-sphere and the stability matrix encoding the linearized renormalization group flow in the vicinity of the fixed point. The eigenvalue spectrum of the stability matrix is analyzed in detail and we identify a “perturbative regime” where the spectral properties are governed by canonical power counting. Our results recover the feature that quantum gravity fluctuations turn the (classically marginal) R2-operator into a relevant one. Moreover, we find strong indications that higher-order curvature terms present in the two-point function play a crucial role in guaranteeing the predictive power of the Reuter fixed point.
KPNet: Towards Minimal Face Detector

The small receptive field and capacity of minimal neural networks limit their performance when using them to be the backbone of detectors. In this work, we find that the appearance feature of a generic face is discriminative enough for a tiny and shallow neural network to verify from the background. And the essential barriers behind us are 1) the vague definition of the face bounding box and 2) tricky design of anchor-boxes or receptive field. Unlike most top-down methods for joint face detection and alignment, the proposed KPNet detects small facial keypoints instead of the whole face by in a bottom-up manner. It first predicts the facial landmarks from a low-resolution image via the well-designed fine-grained scale approximation and scale adaptive soft-argmax operator. Finally, the precise face bounding boxes, no matter how we define it, can be inferred from the keypoints. Without any complex head architecture or meticulous network designing, the KPNet achieves state-of-the-art accuracy on generic face detection and alignment benchmarks with only $\sim1M$ parameters, which runs at 1000fps on GPU and is easy to perform real-time on most modern front-end chips.
A Bayesian Nonparametric Latent Space Approach to Modeling Evolving Communities in Dynamic Networks.

The evolution of communities in dynamic (time-varying) network data is a prominent topic of interest. A popular approach to understanding these dynamic networks is to embed the dyadic relations into a latent metric space. While methods for clustering with this approach exist for dynamic networks, they all assume a static community structure. This paper presents a Bayesian nonparametric model for dynamic networks that can model networks with evolving community structures. Our model extends existing latent space approaches by explicitly modeling the additions, deletions, splits, and mergers of groups with a hierarchical Dirichlet process hidden Markov model. Our proposed approach, the hierarchical Dirichlet process latent position clustering model (HDP-LPCM), incorporates transitivity, models both individual and group level aspects of the data, and avoids the computationally expensive selection of the number of groups required by most popular methods. We provide a Markov chain Monte Carlo estimation algorithm and apply our method to synthetic and real-world networks to demonstrate its performance.
On the nature of the soft γ-ray emission in the hard state of the black hole transient GRS 1716−249

The black hole transient GRS 1716-249 was monitored from the radio to the gamma-ray band during its 2016-2017 outburst. This paper focuses on the Spectral Energy Distribution (SED) obtained in 2017 February-March, when GRS 1716-249 was in a bright hard spectral state. The soft gamma-ray data collected with the INTEGRAL/SPI telescope show the presence of a spectral component which is in excess of the thermal Comptonisation emission. This component is usually interpreted as inverse Compton emission from a tiny fraction of non-thermal electrons in the X-ray corona. We find that hybrid thermal/non-thermal Comptonisation models provide a good fit to the X/gamma-ray spectrum of GRS 1716-249. The best-fit parameters are typical of the bright hard state spectra observed in other black hole X-ray binaries. Moreover, the magnetised hybrid Comptonisation model BELM provides an upper limit on the intensity of the coronal magnetic field of about 1E+06 G. Alternatively, this soft gamma-ray emission could originate from synchrotron emission in the radio jet. In order to test this hypothesis, we fit the SED with the irradiated disc plus Comptonisation model combined with the jet internal shock emission model ISHEM. We found that a jet with an electron distribution of p~2.1 can reproduce the soft gamma-ray emission of GRS 1716-249. However, if we introduce the expected cooling break around 10 keV, the jet model can no longer explain the observed soft gamma-ray emission, unless the index of the electron energy distribution is significantly harder (p<2).