Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical Attention Network for Visually-Aware Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. This work formulates the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users’) history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, this work develops a dedicated neural network-based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, this work constructs a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference on food.
Efficiency through uncertainty: scalable formal synthesis for stochastic hybrid systems

This work targets the development of an efficient abstraction method for formal analysis and control synthesis of discrete-time stochastic hybrid systems (SHS) with linear dynamics. The focus is on temporal logic specifications over both finite- and infinite-time horizons. The framework constructs a finite abstraction as a class of uncertain Markov models known as interval Markov decision process (IMDP). Then, a strategy that maximizes the satisfaction probability of the given specification is synthesized over the IMDP and mapped to the underlying SHS. In contrast to existing formal approaches, which are by and large limited to finite-time properties and rely on conservative over-approximations, we show that the exact abstraction error can be computed as a solution of convex optimization problems and can be embedded into the IMDP abstraction. This is later used in the synthesis step over both bounded- and unbounded-time properties, mitigating the known state-space explosion problem. Our experimental validation of the new approach compared to existing abstraction-based approaches shows: (i) significant (orders of magnitude) reduction of the abstraction error; (ii) marked speed-ups; and (iii) boosted scalability, allowing in particular to verify models with more than 10 continuous variables.
Age-of- Information for Computation- Intensive Messages in Mobile Edge Computing

Age-of-information (AoI) is a novel metric that measures the freshness of information in status update scenarios. It is essential for real-time applications to transmit status update packets to the destination node as timely as possible. However, for some applications, status information embedded in the packets is not revealed until complicated data processing, which is computational expensive and time consuming. As the mobile edge server has sufficient computational resource and is placed close to users, mobile edge computing (MEC) is expected to reduce age for computation-intensive messages. In this paper, we study the AoI for computation-intensive data in MEC, and consider two computing schemes: local computing by user itself and remote computing at MEC server. The two computing models are unified into a two-node tandem queuing model. Zero-wait policy is adopted, i.e., a new message is generated once the previous one leaves the first node. We consider exponentially distributed service time and infinite queue size, and hence, the second node can be seen as an First-Come-First-Served (FCFS) M/M/1 system. Closed-form average AoIs are derived for the two computing schemes. The region where remote computing outperforms local computing is characterized. Simulation results show that there exists an optimal transmission rate so that remote computing is better than local computing for a largest range.
Transductive Zero-Shot Learning with Visual Structure Constraint

To recognize objects of the unseen classes, most existing Zero-Shot Learning(ZSL) methods first learn a compatible projection function between the common semantic space and the visual space based on the data of source seen classes, then directly apply it to the target unseen classes. However, in real scenarios, the data distribution between the source and target domain might not match well, thus causing the well-known \textbf{domain shift} problem. Based on the observation that visual features of test instances can be separated into different clusters, we propose a new visual structure constraint on class centers for transductive ZSL, to improve the generality of the projection function (i.e. alleviate the above domain shift problem). Specifically, three different strategies (symmetric Chamfer-distance, Bipartite matching distance, and Wasserstein distance) are adopted to align the projected unseen semantic centers and visual cluster centers of test instances. We also propose a new training strategy to handle the real cases where many unrelated images exist in the test dataset, which is not considered in previous methods. Experiments on many widely used datasets demonstrate that the proposed visual structure constraint can bring substantial performance gain consistently and achieve state-of-the-art results. The source code is available at \url{this https URL}.
Supervised Neural Networks for Helioseismic Ring-Diagram Inversions

Context. The inversion of ring fit parameters to obtain subsurface flow maps in ring-diagram analysis for eight years of SDO observations is computationally expensive, requiring ∼3200 CPU hours. Aims. In this paper we apply machine-learning techniques to the inversion step of the ring diagram pipeline in order to speed up the calculations. Specifically, we train a predictor for subsurface flows using the mode fit parameters and the previous inversion results to replace future inversion requirements. Methods. We utilize artificial neural networks (ANNs) as a supervised learning method for predicting the flows in 15° ring tiles. We discuss each step of the proposed method to determine the optimal approach. In order to demonstrate that the machine-learning results still contain the subtle signatures key to local helioseismic studies, we use the machine-learning results to study the recently discovered solar equatorial Rossby waves. Results. The ANN is computationally efficient, able to make future flow predictions of an entire Carrington rotation in a matter of seconds, which is much faster than the current ∼31 CPU hours. Initial training of the networks requires ∼3 CPU hours. The trained ANN can achieve a rms error equal to approximately half that reported for the velocity inversions, demonstrating the accuracy of the machine learning (and perhaps the overestimation of the original errors from the ring-diagram pipeline). We find the signature of equatorial Rossby waves in the machine-learning flows covering six years of data, demonstrating that small-amplitude signals are maintained. The recovery of Rossby waves in the machine-learning flow maps can be achieved with only one Carrington rotation (27.275 days) of training data. Conclusions. We show that machine learning can be applied to and perform more efficiently than the current ring-diagram inversion. The computation burden of the machine learning includes 3 CPU hours for initial training, then around 10−4 CPU hours for future predictions.
Variational bridge constructs for approximate Gaussian process regression

This paper introduces a method to approximate Gaussian process regression by representing the problem as a stochastic differential equation and using variational inference to approximate solutions. The approximations are compared with full GP regression and generated paths are demonstrated to be indistinguishable from GP samples. We show that the approach extends easily to non-linear dynamics and discuss extensions to which the approach can be easily applied.
Composite optimization for robust blind deconvolution

The blind deconvolution problem seeks to recover a pair of vectors from a set of rank one bilinear measurements. We consider a natural nonsmooth formulation of the problem and show that under standard statistical assumptions, its moduli of weak convexity, sharpness, and Lipschitz continuity are all dimension independent. This phenomenon persists even when up to half of the measurements are corrupted by noise. Consequently, standard algorithms, such as the subgradient and prox-linear methods, converge at a rapid dimension-independent rate when initialized within constant relative error of the solution. We then complete the paper with a new initialization strategy, complementing the local search algorithms. The initialization procedure is both provably efficient and robust to outlying measurements. Numerical experiments, on both simulated and real data, illustrate the developed theory and methods.
Coding Techniques for Future Wi-Fi

Future wireless applications such as high definition video streaming, wireless cloud radio access networks, and cellular data offload are bandwidth-hungry, which highlights the need for Wi-Fi links exceeding 1 Gb/s. This article discusses coding schemes and, in particular, the use of Low-Density Parity-Check (LDPC) codes as Forward Error Correction (FEC) in future Wi-Fi standards. Moreover, we consider advanced design strategies such as root-check LDPC structures, computer-aided design for short blocks and high-performance decoding algorithms with low latency. We then conclude the article with a discussion of FEC challenges for future Wi-FI applications.
A Systematic Review of Tracing Solutions in Software Product Lines

Software Product Lines are large-scale, multi-unit systems that enable massive, customized production. They consist of a base of reusable artifacts and points of variation that provide the system with flexibility, allowing generating customized products. However, maintaining a system with such complexity and flexibility could be error prone and time consuming. Indeed, any modification (addition, deletion or update) at the level of a product or an artifact would impact other elements. It would therefore be interesting to adopt an efficient and organized traceability solution to maintain the Software Product Line. Still, traceability is not systematically implemented. It is usually set up for specific constraints (e.g. certification requirements), but abandoned in other situations. In order to draw a picture of the actual conditions of traceability solutions in Software Product Lines context, we decided to address a literature review. This review as well as its findings is detailed in the present article.
Distributed Learning with Adversarial Agents Under Relaxed Network Condition

This work studies the problem of non-Bayesian learning over multi-agent network when there are some adversarial (faulty) agents in the network. At each time step, each non-faulty agent collects partial information about an unknown state of the world and tries to estimate true state of the world by iteratively sharing information with its neighbors. Existing algorithms in this setting require that all non-faulty agents in the network should be able to achieve consensus via local information exchange. In this work, we present an analysis of a distributed algorithm which does not require the network to achieve consensus. We show that if every non-faulty agent can receive enough information (via iteratively communicating with neighbors) to differentiate the true state of the world from other possible states then it can indeed learn the true state.
Dynamic and Application-Aware Provisioning of Chained Virtual Security Network Functions

A promising area of application for Network Function Virtualization (NFV) is in network security, where chains of Virtual Security Network Functions (VSNFs), i.e., security-specific virtual functions such as firewalls or Intrusion Prevention Systems, can be dynamically created and configured to inspect, filter or monitor the network traffic. However, the traffic handled by VSNFs could be sensitive to specific network requirements, such as minimum bandwidth or maximum end-to-end latency. Therefore, the decision on which VSNFs should apply for a given application, where to place them and how to connect them, should take such requirements into consideration. Otherwise, security services could affect the quality of service experienced by customers. In this paper, we propose PESS (Progressive Embedding of Security Services), a solution to efficiently deploy chains of virtualised security functions based on the security requirements of individual applications and operators’ policies, while optimizing resource utilization. We provide the PESS mathematical model and heuristic solution. Simulation results show that, compared to state-of-the-art application-agnostic VSNF provisioning models, PESS reduces computational resource utilization by up to 50%, in different network scenarios. This result ultimately leads to a higher number of provisioned security services and to up to a 40% reduction in end-to-end latency of application traffic.
Human Pose Estimation with Spatial Contextual Information

We explore the importance of spatial contextual information in human pose estimation. Most state-of-the-art pose networks are trained in a multi-stage manner and produce several auxiliary predictions for deep supervision. With this principle, we present two conceptually simple and yet computational efficient modules, namely Cascade Prediction Fusion (CPF) and Pose Graph Neural Network (PGNN), to exploit underlying contextual information. Cascade prediction fusion accumulates prediction maps from previous stages to extract informative signals. The resulting maps also function as a prior to guide prediction at following stages. To promote spatial correlation among joints, our PGNN learns a structured representation of human pose as a graph. Direct message passing between different joints is enabled and spatial relation is captured. These two modules require very limited computational complexity. Experimental results demonstrate that our method consistently outperforms previous methods on MPII and LSP benchmark.
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion

Matrix completion focuses on recovering a matrix from a small subset of its observed elements, and has already gained cumulative attention in computer vision. Many previous approaches formulate this issue as a low-rank matrix approximation problem. Recently, a truncated nuclear norm has been presented as a surrogate of traditional nuclear norm, for better estimation to the rank of a matrix. The truncated nuclear norm regularization (TNNR) method is applicable in real-world scenarios. However, it is sensitive to the selection of the number of truncated singular values and requires numerous iterations to converge. Hereby, this paper proposes a revised approach called the double weighted truncated nuclear norm regularization (DW-TNNR), which assigns different weights to the rows and columns of a matrix separately, to accelerate the convergence with acceptable performance. The DW-TNNR is more robust to the number of truncated singular values than the TNNR. Instead of the iterative updating scheme in the second step of TNNR, this paper devises an efficient strategy that uses a gradient descent manner in a concise form, with a theoretical guarantee in optimization. Sufficient experiments conducted on real visual data prove that DW-TNNR has promising performance and holds the superiority in both speed and accuracy for matrix completion.
Resilient Design of 5G Mobile-Edge Computing Over Intermittent mmWave Links

Two enablers of the 5th Generation (5G) of mobile communication systems are the high data rates achievable with millimeter-wave radio signals and the cloudification of the network's mobile edge, made possible also by Multi-access Edge Computing (MEC). In 5G networks, user devices may exploit the high capacity of their mobile connection and the computing capabilities of the edge cloud to offload computational tasks to MEC servers, which run applications on devices' behalf. This paper investigates new methods to perform power- and latency-constrained offloading. First, aiming to minimize user devices' transmit power, the opportunity to exploit concurrent communication links between the device and the edge cloud is studied. The optimal number of channels for simultaneous transmission is characterized in a deterministic and a probabilistic scenario. Subsequently, blocking events that obstruct millimeter-wave channels making them `intermittent' are considered. Resource overprovisioning and error-correcting codes against asymmetric block erasures are proposed to jointly contrast blocking and exploit multi-link communications' diversity. The asymmetric block-erasure channel is characterized by a study of its outage probability. The analysis is performed in a framework that yields closed-form expressions. These, together with corroborating numerical results, are intended to provide reference points and bounds to optimal performance in practical applications.
Toward a Politics of Immediation

A passage from Fred Moten haunts the writing of these words that seek to push a politics of identity toward a politics of immediation. In a parenthetical aside in a paper on the city and the commune, Fred Moten cautions us as regards the critique of identity politics. Too often, he writes, critiques of identity politics are waged against "non-white, non-straight, non-male identity [. ] while courteously leaving politics to its own uncriticized devices” (2016: 163). Through the figure of immediation, I ask: what happens when we do not insert a mediating gesture at the heart of experience? Is such a move capable of problematizing the figure of identity while still remaining sensitive to the fact for some the loss of a sense of identity may feel like the very same gesture as the colonial act of exclusion from the category of the human? In this double articulation of refusing mediation and introducing a time-form that challenges human-centred, colonial history, can we create an affirmative politics of emergent subjectivity that does not ignore that allegiances are necessary in the face of the systemic violence of oppression? The task, it seems to me, involves recasting allegiance such that it need no longer be subsumed to identity, and, by extension, to the individual. For the individual, that pet-figure of neoliberalism, is nothing more than the other side of the subject, which is the other side of the human. To focus on the individual in an exploration of modes of existence, to make the politics about the individual, is to reinstall a mediation that knows in advance how to recognize the human as orienter of experience. The problem of identity must instead be engaged from the perspective of Wynter’s “descriptive statement” of the human. This category of the human, as Wynter underscores, is concerned to perpetuate a genre of the human (2015: 9). What kinds of sociality cut across this genre?
Loss of Function of the E1-Like-b Gene Associates With Early Flowering Under Long-Day Conditions in Soybean

Photoperiod response of flowering determines plant adaptation to different latitudes. Soybean, a short-day plant, has gained the ability to flower under long-day conditions during the growing season at higher latitudes, mainly through dysfunction of phytochrome A genes (E3 and E4) and the floral repressor E1. In this study, we identified a novel molecular genetic basis of photoperiod insensitivity in Far-Eastern Russian soybean cultivars. By testcrossing these cultivars with a Canadian cultivar Harosoy near-isogenic line for a recessive e3 allele, followed by association tests and fine mapping, we determined that the insensitivity was inherited as a single recessive gene located in an 842-kb interval in the pericentromeric region of chromosome 4, where E1-Like b (E1Lb), a homoeolog of E1, is located. Sequencing analysis detected a single-nucleotide deletion in the coding sequence of the gene in insensitive cultivars, which generated a premature stop codon. Near-isogenic lines (NILs) for the loss-of-function allele (designated e1lb) exhibited upregulated expression of soybean FLOWERING LOCUS T (FT) orthologs, FT2a and FT5a, and flowered earlier than those for E1Lb under long-day conditions in both the e3/E4 and E3/E4 genetic backgrounds. These NILs further lacked the inhibitory effect on flowering by far-red light–enriched long-day conditions, which is mediated by E4, but not that of red-light–enriched long-day conditions, which is mediated by E3. These findings suggest that E1Lb retards flowering under long-day conditions by repressing the expression of FT2a and FT5a independently of E1. This loss-of-function allele can be used as a new resource in breeding of photoperiod-insensitive cultivars, and may improve our understanding of the function of the E1 family genes in the photoperiod responses of flowering in soybean.
Eating Disorder Symptoms and Proneness in Gay Men, Lesbian Women, and Transgender and Gender Non-conforming Adults: Comparative Levels and a Proposed Mediational Model

In this study we sought to compare eating disorder attitudes and behaviors, and proneness to an eating disorder (“ED proneness”), between gay men, lesbian women, and transgender and gender non-conforming (TGNC) adults. A further aim was to identify and compare risk and protective factors, and examine a mediational model based on the interpersonal theory of eating disorders (IPT-ED), whereby the association between interpersonal factors and ED proneness would be mediated by psychological constructs pertaining to the self and negative affect. Data was obtained from a larger national study of health risk and protective factors among sexual minority and gender diverse populations. The sample included 97 gay men, 82 lesbian women, and 138 TGNC adults. Participants completed the National College Health Assessment, Eating Disorders Screen for Primary Care, Patient Health Questionnaire Depression scale, Generalized Anxiety Disorder 7 scale, Self-Compassion Scale-Short Form, Negative Social Exchange subscale of the Multidimensional Health Profile, Interpersonal Needs Questionnaire, and Perceived Stigma Scale. There was a significant difference between groups in ED proneness, with lesbian women (66.7%) having a significantly higher percentage than gay men (47.6%). There was also a significant difference between groups in weight-based self-worth, with the lowest percentage in gay men (63%) and the highest percentage in lesbian women (82%), as well as dissatisfaction with eating patterns, with the highest percentage in TGNC adults (69.8%) and the lowest percentage in gay men (47.7%). There was a low percentage of inappropriate compensatory behaviors, with no significant difference between groups. Logistic regression analyses showed that the predictor variables of ED proneness were depression, perceived stigma, and self-compassion in gay men; depression in lesbian women; and self-compassion in the TGNC adults. Mediation analyses showed that thwarted belongingness (i.e., an unmet to belong) and perceived stigma had an indirect association with ED proneness that was mediated by self-compassion and depression (for perceived stigma alone) in gay men, depression in lesbian women, and self-compassion in TGNC adults. The interpersonal theory of eating disorders therefore extends to sexual minority and gender diverse populations; however, the results suggest a broadening of theoretical models and intervention programs to include the role of stigma and self-compassion.