Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Captioning with Attention for Smart Local Tourism using EfficientNet

Smart systems have been massively developed to help humans in various tasks. Deep Learning technologies push even further in creating accurate assistant systems due to the explosion of data lakes. One of the smart system tasks is disseminating ‘users needed information’, which is crucial in the tourism sector to promote local tourism destinations. In this research, we design a local tourism specific image captioning model, which will later support the development of AI-powered systems that assist various users. The model is developed using a visual Attention mechanism and uses the state-of-the-art feature extractor architecture EfficientNet. A local tourism dataset is collected and used in the research and two different captions: captions that describe the image literally and captions that represent human logical responses when seeing the image. The two kinds of captions make the captioning model more humane when implemented in the assistance system. We compared two different models using EfficientNet architectures (BO and B4) with other well-known VGG16 and InceptionV3. The best BLEU scores we get are 73.39 and 24.51 for the training set and the validation set, respectively, using EfficientNetB0. The captioning result using the developed model shows that the model can produce logical caption for local tourism-related images.
SISTA: Learning Optimal Transport Costs under Sparsity Constraints

In this paper, we describe a novel iterative procedure called SISTA to learn the underlying cost in optimal transport problems. SISTA is a hybrid between two classical methods, coordinate descent (“S”‐inkhorn) and proximal gradient descent (“ISTA”). It alternates between a phase of exact minimization over the transport potentials and a phase of proximal gradient descent over the parameters of the transport cost. We prove that this method converges linearly, and we illustrate on simulated examples that it is significantly faster than both coordinate descent and ISTA. We apply it to estimating a model of migration, which predicts the flow of migrants using country‐specific characteristics and pairwise measures of dissimilarity between countries. This application demonstrates the effectiveness of machine learning in quantitative social sciences. © 2022 Wiley Periodicals LLC.
The Hidden Vulnerability of Watermarking for Deep Neural Networks

Watermarking has shown its effectiveness in protecting the intellectual property of Deep Neural Networks (DNNs). Existing techniques usually embed a set of carefully-crafted sample-label pairs into the target model during the training process. Then ownership verification is performed by querying a suspicious model with those watermark samples and checking the prediction results. These watermarking solutions claim to be robustness against model transformations, which is challenged by this paper. We design a novel watermark removal attack, which can defeat state-of-the-art solutions without any prior knowledge of the adopted watermarking technique and training samples. We make two contributions in the design of this attack. First, we propose a novel preprocessing function, which embeds imperceptible patterns and performs spatial-level transformations over the input. This function can make the watermark sample unrecognizable by the watermarked model, while still maintaining the correct prediction results of normal samples. Second, we introduce a fine-tuning strategy using unlabelled and out-of-distribution samples, which can improve the model usability in an efficient manner. Extensive experimental results indicate that our proposed attack can effectively bypass existing watermarking solutions with very high success rates.
Multichannel superconductivity of monolayer FeSe on 
SrTiO3
: Interplay of spin fluctuations and electron-phonon interaction

We investigate the effects of electron-phonon coupling, as well as of spin and charge fluctuations on the superconducting state in a single layer of FeSe on SrTiO$_3$ substrate. These three bosonic mediators of Cooper pairing are treated on equal footing in a multichannel, full-bandwidth, multiband, and anisotropic Eliashberg theory of the interacting state. Our self-consistent calculations show that an s-wave symmetry of the superconducting gap is compatible only with a complete absence of spin fluctuations. When spin fluctuations are present, the sign-changing nodeless d-wave pairing symmetry is always obtained, yet the essential ingredient for explaining the gap magnitude and critical temperature is still the interfacial electron-phonon interaction.
Boosting Retailer Revenue by Generated Optimized Combined Multiple Digital Marketing Campaigns

Campaign is a frequently employed instrument in lifting up the GMV (Gross Merchandise Volume) of retailer in traditional marketing. As its counterpart in online context, digital-marketing-campaign (DMC) has being trending in recent years with the rapid development of the e-commerce. However, how to empower massive sellers on the online retailing platform the capacity of applying combined multiple digital marketing campaigns to boost their shops' revenue, is still a novel topic. In this work, a comprehensive solution of generating optimized combined multiple DMCs is presented. Firstly, a potential personalized DMC pool is generated for every retailer by a newly proposed neural network model, i.e. the DMCNet (Digital-Marketing-Campaign Net). Secondly, based on the sub-modular optimization theory and the DMC pool by DMCNet, the generated combined multiple DMCs are ranked with respect to their revenue generation strength then the top three ranked campaigns are returned to the sellers' back-end management system, so that retailers can set combined multiple DMCs for their online shops just in one-shot. Real online A/B-test shows that with the integrated solution, sellers of the online retailing platform increase their shops' GMVs with approximately 6$\%$.
Skein lasagna modules for 2-handlebodies

Abstract Morrison, Walker, and Wedrich used the blob complex to construct a generalization of Khovanov–Rozansky homology to links in the boundary of a 4-manifold. The degree zero part of their theory, called the skein lasagna module, admits an elementary definition in terms of certain diagrams in the 4-manifold. We give a description of the skein lasagna module for 4-manifolds without 1- and 3-handles, and present some explicit calculations for disk bundles over S2{S^{2}}.
Densely Guided Knowledge Distillation using Multiple Teacher Assistants

With the success of deep neural networks, knowledge distillation which guides the learning of a small student network from a large teacher network is being actively studied for model compression and transfer learning. However, few studies have been performed to resolve the poor learning issue of the student network when the student and teacher model sizes significantly differ. In this paper, we propose a densely guided knowledge distillation using multiple teacher assistants that gradually decreases the model size to efficiently bridge the large gap between the teacher and student networks. To stimulate more efficient learning of the student network, we guide each teacher assistant to every other smaller teacher assistants iteratively. Specifically, when teaching a smaller teacher assistant at the next step, the existing larger teacher assistants from the previous step are used as well as the teacher network. Moreover, we design stochastic teaching where, for each mini-batch, a teacher or teacher assistants are randomly dropped. This acts as a regularizer to improve the efficiency of teaching of the student network. Thus, the student can always learn salient distilled knowledge from the multiple sources. We verified the effectiveness of the proposed method for a classification task using CIFAR-10, CIFAR-100, and ImageNet. We also achieved significant performance improvements with various backbone architectures such as ResNet, WideResNet, and VGG.1
Nanoscale structural and electrical properties of graphene grown on AlGaN by catalyst-free chemical vapor deposition

The integration of graphene (Gr) with nitride semiconductors is highly interesting for applications in high-power/high-frequency electronics and optoelectronics. In this work, we demonstrated the direct growth of Gr on Al0.5Ga0. 5N/sapphire templates by propane (C3H8) chemical vapor deposition at a temperature of 1350 °C. After optimization of the C3H8 flow rate, a uniform and conformal Gr coverage was achieved, which proved beneficial to prevent degradation of AlGaN morphology. X-ray photoemission spectroscopy revealed Ga loss and partial oxidation of Al in the near-surface AlGaN region. Such chemical modification of a ∼2 nm thick AlGaN surface region was confirmed by cross-sectional scanning transmission electron microscopy combined with electron energy loss spectroscopy, which also showed the presence of a bilayer of Gr with partial sp2/sp3 hybridization. Raman spectra indicated that the deposited Gr is nanocrystalline (with domain size ∼7 nm) and compressively strained. A Gr sheet resistance of ∼15.8 kΩ sq−1 was evaluated by four-point-probe measurements, consistently with the nanocrystalline nature of these films. Furthermore, nanoscale resolution current mapping by conductive atomic force microscopy indicated local variations of the Gr carrier density at a mesoscopic scale, which can be ascribed to changes in the charge transfer from the substrate due to local oxidation of AlGaN or to the presence of Gr wrinkles.
Low-rank MDP Approximation via Moment Coupling

We propose a novel method---based on local moment matching---to approximate the value function of a Markov Decision Process. The method is grounded in recent work by Braverman et al (2020) that relates the solution of the Bellman equation to that of a PDE where, in the spirit of the central limit theorem, the transition matrix is reduced to its local first and second moments. Solving the PDE is not required by our method. Instead we construct a "sister" Markov chain whose two local transition moments are (approximately) identical with those of the focal chain. Because they share these moments, the original chain and its "sister" are coupled through the PDE, a coupling that facilitates optimality guarantees. We show how this view can be embedded into the existing aggregation framework of ADP, providing a disciplined mechanism to tune the aggregation and disaggregation probabilities. This embedding into aggregation also reveals how the approximation's accuracy depends on a certain local linearity of the value function. The computational gains arise from the reduction of the effective state space from $N$ to $N^{\frac{1}{2}+\epsilon}$ is as one might intuitively expect from approximations grounded in the central limit theorem.
On More General Distributions of Random Binning for Slepian-Wolf Encoding

Traditionally, ensembles of Slepian-Wolf (S-W) codes are defined such that every bin of each $n$-vector of each source is randomly drawn under the uniform distribution across the sets $\{0,1,\ldots, 2^{nR_{X}}-1\}$ and $\{0,1,\ldots, 2^{nR_{Y}}-1\}$, where $R_{X}$ and $R_{Y}$ are the coding rates of the two sources, $X$ and $Y$, respectively. In a few recent works, where only one source is compressed and the other one serves as side information at the decoder, the scope is extended to variable–rate S-W (VRSW) codes, where the rate may depend on the type class of the source string, but still, the random–binning distribution is assumed uniform within the type–dependent, bin index set. In this expository work, we investigate the role of the uniformity of the random binning distribution from the perspective of the trade-off between the error exponent and the source coding exponent. To this end, we study a much wider class of random–binning distributions, which includes VRSW codes as a special case, but goes considerably beyond. We first show that, except for some pathological cases, the sub-ensemble of VRSW codes is as good as the large ensemble in terms the trade–off between the error exponent and the source coding exponent. Nonetheless, the wider class of ensembles is motivated in two ways. The first is that it outperforms VRSW codes in the above–mentioned pathological cases, and the second is that it allows robustness: in the event of unavailability of the compressed bit–stream from one of the sources, it still allows reconstruction of the other source within some controllable distortion.
Protecting quantum information in quantum dot spin chains by driving exchange interactions periodically

Recent work has demonstrated a new route to discrete time crystal physics in quantum spin chains by periodically driving nearest-neighbor exchange interactions in gate-defined quantum dot arrays [arXiv:2006.10913]. Here, we present a detailed analysis of exchange-driven Floquet physics in small arrays of GaAs quantum dots, including phase diagrams and additional diagnostics. We also show that emergent time-crystalline behavior can benefit the protection and manipulation of multi-spin states. For typical levels of nuclear spin noise in GaAs, the combination of driving and interactions protects spin-singlet states beyond what is possible in the absence of exchange interactions. We further show how to construct a time-crystal-inspired CZ gate between singlet-triplet qubits with high fidelity. These results show that periodically driving exchange couplings can enhance the performance of quantum dot spin systems for quantum information applications.
The Stability of Low-Density Parity-Check Codes and Some of its Consequences

We study the stability of low-density parity-check codes under blockwise or bitwise maximum a posteriori decoding, where transmission takes place over a binary-input memoryless output-symmetric channel. Our study stems from the consideration of constructing universal capacity-achieving codes under low-complexity decoding algorithms, where universality refers to the fact that we are considering a family of channels with equal capacity. Consider, e.g., the right-regular sequence by Shokrollahi and the heavy-tail Poisson sequence by Luby et al. Both sequences are provably capacity-achieving under belief propagation decoding when transmission takes place over the binary erasure channel. In this paper we show that many existing capacity-achieving sequences of low-density parity-check codes are not universal under belief propagation decoding. We reveal that the key to showing this non-universality result is determined by the stability of the underlying codes. More concretely, for an ordered and complete channel family and a sequence of low-density parity-check code ensembles, we determine a stability threshold associated with them, which gives rise to a sufficient condition under which the sequence is not universal under belief propagation decoding. Moreover, we show that the same stability threshold applies to blockwise or bitwise maximum a posteriori decoding as well. We demonstrate how the stability threshold can determine an upper bound on the corresponding blockwise or bitwise maximum a posteriori threshold, revealing the operational significance of the stability threshold.
Conditional Hybrid GAN for Sequence Generation

Conditional sequence generation aims to instruct the generation procedure by conditioning the model with additional context information, which is a self-supervised learning issue (a form of unsupervised learning with supervision information from data itself). Unfortunately, the current state-of-the-art generative models have limitations in sequence generation with multiple attributes. In this paper, we propose a novel conditional hybrid GAN (C-Hybrid-GAN) to solve this issue. Discrete sequence with triplet attributes are separately generated when conditioned on the same context. Most importantly, relational reasoning technique is exploited to model not only the dependency inside each sequence of the attribute during the training of the generator but also the consistency among the sequences of attributes during the training of the discriminator. To avoid the non-differentiability problem in GANs encountered during discrete data generation, we exploit the Gumbel-Softmax technique to approximate the distribution of discrete-valued sequences.Through evaluating the task of generating melody (associated with note, duration, and rest) from lyrics, we demonstrate that the proposed C-Hybrid-GAN outperforms the existing methods in context-conditioned discrete-valued sequence generation.
Rainbow spacetime from a nonlocal gravitational uncertainty principle

The occurrence of spacetime singularities is one of the peculiar features of Einstein gravity, signalling limitation on probing short distances in spacetime. This alludes to the existence of a fundamental length scale in nature. On the contrary, the Heisenberg quantum uncertainty relation seems to allow for probing arbitrarily small length scales. To reconcile these two conflicting ideas in line with a well-known framework of quantum gravity, several modifications of Heisenberg algebra have been proposed. However, it has been extensively argued that such a minimum length would introduce nonlocality in theories of quantum gravity. In this letter, we analyze a previously proposed deformation of the Heisenberg algebra (i.e., ) for a particle confined in a box subjected to a gravitational field. For the problem in hand, such deformation seems to yield an energy-dependent behavior of spacetime in a way consistent with gravity's rainbow, hence demonstrating a connection between non-locality and gravity's rainbow.
Single Image Super-Resolution Of Noisy 3d Dental Ct Images Using Tucker Decomposition

Tensor decomposition has proven to be a strong tool in various 3D image processing tasks such as denoising and superresolution. In this context, we recently proposed a computationally effective, canonical polyadic decomposition (CPD) based algorithm for single image super-resolution. In this work, we investigated the added value brought by Tucker decomposition. While CPD allows a joint implementation of the denoising and deconvolution steps, with Tucker decomposition the denoising is followed by the deconvolution. This way the ill-posedness of the deconvolution caused by noise is partially mitigated. The results achieved using the two different tensor decomposition techniques were compared, and the robustness against noise was investigated. For validation, dental images were used. The superiority of the proposed method is shown in terms of peak signal-to-ratio, structural similarity index, the root canal segmentation accuracy, and runtime.
Delay Optimization of Combinational Logic by AND-OR Path Restructuring

We propose a timing optimization framework that replaces critical paths by logically equivalent realizations with less delay. Our tool allows to revise early decisions on the logical structure of the netlist in late physical design. The core routine of our framework is a new algorithm that constructs delay-optimized circuits for alternating AND-OR paths with prescribed input arrival times. It is a sophisticated dynamic programming algorithm which is a common generalization of the previously best approaches. In contrast to all earlier methods, we avoid fixing the structure of sub-solutions before deciding on how to combine them, significantly expanding the search space of the algorithm. Our algorithm provably fulfills the best known approximation guarantees, almost always computes delay-optimum solutions, and empirically outperforms all previous approaches. The reduction to AND-OR path optimization allows us to optimize general combinatorial paths of arbitrary length in our logic restructuring framework. The framework is applied successfully as a late step in an industrial physical design flow. Experiments demonstrate the effectiveness of our tool on industrial 7nm instances.
C-Balancer: A System for Container Profiling and Scheduling

Linux containers have gained high popularity in recent times. This popularity is significantly due to various advantages of containers over Virtual Machines (VM). The containers are lightweight, occupy lesser storage, have fast boot-up time, easy to deploy and have faster auto-scaling. The key reason behind the popularity of containers is that they leverage the mechanism of micro-service style software development, where applications are designed as independently deployable services. There are various container orchestration tools for deploying and managing the containers in the cluster. The prominent among them are Docker Swarm and Kubernetes. However, they do not address the effects of resource contention when multiple containers are deployed on a node. Moreover, they do not provide support for container migration in the event of an attack or increased resource contention. To address such issues, we propose C-Balancer, a scheduling framework for efficient placement of containers in the cluster environment. C-Balancer works by periodically profiling the containers and deciding the optimal container to node placement. Our proposed approach improves the performance of containers in terms of resource utilization and throughput. Experiments using a workload mix of Stress-NG and iPerf benchmark shows that our proposed approach achieves a maximum performance improvement of 58% for the workload mix. Our approach also reduces the variance in resource utilization across the cluster by 60% on average.
Improved Coding Over Sets for DNA-Based Data Storage

Error-correcting codes over sets, with applications to DNA storage, are studied. The DNA-storage channel receives a set of sequences, and produces a corrupted version of the set, including sequence loss, symbol substitution, symbol insertion/deletion, and limited-magnitude errors in symbols. Various parameter regimes are studied. New bounds on code parameters are provided, which improve upon known bounds. New codes are constructed, at times matching the bounds up to lower-order terms or small constant factors.
Gateway Controller with Deep Sensing: Learning to be Autonomic in Intelligent Internet of Things

The Internet of Things(IoT) will revolutionize the Future Internet through ubiquitous sensing. One of the challenges of having the hundreds of billions of devices that are estimated to be deployed would be rise of an enormous amount of data, along with the devices ability to manage. This paper presents an approach as a controller solution and designed specifically for autonomous management, connectivity and data interoperability in an IoT gateway. The approach supports distributed IoT nodes with both management and data interoperability with other cloud-based solutions. The concept further allows gateways to easily collect and process interoperability of data from IoT devices. We demonstrated the feasibility of the approach and evaluate its advantages regarding deep sensing and autonomous enabled gateway as an edge computational intelligence.
DFedForest: Decentralized Federated Forest

The effectiveness of machine learning systems depends heavily on the relevance of the training data. Usually, the collected data is sensitive and private because it comes from devices and sensors used in people’s daily lives. The General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in California, and China’s Cybersecurity Law put the current approach at risk, as it prohibits centralized remote processing of sensitive data collected in a distributed manner. This paper proposes a distributed machine learning system based on local random forest algorithms created with shared decision trees through the blockchain. The results show that the proposed approach equals or exceeds the results obtained with the use of random forests with only local data. Furthermore, the proposal increases the detection of new attacks when the domains have different threat distributions.