Reviewerly/exHarmony huggingface dataset:
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?

It is believed that a model-based approach for reinforcement learning (RL) is the key to reduce sample complexity. However, the understanding of the sample optimality of model-based RL is still largely missing, even for the linear case. This work considers sample complexity of finding an $\epsilon$-optimal policy in a Markov decision process (MDP) that admits a linear additive feature representation, given only access to a generative model. We solve this problem via a plug-in solver approach, which builds an empirical model and plans in this empirical model via an arbitrary plug-in solver. We prove that under the anchor-state assumption, which implies implicit non-negativity in the feature space, the minimax sample complexity of finding an $\epsilon$-optimal policy in a $\gamma$-discounted MDP is $O(K/(1-\gamma)^3\epsilon^2)$, which only depends on the dimensionality $K$ of the feature space and has no dependence on the state or action space. We further extend our results to a relaxed setting where anchor-states may not exist and show that a plug-in approach can be sample efficient as well, providing a flexible approach to design model-based algorithms for RL.
Task Offloading for Vehicular Edge Computing: A Learning-Based Intent-Aware Approach

Air-ground integrated vehicular edge computing (AGI-VEC) has emerged as an effective solution for task processing in vehicular networks. However, due to vehicle mobility, the network topology and available computing resources vary rapidly and are difficult to predict. In this paper, we develop a novel task offloading framework for AGI-VEC, which is called the learning-based Intent-aware Upper Confidence Bound (IUCB) algorithm. IUCB enables a UV to learn the long-term optimal task offloading strategy while satisfying the long-term ultra-reliable low-latency communication (URLLC) constraints in a best effort way under information uncertainty. Simulation results confirm that the proposed algorithm can approach the optimal performance.
Advances in Wearable Sensors: Signalling the Provenance of Garments Using Radio Frequency Watermarks

There is a significant nascent market for ethically produced products with enormous commercial potential around the world. A reliable method to signal the provenance of products is therefore critical for industry, given that competition based on price is not a viable strategy. The ability to trace and signal ethical treatment of animals is also of significant value to textiles manufactures. The efficacy of such a method can be measured with respect to the cost of implementation, scalability, and the difficulty of counterfeiting. The key to traceability is to win the trust of the consumer about the veracity of this information. Wearable sensors make it possible to monitor and improve the management of traceability and/or provenance. In this paper, we introduce a method for signalling the provenance of garments using radio frequency watermarks. The proposed model consists of two levels of authentication that are easy to use by legitimate vendors, but extremely difficult to imitate or hack, because the watermark is built-in and based on the radiation signature of electroactive materials.
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds

Gaussian Graphical Models (GGMs) have wide-ranging applications in machine learning and the natural and social sciences. In most of the settings in which they are applied, the number of observed samples is much smaller than the dimension and they are assumed to be sparse. While there are a variety of algorithms (e.g. Graphical Lasso, CLIME) that provably recover the graph structure with a logarithmic number of samples, they assume various conditions that require the precision matrix to be in some sense well-conditioned. Here we give the first polynomial-time algorithms for learning attractive GGMs and walk-summable GGMs with a logarithmic number of samples without any such assumptions. In particular, our algorithms can tolerate strong dependencies among the variables. Our result for structure recovery in walk-summable GGMs is derived from a more general result for efficient sparse linear regression in walk-summable models without any norm dependencies. We complement our results with experiments showing that many existing algorithms fail even in some simple settings where there are long dependency chains, whereas ours do not.
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection

While the self-attention mechanism has been widely used in a wide variety of tasks, it has the unfortunate property of a quadratic cost with respect to the input length, which makes it difficult to deal with long inputs. In this paper, we present a method for accelerating and structuring self-attentions: Sparse Adaptive Connection (SAC). In SAC, we regard the input sequence as a graph and attention operations are performed between linked nodes. In contrast with previous self-attention models with pre-defined structures (edges), the model learns to construct attention edges to improve task-specific performances. In this way, the model is able to select the most salient nodes and reduce the quadratic complexity regardless of the sequence length. Based on SAC, we show that previous variants of self-attention models are its special cases. Through extensive experiments on neural machine translation, language modeling, graph representation learning and image classification, we demonstrate SAC is competitive with state-of-the-art models while significantly reducing memory cost.
Integrated Cognition: A Survey of Systems

A survey of cognitive architectures and systems is presented based on a reference framework for integrated cognition (INCOG). Each cognitive architecture/system is described briefly and evaluated with respect to its support for cognitive capabilities along the six dimensions of the INCOG framework. Hypotheses are formulated about the potential contributions of existing systems to a cognitive system covering all the capabilities of the INCOG framework. Relationships between this framework and an emerging Standard Model of the Mind are discussed. Finally, some generalizations of the survey results are presented.
Approaches to Scaling Agile

The evolution of agile approaches to projects and other organizational endeavors brought with it the emergence of approaches to scaling agile. My intent in the overview and discussion that follows here is to provide exactly that – overview and discussion. I’m focusing on three approaches to scaling agile, specifically Scrum, that are relatively well known. There are three main approaches to scaling agile that are particularly important to know – Large-Scale Scrum (LeSS), Scaled Agile Framework (SAFe), and Scrum@Scale.
User-Dependent Neural Sequence Models for Continuous-Time Event Data

Continuous-time event data are common in applications such as individual behavior data, financial transactions, and medical health records. Modeling such data can be very challenging, in particular for applications with many different types of events, since it requires a model to predict the event types as well as the time of occurrence. Recurrent neural networks that parameterize time-varying intensity functions are the current state-of-the-art for predictive modeling with such data. These models typically assume that all event sequences come from the same data distribution. However, in many applications event sequences are generated by different sources, or users, and their characteristics can be very different. In this paper, we extend the broad class of neural marked point process models to mixtures of latent embeddings, where each mixture component models the characteristic traits of a given user. Our approach relies on augmenting these models with a latent variable that encodes user characteristics, represented by a mixture model over user behavior that is trained via amortized variational inference. We evaluate our methods on four large real-world datasets and demonstrate systematic improvements from our approach over existing work for a variety of predictive metrics such as log-likelihood, next event ranking, and source-of-sequence identification.
The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise

We study the computational complexity of adversarially robust proper learning of halfspaces in the distribution-independent agnostic PAC model, with a focus on $L_p$ perturbations. We give a computationally efficient learning algorithm and a nearly matching computational hardness result for this problem. An interesting implication of our findings is that the $L_{\infty}$ perturbations case is provably computationally harder than the case $2 \leq p < \infty$.
Learning in School: Multi-teacher Knowledge Inversion for Data-Free Quantization.

User data confidentiality protection is becoming a rising challenge in the present deep learning research. In that case, data-free quantization has emerged as a promising method to conduct model compression without the need for user data. With no access to data, model quantization naturally becomes less resilient and faces a higher risk of performance degradation. Prior works propose to distill fake images by matching the activation distribution given a specific pre-trained model. However, this fake data cannot be applied to other models easily and is optimized by an invariant objective, resulting in the lack of generalizability and diversity whereas these properties can be found in the natural image dataset. To address these problems, we propose Learning in School~(LIS) algorithm, capable to generate the images suitable for all models by inverting the knowledge in multiple teachers. We further introduce a decentralized training strategy by sampling teachers from hierarchical courses to simultaneously maintain the diversity of generated images. LIS data is highly diverse, not model-specific and only requires one-time synthesis to generalize multiple models and applications. Extensive experiments prove that LIS images resemble natural images with high quality and high fidelity. On data-free quantization, our LIS method significantly surpasses the existing model-specific methods. In particular, LIS data is effective in both post-training quantization and quantization-aware training on the ImageNet dataset and achieves up to 33\% top-1 accuracy uplift compared with existing methods.
Implementation of repeaterless quantum key distribution over 502 km fibers

We implemented the recently proposed phase-matching quantum key distribution scheme using the coherent laser sources, optical fibers, and single-photon detectors. With the laser injection technique and phase-post compensation method, we realize the repeaterless quantum key distribution over 500 km fiber for the first time.
Natural language processing bots

Now that we know all the different aspects of language that we must take into consideration, we can start with building our Teams-integrated bots. Bot features are used very extensively in Teams, and the Bot Framework is heavily used for Teams development, not only to build chatbots but also for other functionalities. This is because the Bot Framework works with authenticated HTTP Post calls between your code, Azure, and Teams. This secure way of communication allowed the Teams team to reuse the technology to get other features working as well. One might say that bots are used even more inside of Teams than Tabs. In this chapter, we will go deeper into building NLP (natural language processing) bots and how they work.
CcGAN: Continuous Conditional Generative Adversarial Networks for Image Generation

This work proposes the continuous conditional generative adversarial network (CcGAN), the first generative model for image generation conditional on continuous, scalar conditions (termed regression labels). Existing conditional GANs (cGANs) are mainly designed for categorical conditions (e.g., class labels); conditioning on regression labels is mathematically distinct and raises two fundamental problems: (P1) Since there may be very few (even zero) real images for some regression labels, minimizing existing empirical versions of cGAN losses (a.k.a. empirical cGAN losses) often fails in practice; (P2) Since regression labels are scalar and infinitely many, conventional label input methods are not applicable. The proposed CcGAN solves the above problems, respectively, by (S1) reformulating existing empirical cGAN losses to be appropriate for the continuous scenario; and (S2) proposing a naive label input (NLI) method and an improved label input (ILI) method to incorporate regression labels into the generator and the discriminator. The reformulation in (S1) leads to two novel empirical discriminator losses, termed the hard vicinal discriminator loss (HVDL) and the soft vicinal discriminator loss (SVDL) respectively, and a novel empirical generator loss. The error bounds of a discriminator trained with HVDL and SVDL are derived under mild assumptions in this work. Two new benchmark datasets (RC-49 and Cell-200) and a novel evaluation metric (Sliding Frechet Inception Distance) are also proposed for this continuous scenario. Our experiments on the Circular 2-D Gaussians, RC-49, UTKFace, Cell-200, and Steering Angle datasets show that CcGAN can generate diverse, high-quality samples from the image distribution conditional on a given regression label. Moreover, in these experiments, CcGAN substantially outperforms cGAN both visually and quantitatively.
Synthetic Data Generators -- Sequential and Private

We study the sample complexity of private synthetic data generation over an unbounded sized class of statistical queries, and show that any class that is privately proper PAC learnable admits a private synthetic data generator (perhaps non-efficient). Previous work on synthetic data generators focused on the case that the query class $\mathcal{D}$ is finite and obtained sample complexity bounds that scale logarithmically with the size $|\mathcal{D}|$. Here we construct a private synthetic data generator whose sample complexity is independent of the domain size, and we replace finiteness with the assumption that $\mathcal{D}$ is privately PAC learnable (a formally weaker task, hence we obtain equivalence between the two tasks).
Reckoning of Emotions Through Recognition of Posture Features

Emotions are noteworthy signs to understand the intentions of others peoples during communication with them. Similarly, in order to identify the different emotional states of individual such as joy...
The Power of Comparisons for Actively Learning Linear Classifiers

In the world of big data, large but costly to label datasets dominate many fields. Active learning, a semi-supervised alternative to the standard PAC-learning model, was introduced to explore whether adaptive labeling could learn concepts with exponentially fewer labeled samples. While previous results show that active learning performs no better than its supervised alternative for important concept classes such as linear separators, we show that by adding weak distributional assumptions and allowing comparison queries, active learning requires exponentially fewer samples. Further, we show that these results hold as well for a stronger model of learning called Reliable and Probably Useful (RPU) learning. In this model, our learner is not allowed to make mistakes, but may instead answer I don't know. While previous negative results showed this model to have intractably large sample complexity for label queries, we show that comparison queries make RPU-learning at worst logarithmically more expensive in both the passive and active regimes.
On Adaptive Distance Estimation

We provide a static data structure for distance estimation which supports {\it adaptive} queries. Concretely, given a dataset $X = \{x_i\}_{i = 1}^n$ of $n$ points in $\mathbb{R}^d$ and $0 < p \leq 2$, we construct a randomized data structure with low memory consumption and query time which, when later given any query point $q \in \mathbb{R}^d$, outputs a $(1+\epsilon)$-approximation of $\lVert q - x_i \rVert_p$ with high probability for all $i\in[n]$. The main novelty is our data structure's correctness guarantee holds even when the sequence of queries can be chosen adaptively: an adversary is allowed to choose the $j$th query point $q_j$ in a way that depends on the answers reported by the data structure for $q_1,\ldots,q_{j-1}$. Previous randomized Monte Carlo methods do not provide error guarantees in the setting of adaptively chosen queries. Our memory consumption is $\tilde O((n+d)d/\epsilon^2)$, slightly more than the $O(nd)$ required to store $X$ in memory explicitly, but with the benefit that our time to answer queries is only $\tilde O(\epsilon^{-2}(n + d))$, much faster than the naive $\Theta(nd)$ time obtained from a linear scan in the case of $n$ and $d$ very large. Here $\tilde O$ hides $\log(nd/\epsilon)$ factors. We discuss applications to nearest neighbor search and nonparametric estimation. Our method is simple and likely to be applicable to other domains: we describe a generic approach for transforming randomized Monte Carlo data structures which do not support adaptive queries to ones that do, and show that for the problem at hand, it can be applied to standard nonadaptive solutions to $\ell_p$ norm estimation with negligible overhead in query time and a factor $d$ overhead in memory.
A Study on the Significance of Liberal Education Based on Classic Reading : A Case Study of Kyunghee University [Classic Reading - Toji (The Land) by Park Kyongri]

This paper examines how ’literary classics’ can be utilized in liberal education, using the case of Kyunghee University [Classic Reading - Toji (The Land) by Park Kyongni], as well as its significance and prospect. In actual classes, a class model was designed in which focus was put on students
An Attentive Recurrent Model for Incremental Prediction of Sentence-final Verbs

Verb prediction is important for understanding human processing of verb-final languages, with practical applications to real-time simultaneous interpretation from verb-final to verb-medial languages. While previous approaches use classical statistical models, we introduce an attention-based neural model to incrementally predict final verbs on incomplete sentences in Japanese and German SOV sentences. To offer flexibility to the model, we further incorporate synonym awareness. Our approach both better predicts the final verbs in Japanese and German and provides more interpretable explanations of why those verbs are selected.
Regularizing Towards Permutation Invariance In Recurrent Models

In many machine learning problems the output should not depend on the order of the input. Such functions have been studied extensively recently. Here we argue that temporal architectures such as RNNs are highly relevant for such problems, despite the inherent dependence of RNNs on order. We show that RNNs can be regularized towards permutation invariance, and that this can result in compact models, as compared to non-recurrent architectures. We implement this idea via a novel form of stochastic regularization. Existing solutions mostly suggest restricting the learning problem to hypothesis classes which are permutation invariant by design. Our approach of enforcing permutation invariance via regularization gives rise to models which are \textit{semi permutation invariant} (e.g. invariant to some permutations and not to others). We show that our method outperforms other permutation invariant approaches on synthetic and real world datasets.
Texture Classification by Local Rajan Transform Based Descriptor

The texture is a significant factor used for the applications of recognizing objects and classifying the surface texture. This work presents a new texture feature descriptor based on Local Forward Rajan Transform (LFRT) for texture classification. The proposed method provides the features from the last stage key matrix of the LFRT. Based on the features the given texture images classified using K Nearest Neighboring (KNN) classifier. Experimental results are presented on a KTH-TIPS texture database, using KNN classifier algorithm. It shows good performance with 95.6% accuracy.
Efficient semidefinite-programming-based inference for binary and multi-class MRFs

Probabilistic inference in pairwise Markov Random Fields (MRFs), i.e. computing the partition function or computing a MAP estimate of the variables, is a foundational problem in probabilistic graphical models. Semidefinite programming relaxations have long been a theoretically powerful tool for analyzing properties of probabilistic inference, but have not been practical owing to the high computational cost of typical solvers for solving the resulting SDPs. In this paper, we propose an efficient method for computing the partition function or MAP estimate in a pairwise MRF by instead exploiting a recently proposed coordinate-descent-based fast semidefinite solver. We also extend semidefinite relaxations from the typical binary MRF to the full multi-class setting, and develop a compact semidefinite relaxation that can again be solved efficiently using the solver. We show that the method substantially outperforms (both in terms of solution quality and speed) the existing state of the art in approximate inference, on benchmark problems drawn from previous work. We also show that our approach can scale to large MRF domains such as fully-connected pairwise CRF models used in computer vision.
Aplikasi Pengenalan Pola Citra Logo Obat Medis Menggunakan K-Means Clustering

Trends in drug abuse have occurred since 2014 such as ATS abuse or chemicals such as over-the-counter PCC. People no longer follow the rules of using drugs according to applicable regulations, but many who buy and consume drugs without a doctor's prescription. These behaviors and actions can endanger health, so people need to know the use of drugs to be more safe for consumption, one of them by paying attention to the type of logo on the drug packaging. To make it easier to find out the name of the logo type on drug packaging, it is necessary to develop an application using digital image processing. The study began with taking a drug pattern image using a smartphone camera and the results were carried out cropping the logo image in detail. Furthermore, cropping (RGB) images are converted into binary images and grayscale images to be performed a feature extraction process that is metric, eccentricity, and GLCM. The results of testing the test image data, a percentage of 91.5% with an accuracy rate of 100% types of over-the-counter drugs, hard drugs, and narcotics, while over-the-counter drug images are limited to 66%.
Low Distortion Block-Resampling with Spatially Stochastic Networks

We formalize and attack the problem of generating new images from old ones that are as diverse as possible, only allowing them to change without restrictions in certain parts of the image while remaining globally consistent. This encompasses the typical situation found in generative modelling, where we are happy with parts of the generated data, but would like to resample others (I like this generated castle overall, but this tower looks unrealistic, I would like a new one). In order to attack this problem we build from the best conditional and unconditional generative models to introduce a new network architecture, training procedure, and algorithm for resampling parts of the image as desired.