Miracle-dz/UniFAR-FTUs huggingface dataset:
Supplementary Material for the Paper: Over-the-Air Adversarial Flickering Attacks against Video Recognition Networks

m > 0 is the desired margin of the original class probability below the adversarial class probability. When loss values are within the desired margin, the quadratic loss term relaxes the relatively steep gradients and momentum of the optimizer, and the difference between the first and second class probabilities approach the desired margin m. When the loss starts rising, the quadratic term gently maintains the desired difference between these two classes, therefore preventing overshoot effects. In order to apply the suggested mechanism on targeted attack, the loss term changed to `m(y, t) = maxi 6=t(yi)−yt+m, while this time, t is the targeted adversarial class.
Near real‐time monitoring of volcanic surface deformation from GPS measurements at Long Valley Caldera, California

Long Valley Caldera in eastern California is an active volcanic area and has shown continued unrest in the last three decades. We have monitored surface deformation from Global Positioning System (GPS) data by using a projection method that we call Targeted Projection Operator (TPO). TPO projects residual time series with secular rates and periodic terms removed onto a predefined spatial pattern. We used the 2009–2010 slow deflation as a target spatial pattern. The resulting TPO time series shows a detailed deformation history including the 2007–2009 inflation, the 2009–2010 deflation, and a recent inflation that started in late‐2011 and is continuing at the present time (November 2012). The recent inflation event is about four times faster than the previous 2007–2009 event. A Mogi source of the recent event is located beneath the resurgent dome at about 6.6 km depth at a rate of 0.009 km3/yr volume change. TPO is simple and fast and can provide a near real‐time continuous monitoring tool without directly looking at all the data from many GPS sites in this potentially eruptive volcanic system.
Financial Stress and Economic Dynamics : the transmission of crises ∗

The recent financial crisis and the associated decline in economic activity have raised some important questions about economic activity and its links to the financial sector. This paper introduces an index of financial stress—an index that was used in real time by the staff of the Federal Reserve Board to monitor the crisis—and shows how stress interacts with real activity, inflation and monetary policy. We examine a variety of questions including the implications of financial stress for the real economy; the implications of shocks to the real economy for financial stress; the role of monetary policy and what constitutes a useful and credible measure of stress in this context. We address these questions using a richly parameterized multivariate Markov-switching VAR model estimated using Bayesian methods. We clearly reject the constant parameter model and find that the negative output effects of a financial stress shock are much more pronounced and long-lasting in times of high financial stress than in normal times. We also find that monetary policy has reacted differently in times of financial stress than otherwise. JEL Classification: E44, C11,C32
Invariant Information Clustering for Unsupervised Image Classiﬁcation and Segmentation: Supplementary Material

We used three generic CNN bases b across our experiments: A (ResNet34 [5]), B (4 convolutional layers) and C (6 convolutional layers). For details see table 1. See table 2 for per-experiment details including b, batch size, input channels, input size, and number of clusters used in overclustering denoted by k. Recall the latter refers to the sole output head for semi-supervised overclustering but to the auxiliary head for unsupervised IIC, where the main head produces output with dimensionality kgt. For segmentation, bilinear resampling is used to resize the network output back to input size for implementational simplicity. Since there is one pooling layer in network C which halves spatial size, this is by a factor of 2.
Meta Reinforcement Learning with Latent Variable Gaussian Processes

such that the successor state depends on the latent system specification hp. This means, we explicitly model the global properties through a shared function f and the task-specific variation using a distribution over the latent variables p(hp). Framing the meta learning problem as a hierarchical Bayesian model means that meta-training becomes inference in a meta-learning model. The meta RL procedures for training and testing are detailed in algorithms 1 and 2, respectively.
Black-box Certification and Learning under Adversarial Perturbations

Clearly, f is a measurable function, and every set U(x) is measurable. However, we get marUf = M , that is, the margin area of f under these perturbations is not measurable, and therefore the adversarial loss with respect to U is not measurable. Note that the same phenomenon can occur for sets U that are always open intervals containing the point x. With the same function f , for perturbation sets
CompactChain:An E�cient Stateless Chain for UTXO-model Blockchain

We propose a stateless blockchain called CompactChain, which compacts the entire state of the Unspent Transaction Output (UTXO) based blockchain systems into two RSA accumulators-Transaction Output (TXO) commitment (representing TXO set), and Spent Transaction Out-put (STXO) commitment (representing STXO set). In this work, we discuss three algorithms - ( i ) To update the TXO and STXO commitments by the miner along with a valid proof; ( ii ) To prove the transaction’s validity by providing a membership witness in TXO commitment and non-membership witness against STXO commitment for a coin being spent by a user. The user also updates the witness for the coin that is not yet spent; ( iii ) Validator verify the above proofs created by the miner and user. The experimental results evaluate the performance of the CompactChain in terms of time consumed by a miner to update the commitments, time consumed by a validator to verify the commitments and validate the transactions. We compare the performance of CompactChain with the existing state-of-art works on stateless blockchains. CompactChain shows a reduction in commitments update complexity and transaction witness size without compromising the system throughput in terms of transactions per second (TPS).
Performance of Non-Orthogonal Multiple Access (NOMA) in mmWave wireless communications for 5G networks

Among the key technologies that have been identified as capacity boosters for fifth generation - 5G - mobile networks, are millimeter wave (mmWave) transmissions and non-orthogonal multiple access (NOMA). The large amount of spectrum available at mmWave frequencies combined with a more effective use of available resources, helps improving the overall capacity. NOMA, unlike orthogonal multiple access (OMA) methods, allows sharing the same frequency resources at the same time, by implementing adaptive power allocation. In this paper we present a performance analysis of NOMA in mmWave cells, using OMA as a benchmark. The results show that up to 70% channel capacity gain can be achieved when using NOMA instead of OMA. Most of the NOMA studies to the date focus mainly on the capacity gain; therefore we also present an analysis of the required signal-to-interference-plus-noise ratio (SINR) in NOMA, needed to achieve a target block error rate (BLER). The results show that an average SINR penalty of 12 dB is present when choosing NOMA over OMA.
Doubly Nested Network for Resource-Efficient Inference

We propose a new anytime neural network which allows partial evaluation by subnetworks with different widths as well as depths. Compared to conventional anytime networks only with the depth controllability, the increased architectural diversity leads to higher resource utilization and consequent performance improvement under various and dynamic resource budgets. We highlight architectural features to make our scheme feasible as well as efficient, and show its effectiveness in image classification tasks.
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods

We consider the problem of approximate Bayesian parameter inference in non-linear state-space models with intractable likelihoods. Sequential Monte Carlo with approximate Bayesian computations (SMC-ABC) is one approach to approximate the likelihood in this type of models. However, such approximations can be noisy and computationally costly which hinders efficient implementations using standard methods based on optimisation and Monte Carlo methods. We propose a computationally efficient novel method based on the combination of Gaussian process optimisation and SMC-ABC to create a Laplace approximation of the intractable posterior. We exemplify the proposed algorithm for inference in stochastic volatility models with both synthetic and real-world data as well as for estimating the Value-at-Risk for two portfolios using a copula model. We document speed-ups of between one and two orders of magnitude compared to state-of-the-art algorithms for posterior inference.
Multivariate patent analysis—Using chemometrics to analyze collections of chemical and pharmaceutical patents

Patents are an important source of technological knowledge, but the amount of existing patents is vast and quickly growing. This makes development of tools and methodologies for quickly revealing patterns in patent collections important. In this paper, we describe how structured chemometric principles of multivariate data analysis can be applied in the context of text analysis in a novel combination with common machine learning preprocessing methodologies. We demonstrate our methodology in 2 case studies. Using principal component analysis (PCA) on a collection of 12338 patent abstracts from 25 companies in big pharma revealed sub‐fields which the companies are active in. Using PCA on a smaller collection of patents retrieved by searching for a specific term proved useful to quickly understand how patent classifications relate to the search term. By using orthogonal projections to latent structures (O‐PLS) on patent classification schemes, we were able to separate patents on a more detailed level than using PCA. Lastly, we performed multi‐block modeling using OnPLS on bag‐of‐words representations of abstracts, claims, and detailed descriptions, respectively, showing that semantic variation relating to patent classification is consistent across multiple text blocks, represented as globally joint variation. We conclude that using machine learning to transform unstructured data into structured data provide a good preprocessing tool for subsequent chemometric multivariate data analysis and provides an easily interpretable and novel workflow to understand large collections of patents. We demonstrate this on collections of chemical and pharmaceutical patents.
Transmural dispersion of repolarization and atrial electromechanical coupling: complementary indices for quantifying cardiac electrical heterogeneity in patients with conversion disorder

We read with great interest the article entitled “P-wave and QT dispersion in patients with conversion disorder” by Izci et al 1 in Therapeutics and Clinical Risk Management . In this well designed research, Izci et al studied QT dispersion (QTd) and P-wave dispersion (Pd) in patients with conversion disorder (CD). In conclusion, they reported that corrected QT (QTc) and QTd values were significantly altered in patients with CD when compared to healthy controls, but that there was no significant difference in terms of Pd. It has been postulated that the relationship between somatoform disorders and CD is related to altered
First measurement of the CP-violating phase in B0s→ϕϕ decays

A first flavor-tagged measurement of the time-dependent CP-violating asymmetry in B0s→ϕϕ decays is presented. In this decay channel, the CP-violating weak phase arises due to CP violation in the interference between B0s-B¯¯¯0s mixing and the b→ss¯s gluonic penguin decay amplitude. Using a sample of pp collision data corresponding to an integrated luminosity of 1.0 fb−1 and collected at a center-of-mass energy of 7 TeV with the LHCb detector, 880 B0s→ϕϕ signal decays are obtained. The CP-violating phase is measured to be in the interval [−2.46,−0.76] rad at a 68% confidence level. The p value of the standard model prediction is 16%.
A Comparison of Social Media Marketing Between B2B, B2C and Mixed Business Models

This paper explores the implicit assumption in the growing body of literature that social media usage is fundamentally different in business-to-business (B2B) companies than in the extant business-to-consumer (B2C) literature. Sashi’s (2012) customer engagement cycle is utilized to compare organizational practices in relation to social media marketing in B2B, B2C, Mixed B2B/B2C and B2B2C business models. Utilizing 449 responses to an exploratory panel based survey instrument, we clearly identify differences in social media usage and its perceived importance as a communications channel. In particular we identify distinct differences in the relationship between social media importance and the perceived effectiveness of social media marketing across business models. Our results indicate that B2B social media usage is distinct from B2C, Mixed and B2B2C business model approaches. Specifically B2B organizational members perceive social media to have a lower overall effectiveness as a channel and identify it as less important for relationship oriented usage than other business models.
Development of an orthogonal-stripe CdZnTe gamma radiation imaging spectrometer

We report performance measurements of a sub-millimeter resolution CdZnTe strip detector developed as a prototype for astronomical instruments operating with good efficiency in the 30-300 keV photon energy range. The prototype is a 1.4 mm thick, 64/spl times/64 contact stripe CdZnTe array of 0.375 mm pitch in both dimensions. Pulse height spectra were recorded in orthogonal-stripe coincidence mode which demonstrate room-temperature energy resolution 5:1. Good response is also demonstrated at higher energies using a coplanar grid readout configuration. Spatial resolution capabilities finer than the stripe pitch are demonstrated. We present the image of a /sup 133/Ba source viewed through a collimator slit produced by a 4/spl times/4 stripe detector segment. Charge signals from electron and hole collecting contacts are also discussed.
Single-source Attention Path Prediction Multi-source Attention Predicted Observed

We present an interpretable framework for path prediction that leverages dependencies between agents’ behaviors and their spatial navigation environment. We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-view image of the navigation scene. We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where to look in a large image of the scene when solving the path prediction task. Our method can attend to any area, or combination of areas, within the raw image (e.g., road intersections) when predicting the trajectory of the agent. This allows us to visualize fine-grained semantic elements of navigation scenes that influence the prediction of trajectories. To study the impact of space on agents’ trajectories, we build a new dataset made of top-view images of hundreds of scenes (Formula One racing tracks) where agents’ behaviors are heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net successfully attends to these salient regions. Additionally, CAR-Net reaches state-of-the-art accuracy on the standard trajectory forecasting benchmark, Stanford Drone Dataset (SDD). Finally, we show CAR-Net’s ability to generalize to unseen scenes.
Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification

—Convolutional Neural Networks (CNNs) have gained a remarkable success on many real-world problems in recent years. However, the performance of CNNs is highly relied on their architectures. For some state-of-the-art CNNs, their architectures are hand-crafted with expertise in both CNNs and the investigated problems. To this end, it is difﬁcult for researchers, who have no extended expertise in CNNs, to explore CNNs for their own problems of interest. In this paper, we propose an automatic architecture design method for CNNs by using genetic algorithms, which is capable of discovering a promising architecture of a CNN on handling image classiﬁcation tasks. The proposed algorithm does not need any pre-processing before it works, nor any post-processing on the discovered CNN, which means it is completely automatic. The proposed algorithm is validated on widely used benchmark datasets, by comparing to the state-of-the-art peer competitors covering eight manually designed CNNs, four semi-automatically designed CNNs and additional four automatically designed CNNs. The experimental results indicate that the proposed algorithm achieves the best classiﬁcation accuracy consistently among manually and automatically designed CNNs. Furthermore, the proposed algorithm also shows the competitive classiﬁcation accuracy to the semi-automatic peer competitors, while reducing 10 times of the parameters. In addition, on the average the proposed algorithm takes only one percentage of computational resource compared to that of all the other architecture discovering algorithms. Experimental codes and the discovered architectures along with the trained weights are made public to the interested readers.
A Survey on Knowledge-Enhanced Pre-trained Language Models

Natural Language Processing (NLP) has been revolutionized by the use of Pre-trained Language Models (PLMs) such as BERT. Despite setting new records in nearly every NLP task, PLMs still face a number of challenges including poor interpretability, weak reasoning capability, and the need for a lot of expensive annotated data when applied to downstream tasks. By integrating external knowledge into PLMs, \textit{\underline{K}nowledge-\underline{E}nhanced \underline{P}re-trained \underline{L}anguage \underline{M}odels} (KEPLMs) have the potential to overcome the above-mentioned limitations. In this paper, we examine KEPLMs systematically through a series of studies. Specifically, we outline the common types and different formats of knowledge to be integrated into KEPLMs, detail the existing methods for building and evaluating KEPLMS, present the applications of KEPLMs in downstream tasks, and discuss the future research directions. Researchers will benefit from this survey by gaining a quick and comprehensive overview of the latest developments in this field.
Local Critic Training for Model-Parallel Learning of Deep Neural Networks

This paper proposes a novel approach to train deep neural networks in a parallelized manner by unlocking the layer-wise dependency of backpropagation training. The approach employs additional modules called local critic networks besides the main network model to be trained, which estimate the output of the main network in order to obtain error gradients without complete feedforward and backward propagation processes. We propose a cascaded learning strategy for these local networks so that parallelized training of different layer groups is possible. Experimental results show the effectiveness of the proposed approach and suggest guidelines for determining appropriate algorithm parameters. In addition, we demonstrate that the approach can be also used for structural optimization of neural networks, computationally efficient progressive inference, and ensemble classification for performance improvement.
Ongoing Brain Activity Fluctuations Directly Account for Intertrial and Indirectly for Intersubject Variability in Stroop Task Performance

Recent studies have established a relation between ongoing brain activity fluctuations and intertrial variability in evoked neural responses, perception, and motor performance. Here, we extended these investigations into the domain of cognitive control. Using functional neuroimaging and a sparse event-related design (with long and unpredictable intervals), we measured ongoing activity fluctuations and evoked responses in volunteers performing a Stroop task with color-word interference. Across trials, prestimulus activity of several regions predicted subsequent response speed and across subjects this effect scaled with the Stroop effect size, being significant only in subjects manifesting behavioral interference. These effects occurred only in task relevant as the dorsal anterior cingulate and dorsolateral prefrontal cortex as well as ventral visual areas sensitive to color and visual words. Crucially, in subjects showing a Stroop effect, reaction times were faster when prestimulus activity was higher in task-relevant (color) regions and slower when activity was higher in irrelevant (word form) regions. These findings suggest that intrinsic brain activity fluctuations modulate neural mechanisms underpinning selective voluntary attention and cognitive control. Rephrased in terms of predictive coding models, ongoing activity can hence be considered a proxy of the precision (gain) with which prediction error signals are transmitted upon sensory stimulation.
Thermal transfer in Rayleigh–Bénard cell with smooth or rough boundaries

Several Rayleigh–Bénard experiments in water are performed with smooth or rough boundaries. We present new thermal transfer measurements obtained with large roughness elements arranged in a square lattice. The data are compared to previous data obtained with smaller elements in the same cell (Tisserand et al., Phys. Fluids, vol. 23, 2011). Experiments in the same apparatus without roughness are presented, as reference results, to allow for comparison. In the rough case, several regimes of heat transfer are identified: one similar to the smooth case, an enhanced heat transfer regime characterized by a modification of the Nusselt versus Rayleigh number relation and a third part where the relation can be similar to a smooth one with a corrected prefactor.
1 2 N ov 2 01 8 Stochastic Gradient Descent with Hyperbolic-Tangent Decay on Classification

Learning rate scheduler has been a critical issue in the deep neural network training. Several schedulers and methods have been proposed, including step decay scheduler, adaptive method, cosine scheduler and cyclical scheduler. This paper proposes a new scheduling method, named hyperbolic-tangent decay (HTD). We run experiments on several benchmarks such as: ResNet, Wide ResNet and DenseNet for CIFAR-10 and CIFAR-100 datasets, LSTM for PAMAP2 dataset, ResNet on ImageNet and FashionMNIST datasets. In our experiments, HTD outperforms step decay and cosine scheduler in nearly all cases, while requiring less hyperparameters than step decay, and more flexible than cosine scheduler. Code is available at https://github.com/BIGBALLON/HTD.
X ray spectroscopy on Ξ − atoms (J-PARC E03, E07 and future)

X-ray spectroscopy of hadronic atoms is a powerful method to study strong interaction between hadrons and nuclei. At J-PARC, we have conducted two experiments, J-PARC E07 and E03, for hadronic atoms with a doubly strange hyperon, Ξ − , aiming at the world-ﬁrst detection of their X-rays. The ﬁrst measurement is performed as a byproduct of J-PARC E07 experiment with the hybrid emulsion technique. The second one, J-PARC E03, is a dedicated experiment for detection of Ξ − Fe atom X rays. The preliminary results and the present status of E07 and E03 are shown in this article. Future prospects of Ξ − - atomic X-ray spectroscopy are also discussed. A new measurement has been proposed for detecting Ξ − C atom X rays, where a novel Ξ − tracking method will be applied to realize an improved signal to noise ratio.