Miracle-dz/UniFAR-FTUs huggingface dataset:
Observability Blocking for Functional Privacy of Linear Dynamic Networks

This paper addresses the problem of determining the minimum set of state variables in a network that need to be blocked from direct measurements in order to protect functional privacy with respect to any output matrices. More precisely, the goal is to prevent adversarial observers or eavesdroppers from inferring a linear functional of states, either vector-wise or entry-wise. We relate the considered functional privacy to the concept of functional observability. Building on a PBH-like criterion for functional observability, we prove that both problems are NP-hard. However, by assuming a reasonable constant bound on the geometric multiplicities of the system's eigenvalues, we present an exact algorithm with polynomial time complexity for the vector-wise functional privacy protection problem. Based on this algorithm, we then provide a greedy algorithm for the entry-wise privacy protection problem. Finally, we provide an example to demonstrate the effectiveness of our proposed approach.
Instance-Level Few-Shot Learning With Class Hierarchy Mining

Few-shot learning is proposed to tackle the problem of scarce training data in novel classes. However, prior works in instance-level few-shot learning have paid less attention to effectively utilizing the relationship between categories. In this paper, we exploit the hierarchical information to leverage discriminative and relevant features of base classes to effectively classify novel objects. These features are extracted from abundant data of base classes, which could be utilized to reasonably describe classes with scarce data. Specifically, we propose a novel superclass approach that automatically creates a hierarchy considering base and novel classes as fine-grained classes for few-shot instance segmentation (FSIS). Based on the hierarchical information, we design a novel framework called Soft Multiple Superclass (SMS) to extract relevant features or characteristics of classes in the same superclass. A new class assigned to the superclass is easier to classify by leveraging these relevant features. Besides, in order to effectively train the hierarchy-based-detector in FSIS, we apply the label refinement to further describe the associations between fine-grained classes. The extensive experiments demonstrate the effectiveness of our method on FSIS benchmarks. The source code is available here: https://github.com/nvakhoa/superclass-FSIS
CAViaR: Context Aware Video Recommendations

Many recommendation systems rely on point-wise models, which score items individually. However, point-wise models generating scores for a video are unable to account for other videos being recommended in a query. Due to this, diversity has to be introduced through the application of heuristic-based rules, which are not able to capture user preferences, or make balanced trade-offs in terms of diversity and item relevance. In this paper, we propose a novel method which introduces diversity by modeling the impact of low diversity on user’s engagement on individual items, thus being able to account for both diversity and relevance to adjust item scores. The proposed method is designed to be easily pluggable into existing large-scale recommender systems, while introducing minimal changes in the recommendations stack. Our models show significant improvements in offline metrics based on the normalized cross entropy loss compared to production point-wise models. Our approach also shows a substantial increase of 1.7% in topline engagements coupled with a 1.5% increase in daily active users in an A/B test with live traffic on Facebook Watch, which translates into an increase of millions in the number of daily active users for the product.
Strategies for deliveries of anti-cancer drugs from perspectives of a measurement theory and an adsorption theory

In this letter, we present anti-cancer drug delivery strategies using knowledges obtained from our recent studies. We have conducted inverse analyses of"density distributions of colloidal particles near a focused surface"and"pair potentials between the surface and the colloidal particle"using data measured by optical tweezers (OT) and atomic force microscopy (AFM). Non-additive Asakura-Oosawa (NAO) theory and a lattice theory in statistical mechanics of simple polymers have been also our research topics. Summarizing the knowledges, we propose two strategies to increase the delivery rate of capsule shaped anti-cancer drugs to cancer cells. We consider that enhanced repulsion between the normal cell and the drug accelerates the attraction between the cancer cell and the drug, which can be named enhanced repulsion and accelerated adsorption (ERAA) effect. To realize the ERAA effect, we propose a supporting method for measuring the interactions using OT and AFM. In the second strategy, dose of water-soluble polymers is considered to realize adsorptions of the drugs and cancer cell derived exosomes onto the cancer cells, which we call non-specific and selective adsorption (NSSA) effect. In the main text, we explain the NSSA effect using the NAO theory. Moreover, we explain that structural stabilities of normal dispersed proteins around the cancer cells are not largely destroyed by the dosed polymers from the viewpoint of the lattice theory.
HEAT: A Highly Efficient and Affordable Training System for Collaborative Filtering Based Recommendation on CPUs

Collaborative filtering (CF) has been proven to be one of the most effective techniques for recommendation. Among all CF approaches, SimpleX is the state-of-the-art method that adopts a novel loss function and a proper number of negative samples. However, there is no work that optimizes SimpleX on multi-core CPUs, leading to limited performance. To this end, we perform an in-depth profiling and analysis of existing SimpleX implementations and identify their performance bottlenecks including (1) irregular memory accesses, (2) unnecessary memory copies, and (3) redundant computations. To address these issues, we propose an efficient CF training system (called HEAT) that fully enables the multi-level caching and multi-threading capabilities of modern CPUs. Specifically, the optimization of HEAT is threefold: (1) It tiles the embedding matrix to increase data locality and reduce cache misses (thus reduces read latency); (2) It optimizes stochastic gradient descent (SGD) with sampling by parallelizing vector products instead of matrix-matrix multiplications, in particular the similarity computation therein, to avoid memory copies for matrix data preparation; and (3) It aggressively reuses intermediate results from the forward phase in the backward phase to alleviate redundant computation. Evaluation on five widely used datasets with both x86- and ARM-architecture processors shows that HEAT achieves up to 45.2× speedup over existing CPU solution and 4.5× speedup and 7.9× cost reduction in Cloud over existing GPU solution with NVIDIA V100 GPU.
Pitfalls in Effective Knowledge Management: Insights from an International Information Technology Organization

Knowledge is considered an essential resource for organizations. For organizations to benefit from their possessed knowledge, knowledge needs to be managed effectively. Despite knowledge sharing and management being viewed as important by practitioners, organizations fail to benefit from their knowledge, leading to issues in cooperation and the loss of valuable knowledge with departing employees. This study aims to identify hindering factors that prevent individuals from effectively sharing and managing knowledge and understand how to eliminate these factors. Empirical data were collected through semi-structured group interviews from 50 individuals working in an international large IT organization. This study confirms the existence of a gap between the perceived importance of knowledge management and how little this importance is reflected in practice. Several hindering factors were identified, grouped into personal social topics, organizational social topics, technical topics, environmental topics, and interrelated social and technical topics. The presented recommendations for mitigating these hindering factors are focused on improving employees' actions, such as offering training and guidelines to follow. The findings of this study have implications for organizations in knowledge-intensive fields, as they can use this knowledge to create knowledge sharing and management strategies to improve their overall performance.
Systematic Review on Learning-based Spectral CT

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: 1) dual-energy CT (DECT) and 2) photon-counting CT (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.
Political corpus creation through automatic speech recognition on EU debates

In this paper, we present a transcribed corpus of the LIBE committee of the EU parliament, totalling 3.6 Million running words. The meetings of parliamentary committees of the EU are a potentially valuable source of information for political scientists but the data is not readily available because only disclosed as speech recordings together with limited metadata. The meetings are in English, partly spoken by non-native speakers, and partly spoken by interpreters. We investigated the most appropriate Automatic Speech Recognition (ASR) model to create an accurate text transcription of the audio recordings of the meetings in order to make their content available for research and analysis. We focused on the unsupervised domain adaptation of the ASR pipeline. Building on the transformer-based Wav2vec2.0 model, we experimented with multiple acoustic models, language models and the addition of domain-specific terms. We found that a domain-specific acoustic model and a domain-specific language model give substantial improvements to the ASR output, reducing the word error rate (WER) from 28.22 to 17.95. The use of domain-specific terms in the decoding stage did not have a positive effect on the quality of the ASR in terms of WER. Initial topic modelling results indicated that the corpus is useful for downstream analysis tasks. We release the resulting corpus and our analysis pipeline for future research.
Context-Dependent Embedding Utterance Representations for Emotion Recognition in Conversations

Emotion Recognition in Conversations (ERC) has been gaining increasing importance as conversational agents become more and more common. Recognizing emotions is key for effective communication, being a crucial component in the development of effective and empathetic conversational agents. Knowledge and understanding of the conversational context are extremely valuable for identifying the emotions of the interlocutor. We thus approach Emotion Recognition in Conversations leveraging the conversational context, i.e., taking into attention previous conversational turns. The usual approach to model the conversational context has been to produce context-independent representations of each utterance and subsequently perform contextual modeling of these. Here we propose context-dependent embedding representations of each utterance by leveraging the contextual representational power of pre-trained transformer language models. In our approach, we feed the conversational context appended to the utterance to be classified as input to the RoBERTa encoder, to which we append a simple classification module, thus discarding the need to deal with context after obtaining the embeddings since these constitute already an efficient representation of such context. We also investigate how the number of introduced conversational turns influences our model performance. The effectiveness of our approach is validated on the open-domain DailyDialog dataset and on the task-oriented EmoWOZ dataset.
Generalizing Scaling Laws for Mantle Convection With Mixed Heating

Convection in planetary mantles is in the so‐called mixed heating mode; it is driven by heating from below, due to a hotter core, as well as heating from within, due to radiogenic heating and secular cooling. Thus, in order to model the thermal evolution of terrestrial planets, we require the parameterization of heat flux for mixed heated convection in particular. However, deriving such a parameterization from basic principles is an elusive task. While scaling laws for purely internal heating and purely basal heating have been successfully determined using the idea that thermal boundary layers are marginally stable, recent theoretical analyses have questioned the applicability of this idea to convection in the mixed heating mode. Here, we present a scaling approach that is rooted in the physics of convection, including the boundary layer stability criterion. We show that, as long as interactions between thermal boundary layers are properly accounted for, this criterion succeeds in describing relationships between thermal boundary layer (TBL) properties for mixed heated convection. The surface heat flux of a convecting fluid is locally determined by the properties of the upper TBL, as opposed to globally determined. Our foundational scaling approach can be readily extended to nearly any complexity of convection within planetary mantles.
A generalization of iteration-free search vectors of ABS methods

Recently, we introduced iteration-free search vectors of the ABS methods and showed how they can be used to compute the search directions of primal--dual interior point methods, when the coefficient matrix of the constraints of the linear programming problem is square. Here, we generalize those results for the general case when, the coefficient matrix is non-square.
Cross-domain Food Image-to-Recipe Retrieval by Weighted Adversarial Learning

Food image-to-recipe aims to learn an embedded space linking the rich semantics in recipes with the visual content in food image for cross-modal retrieval. The existing research works carry out the learning of such space by assuming that all the image-recipe training example pairs belong to the same cuisine. As a result, despite the excellent performance reported in the literature, such space is not transferable for retrieving recipes of different cuisine. In this paper, we aim to address this issue by cross-domain food image-to-recipe retrieval, such that by leveraging abundant image-recipe pairs in source domain (one cuisine), the embedding space is generalizable to a target domain (the other cuisine) that does not have images to pair with recipes for training. With the intuition that the importance of different source samples should vary, this paper proposes two novel mechanisms for cross-domain food image-to-recipe retrieval, i.e., source data selector and weighted cross-modal adversarial learning. The former aims to select source samples similar to the target data and filter out distinctive ones for training. The latter is capable to assign higher weights to the source samples more similar to the target data and lower weights to suppress the distinctive ones for both cross-modal and adversarial learning. The weights are computed from the recipe features extracted from a pre-trained source model. Experiments on three different cuisines (Chuan, Yue and Washoku) demonstrate that the proposed method manages to achieve state-of-the-art performances in all the transfers.
The Art of Camouflage: Few-shot Learning for Animal Detection and Segmentation

Camouflaged object detection and segmentation is a new and challenging research topic in computer vision. There is a serious issue of lacking data of camouflaged objects such as camouflaged animals in natural scenes. In this paper, we address the problem of few-shot learning for camouflaged object detection and segmentation. To this end, we first collect a new dataset, CAMO-FS, for the benchmark. We then propose a novel method to efficiently detect and segment the camouflaged objects in the images. In particular, we introduce the instance triplet loss and the instance memory storage. The extensive experiments demonstrated that our proposed method achieves state-of-the-art performance on the newly collected dataset.
Spectral classification of young stars using conditional invertible neural networks. I. Introducing and validating the method

Aims. We introduce a new deep learning tool that estimates stellar parameters (such as effective temperature, surface gravity, and extinction) of young low-mass stars by coupling the Phoenix stellar atmosphere model with a conditional invertible neural network (cINN). Our networks allow us to infer the posterior distribution of each stellar parameter from the optical spectrum. Methods. We discuss cINNs trained on three different Phoenix grids: Settl, NextGen, and Dusty. We evaluate the performance of these cINNs on unlearned Phoenix synthetic spectra and on the spectra of 36 Class III template stars with well-characterised stellar parameters. Results. We confirm that the cINNs estimate the considered stellar parameters almost perfectly when tested on unlearned Phoenix synthetic spectra. Applying our networks to Class III stars, we find good agreement with deviations of at most 5--10 per cent. The cINNs perform slightly better for earlier-type stars than for later-type stars like late M-type stars, but we conclude that estimations of effective temperature and surface gravity are reliable for all spectral types within the network's training range. Conclusions. Our networks are time-efficient tools applicable to large amounts of observations. Among the three networks, we recommend using the cINN trained on the Settl library (Settl-Net), as it provides the best performance across the largest range of temperature and gravity.
Uncovering the Background-Induced bias in RGB based 6-DoF Object Pose Estimation

In recent years, there has been a growing trend of using data-driven methods in industrial settings. These kinds of methods often process video images or parts, therefore the integrity of such images is crucial. Sometimes datasets, e.g. consisting of images, can be sophisticated for various reasons. It becomes critical to understand how the manipulation of video and images can impact the effectiveness of a machine learning method. Our case study aims precisely to analyze the Linemod dataset, considered the state of the art in 6D pose estimation context. That dataset presents images accompanied by ArUco markers; it is evident that such markers will not be available in real-world contexts. We analyze how the presence of the markers affects the pose estimation accuracy, and how this bias may be mitigated through data augmentation and other methods. Our work aims to show how the presence of these markers goes to modify, in the testing phase, the effectiveness of the deep learning method used. In particular, we will demonstrate, through the tool of saliency maps, how the focus of the neural network is captured in part by these ArUco markers. Finally, a new dataset, obtained by applying geometric tools to Linemod, will be proposed in order to demonstrate our hypothesis and uncovering the bias. Our results demonstrate the potential for bias in 6DOF pose estimation networks, and suggest methods for reducing this bias when training with markers.
Comparing a classical and quantum one round algorithm on LocalMaxCut

We compare the performance of a quantum local algorithm to a similar classical counter-part on a well-established combinatorial optimization problem LocalMaxCut . We show that a popular quantum algorithm ﬁrst discovered by Farhi, Goldstone, and Gutmannn [1] called the quantum optimization approximation algorithm (QAOA) has a computational advantage over comparable local classical techniques on degree-3 graphs. These results hint that even small-scale quantum computation, which is relevant to the current state-of the art quantum hardware, could have signiﬁcant advantages over comparably simple classical computation.
Simulating the dynamics of binary black holes in nuclear gaseous discs

Abstract We study the pairing of massive black holes embedded in a massive circum–nuclear, rotationally supported disc, until they form a close binary. Using high resolution SPH simulations, we follow the black hole dynamics, and in particular the eccentricity evolution, as a function of the composition in stars and gas of the disc. Binary–disc interaction always leads to orbital decay and, in case of co–rotating black holes, to orbit circularization. We present also a higher resolution simulation performed using the particle–splitting technique showing that the binary orbital decay is efficient down to a separation of ~ 0.1 pc, comparable to our new resolution limit. We detail the gaseous mass profile bound to each black hole. Double nuclear activity is expected to occur on an estimated timescale of ≲ 10 Myrs.
Non-Proportional Parametrizations for Stable Hypernetwork Learning

Hypernetworks are neural networks that generate the parameters of another neural network. In many scenarios current hypernetwork training strategies are unstable, and convergence is often far slower than for non-hypernetwork models. We show that this problem is linked to an issue that arises when using common choices of hypernetwork architecture and initialization. We demonstrate analytically and experimentally how this numerical issue can lead to an instability during training that slows, and sometimes even prevents, convergence. We also demonstrate that popular deep learning normalization strategies fail to address these issues. We then propose a solution to the problem based on a revised hyper-network formulation that uses non-proportional additive parametrizations. We test the proposed reparametrization on several tasks, and demonstrate that it consistently leads to more stable training, achieving faster convergence.
Predicting dynamic, motion-related changes in B0 field in the brain at a 7 T MRI using a subject-specific fine-tuned U-net

Subject movement during the magnetic resonance examination is inevitable and causes not only image artefacts but also deteriorates the homogeneity of the main magnetic field (B0), which is a prerequisite for high quality data. Thus, characterization of changes to B0, e.g. induced by patient movement, is important for MR applications that are prone to B0 inhomogeneities. We propose a deep learning based method to predict such changes within the brain from the change of the head position to facilitate retrospective or even real-time correction. A 3D U-net was trained on in vivo brain 7T MRI data. The input consisted of B0 maps and anatomical images at an initial position, and anatomical images at a different head position (obtained by applying a rigid-body transformation on the initial anatomical image). The output consisted of B0 maps at the new head positions. We further fine-tuned the network weights to each subject by measuring a limited number of head positions of the given subject, and trained the U-net with these data. Our approach was compared to established dynamic B0 field mapping via interleaved navigators, which suffer from limited spatial resolution and the need for undesirable sequence modifications. Qualitative and quantitative comparison showed similar performance between an interleaved navigator-equivalent method and proposed method. We therefore conclude that it is feasible to predict B0 maps from rigid subject movement and, when combined with external tracking hardware, this information could be used to improve the quality of magnetic resonance acquisitions without the use of navigators.