Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications

This work takes a critical look at the application of conventional machine learning methods to wireless communication problems through the lens of reliability and robustness. Deep learning techniques adopt a frequentist framework, and are known to provide poorly calibrated decisions that do not reproduce the true uncertainty caused by limitations in the size of the training data. Bayesian learning, while in principle capable of addressing this shortcoming, is in practice impaired by model misspecification and by the presence of outliers. Both problems are pervasive in wireless communication settings, in which the capacity of machine learning models is subject to resource constraints and training data is affected by noise and interference. In this context, we explore the application of the framework of robust Bayesian learning. After a tutorial-style introduction to robust Bayesian learning, we showcase the merits of robust Bayesian learning on several important wireless communication problems in terms of accuracy, calibration, and robustness to outliers and misspecification.
Phonon-limited transport of two-dimensional semiconductors: Quadrupole scattering and free carrier screening

: Two-dimensional (2D) semiconductors have demonstrated great potential for next-generation electronics and optoelectronics. An important property for these applications is the phonon-limited charge carrier mobility. The common approach to calculate the mobility from first principles relies on the interpolation of the electron-phonon coupling (EPC) matrix. However, it neglects the scattering by the dynamical quadrupoles generated by phonons, limiting its accuracy. Here we present a first-principles method to incorporate the quadrupole scattering, which results in a much better interpolation quality and thus a more accurate mobility as exemplified by monolayer MoS 2 and InSe. This method also allows for a natural incorporation of the effects of the free carriers, enabling us to efficiently compute the screened EPC and thus the mobility for doped semiconductors. Particularly, we find that the electron mobility of InSe is more sensitive to the carrier concentration than that of MoS 2 due to the stronger long-range scattering in intrinsic InSe. With increasing electron concentration, the InSe mobility can reach ~4 times of the intrinsic value, then decrease owing to the involvement of heavier electronic states. Our work provides accurate and efficient methods to calculate the phonon-limited mobility in the intrinsic and doped 2D materials, and improves the fundamental understanding of their transport mechanism.
Variational Inference for Additive Main and Multiplicative Interaction Effects Models

In plant breeding the presence of a genotype by environment (GxE) interaction has a strong impact on cultivation decision making and the introduction of new crop cultivars. The combination of linear and bilinear terms has been shown to be very useful in modelling this type of data. A widely-used approach to identify GxE is the Additive Main Effects and Multiplicative Interaction Effects (AMMI) model. However, as data frequently can be high-dimensional, Markov chain Monte Carlo (MCMC) approaches can be computationally infeasible. In this article, we consider a variational inference approach for such a model. We derive variational approximations for estimating the parameters and we compare the approximations to MCMC using both simulated and real data. The new inferential framework we propose is on average two times faster whilst maintaining the same predictive performance as MCMC.
TopicFM: Robust and Interpretable Feature Matching with Topic-assisted

Finding correspondences across images is an important task in many visual applications. Recent state-of-the-art methods focus on end-to-end learning-based architectures designed in a coarse-to-ﬁne manner. They use a very deep CNN or multi-block Transformer to learn robust representation, which requires high computation power. Moreover, these methods learn features without reasoning about objects, shapes inside images, thus lacks of interpretability. In this paper, we propose an architecture for image matching which is efﬁcient, robust, and interpretable. More speciﬁ-cally, we introduce a novel feature matching module called TopicFM which can roughly organize same spatial structure across images into a topic and then augment the features inside each topic for accurate matching. To infer topics, we ﬁrst learn global embedding of topics and then use a latent-variable model to detect-then-assign the image structures into topics. Our method can only perform matching in co-visibility regions to reduce computations. Extensive experiments in both outdoor and indoor datasets show that our method outperforms the recent methods in terms of matching performance and computational efﬁciency. The code is available at https://github.com/TruongKhang/ TopicFM .
FLVoogd: Robust And Privacy Preserving Federated Learning

In this work, we propose FLVoogd, an updated federated learning method in which servers and clients collaboratively eliminate Byzantine attacks while preserving privacy. In particular, servers use automatic Density-based Spatial Clustering of Applications with Noise (DBSCAN) combined with S2PC to cluster the benign majority without acquiring sensitive personal information. Meanwhile, clients build dual models and perform test-based distance controlling to adjust their local models toward the global one to achieve personalizing. Our framework is automatic and adaptive that servers/clients don't need to tune the parameters during the training. In addition, our framework leverages Secure Multi-party Computation (SMPC) operations, including multiplications, additions, and comparison, where costly operations, like division and square root, are not required. Evaluations are carried out on some conventional datasets from the image classification field. The result shows that FLVoogd can effectively reject malicious uploads in most scenarios; meanwhile, it avoids data leakage from the server-side.
MultiViz: An Analysis Benchmark for Visualizing and Understanding Multimodal Models

The promise of multimodal models for real-world applications has inspired research in visualizing and understanding their internal mechanics with the end goal of empowering stakeholders to visualize model behavior, perform model debugging, and promote trust in machine learning models. However, modern multimodal models are typically black-box neural networks, which makes it challenging to understand their internal mechanics. How can we visualize the internal modeling of multimodal interactions in these models? Our paper aims to fill this gap by proposing MULTIVIZ, a method for analyzing the behavior of multimodal models by scaffolding the problem of interpretability into 4 stages: (1) unimodal importance: how each modality contributes towards downstream modeling and prediction, (2) cross-modal interactions: how different modalities relate with each other, (3) multimodal representations: how unimodal and cross-modal interactions are represented in decision-level features, and (4) multimodal prediction: how decision-level features are composed to make a prediction. MULTIVIZ is designed to operate on diverse modalities, models, tasks, and research areas. Through experiments on 8 trained models across 6 real-world tasks, we show that the complementary stages in MULTIVIZ together enable users to (1) simulate model predictions, (2) assign interpretable concepts to features, (3) perform error analysis on model misclassifications, and (4) use insights from error analysis to debug models. MULTIVIZ is publicly available, will be regularly updated with new interpretation tools and metrics, and welcomes inputs from the community.
Modelling Users with Item Metadata for Explainable and Interactive Recommendation

Recommender systems are used in many different applications and contexts, however their main goal can always be summarised as “connecting relevant content to interested users”. Personalized recommendation algorithms achieve this goal by first building a profile of the user, either implicitly or explicitly, and then matching items with this profile to find relevant content. The more interpretable the profile and this “matching function” are, the easier it is to provide users with accurate and intuitive explanations, and also to let them interact with the system. Indeed, for a user to see what the system has already learned about her interests is of key importance for her to provide feedback to the system and to guide it towards better understanding her preferences. To this end, we propose a linear collaborative filtering recommendation model that builds user profiles within the domain of item metadata, which is arguably the most interpretable domain for end users. Our method is hence inherently transparent and explainable. Moreover, since recommendations are computed as a linear function of item metadata and the interpretable user profile, our method seamlessly supports interactive recommendation. In other words, users can directly tweak the weights of the learned profile for more fine-grained browsing and discovery of content based on their current interests. We demonstrate the interactive aspect of this model in an online application for discovering cultural events in Belgium. Additionally, the performance of the model is evaluated with offline experiments, both static and with simulated feedback, and compared to several state-of-the-art and state-of-practice baselines. to rank
High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs

While parallelism remains the main source of performance, architectural implementations and programming models change with each new hardware generation, often leading to costly application re-engineering. Most tools for performance portability require manual and costly application porting to yet another programming model. We propose an alternative approach that automatically translates programs written in one programming model (CUDA), into another (CPU threads) based on Polygeist/MLIR. Our approach includes a representation of parallel constructs that allows conventional compiler transformations to apply transparently and without modification and enables parallelism-specific optimizations. We evaluate our framework by transpiling and optimizing the CUDA Rodinia benchmark suite for a multi-core CPU and achieve a 58% geomean speedup over handwritten OpenMP code. Further, we show how CUDA kernels from PyTorch can efficiently run and scale on the CPU-only Supercomputer Fugaku without user intervention. Our PyTorch compatibility layer making use of transpiled CUDA PyTorch kernels outperforms the PyTorch CPU native backend by 2.7×.
Stain Isolation-based Guidance for Improved Stain Translation

Unsupervised and unpaired domain translation using generative adversarial neural networks, and more precisely CycleGAN, is state of the art for the stain translation of histopathology images. It often, however, suffers from the presence of cycle-consistent but non structure-preserving errors. We propose an alternative approach to the set of methods which, relying on segmentation consistency, enable the preservation of pathology structures. Focusing on immunohistochemistry (IHC) and multiplexed immunofluorescence (mIF), we introduce a simple yet effective guidance scheme as a loss function that leverages the consistency of stain translation with stain isolation. Qualitative and quantitative experiments show the ability of the proposed approach to improve translation between the two domains.
Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts

Recent deep learning-based methods for medical image registration achieve results that are competitive with conventional optimization algorithms at reduced run times. However, deep neural networks generally require plenty of labeled training data and are vulnerable to domain shifts between training and test data. While typical intensity shifts can be mitigated by keypoint-based registration, these methods still suffer from geometric domain shifts, for instance, due to different fields of view. As a remedy, in this work, we present a novel approach to geometric domain adaptation for image registration, adapting a model from a labeled source to an unlabeled target domain. We build on a keypoint-based registration model, combining graph convolutions for geometric feature learning with loopy belief optimization, and propose to reduce the domain shift through self-ensembling. To this end, we embed the model into the Mean Teacher paradigm. We extend the Mean Teacher to this context by 1) adapting the stochastic augmentation scheme and 2) combining learned feature extraction with differentiable optimization. This enables us to guide the learning process in the unlabeled target domain by enforcing consistent predictions of the learning student and the temporally averaged teacher model. We evaluate the method for exhale-to-inhale lung CT registration under two challenging adaptation scenarios (DIR-Lab 4D CT to COPD, COPD to Learn2Reg). Our method consistently improves on the baseline model by 50%/47% while even matching the accuracy of models trained on target data. Source code is available at https://github.com/multimodallearning/registration-da-mean-teacher.
Control of dual-wavelength laser emission viaoptical feedback phase tuning

We propose and demonstrate a technique to control the balance between the two amplitudes of a dual-wavelength laser based on a phase-controlled optical feedback. The feedback cavity length is adjusted to achieve a relative phase shift between the desired emission wavelengths, introducing a boost in gain for one wavelength while the other wavelength experiences additional losses. Tuning the optical feedback phase proves to be an effective way to control the gain and losses, and, thus, to select one or balance the amplitude of the two emission wavelengths. This concept can be easily adapted to any platform, wavelength range and wavelength separations providing that a sufficient carrier coupling and gain can be obtained for each mode. To demonstrate the feasibility and to evaluate the performance of this approach, we have implemented two dual-wavelength lasers with different spectral separations together with individual optical feedback loops onto a InP generic foundry platform emitting around 1550 nm. An electro-optical-phase-modulator is used to tune the feedback phase. With this single control parameter, we successfully achieved extinction ratios of up to 38.6 dB for a 10 nm wavelength separation and up to 49 dB for a 1 nm wavelength separation.
Cosmic time evolution and propagator from a Yang–Mills matrix model

We consider a solution of a IKKT-type matrix model which can be considered as a 1+1-dimensional space-time with Minkowski signature and a Big Bounce (BB)-like singularity. A suitable iε regularization of the Lorentzian matrix integral is proposed, which leads to the standard iε -prescription for the effective field theory. In particular, the Feynman propagator is recovered locally for late times. This demonstrates that a causal structure and time evolution can emerge in the matrix model, even on non-trivial geometries. We also consider the propagation of modes through the BB, and observe an interesting correlation between the post-BB and pre-BB sheets, which reflects the structure of the brane in target space.
A complete POVM description of multi-channel quantum electro-optic sampling with monochromatic field modes

We propose a multi-channel version of quantum electro-optic sampling involving monochromatic field modes. It allows for multiple simultaneous measurements of arbitrarily many $\hat{X}$ and $\hat{Y}$ field-quadrature for a single quantum-state copy, while independently tuning the interaction strengths at each channel. In contrast to standard electro-optic sampling, the sampled mid-infrared (MIR) mode undergoes a nonlinear interaction with multiple near-infrared (NIR) pump beams. We present a complete positive operator-valued measure (POVM) description for quantum states in the MIR mode. The probability distribution of the electro-optic signal outcomes is shown to be related to an $s$-parametrized phase-space quasiprobability distribution of the indirectly measured MIR state, with the parameter $s$ depending solely on the quantities characterizing the nonlinear interaction. Furthermore, we show that the quasiprobability distributions for the sampled and post-measurement states are related to each other through a renormalization and a change in the parametrization. This result is then used to demonstrate that two consecutive measurements of both $\hat{X}$ and $\hat{Y}$ quadratures can outperform eight-port homodyne detection.
A Comparative Study of Graph Matching Algorithms in Computer Vision

The graph matching optimization problem is an essential component for many tasks in computer vision, such as bringing two deformable objects in correspondence. Naturally, a wide range of applicable algorithms have been proposed in the last decades. Since a common standard benchmark has not been developed, their performance claims are often hard to verify as evaluation on differing problem instances and criteria make the results incomparable. To address these shortcomings, we present a comparative study of graph matching algorithms. We create a uniform benchmark where we collect and categorize a large set of existing and publicly available computer vision graph matching problems in a common format. At the same time we collect and categorize the most popular open-source implementations of graph matching algorithms. Their performance is evaluated in a way that is in line with the best practices for comparing optimization algorithms. The study is designed to be reproducible and extensible to serve as a valuable resource in the future. Our study provides three notable insights: 1. ) popular problem instances are exactly solvable in substantially less than 1 second and, therefore, are insufficient for future empirical evaluations; 2. ) the most popular baseline methods are highly inferior to the best available methods; 3. ) despite the NP-hardness of the problem, instances coming from vision applications are often solvable in a few seconds even for graphs with more than 500 vertices.
Reduce Indonesian Vocabularies with an Indonesian Sub-word Separator

Indonesian is an agglutinative language since it has a compounding process of word-formation. Therefore, the translation model of this language requires a mechanism that is even lower than the word level, referred to as the sub-word level. This compounding process leads to a rare word problem since the number of vocabulary explodes. We propose a strategy to address the unique word problem of the neural machine translation (NMT) system, which uses Indonesian as a pair language. Our approach uses a rule-based method to transform a word into its roots and accompanied affixes to retain its meaning and context. Using a rule-based algorithm has more advantages: it does not require corpus data but only applies the standard Indonesian rules. Our experiments confirm that this method is practical. It reduces the number of vocabulary significantly up to 57\%, and on the English to Indonesian translation, this strategy provides an improvement of up to 5 BLEU points over a similar NMT system that does not use this technique.
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning

Graph contrastive learning has emerged as a powerful unsupervised graph representation learning tool. The key to the success of graph contrastive learning is to acquire high-quality positive and negative samples as contrasting pairs to learn the underlying structural semantics of the input graph. Recent works usually sample negative samples from the same training batch with the positive samples or from an external irrelevant graph. However, a significant limitation lies in such strategies: the unavoidable problem of sampling false negative samples. In this paper, we propose a novel method to utilize Counterfactual mechanism to generate artificial hard negative samples for Graph Contrastive learning, namely CGC. We utilize a counterfactual mechanism to produce hard negative samples, ensuring that the generated samples are similar but have labels that differ from the positive sample. The proposed method achieves satisfying results on several datasets. It outperforms some traditional unsupervised graph learning methods and some SOTA graph contrastive learning methods. We also conducted some supplementary experiments to illustrate the proposed method, including the performances of CGC with different hard negative samples and evaluations for hard negative samples generated with different similarity measurements. The implementation code is available online to ease reproducibility1.
Single-Crystal Silicon Thermoelectrics by Phonon Engineering

Herein, we report the use of nanostructured crystalline Si as a thermoelectric material and its integration into thermoelectric harvesters. The proof-of-concept relies on the partial suppression of lattice thermal transport by introducing pores with dimensions scaling between the electron mean free path and the phonon mean free path. In other words, we artificially aimed at the electron crystal–phonon glass trade-off targeted for thermoelectric efficiency. The devices were fabricated using CMOS-compatible processes and exhibited power generation from a few µW/cm 2 to a few mW/cm 2 under temperature differences from a few K to 200 K across the thermopiles. These numbers demonstrate the capability to power autonomous devices with environmental or body heat using silicon chips with areas below cm². This paper also reports the possibility of using the developed demonstrators for integrated thermoelectric cooling.
AUDITEM: Toward an Automated and Efficient Data Integrity Verification Model Using Blockchain

—Data tampering is often considered a severe problem in industrial applications as it can lead to inaccurate ﬁnancial reports or even a corporate security crisis. A correct representation of data is essential for companies’ core business processes and is demanded by investors and customers. Traditional data audits are performed through third-party auditing services; however, these services are expensive and can be untrustworthy in some cases. Blockchain and smart contracts provide a decentralized mechanism to achieve secure and trustworthy data integrity veriﬁcation; however, existing solutions present challenges in terms of scalability, privacy protection, and compliance with data regulations. In this paper, we propose the AUtomated and Decentralized InTegrity vEriﬁcation Model (AUDITEM) to assist business stakeholders in verifying data integrity in a trustworthy and automated manner. To address the challenges in existing integrity veriﬁcation processes, our model uses carefully designed smart contracts and a distributed ﬁle system to store integrity veriﬁcation attributes and uses blockchain to enhance the authenticity of data certiﬁcates. A sub-module called Data Integrity Veriﬁcation Tool (DIVT) is also developed to support easy-to-use interfaces and customizable veriﬁcation operations. This paper presents a detailed implementation and designs experiments to verify the proposed model. The experimental and analytical results demonstrate that our model is feasible and efﬁcient to meet various business requirements for data integrity veriﬁcation.