Miracle-dz/UniFAR-FTUs huggingface dataset:
Household food insecurity risk indices for English neighbourhoods: Measures to support local policy decisions

Background : In England, the responsibility to address food insecurity lies with local government, yet the prevalence of this social inequality is unknown in small subnational areas. In 2018 an index of small-area household food insecurity risk was developed and utilised by public and third sector organisations to target interventions; this measure needed updating to better support decisions in different contexts. Methods : We held interviews with stakeholders (n=11) and completed a scoping review to identify appropriate variables to create an updated risk measure. We then sourced a range of open access secondary data to develop an indices of food insecurity risk in English neighbourhoods. Following a process of data transformation and normalisation, we tested combinations of variables and identified the most appropriate data to reflect household food insecurity risk in urban and rural areas. Results : Eight variables, reflecting both household circumstances and local service availability, were separated into two domains with equal weighting for a new index, the Complex Index, and a subset of these make up the Simple Index. Within the Complex Index the Compositional Domain includes population characteristics while the Structural Domain reflects access to resources. The Compositional Domain is correlated well with free school meal eligibility (r s =0.705) and prevalence of childhood obesity (r s =0.641). This domain was the preferred measure for use in most areas when shared with stakeholders, and when assessed alongside other configurations of the variables. Areas of highest risk were most often located in the North of England. Conclusion : We recommend the use of the Compositional Domain for all areas, with inclusion of the Structural Domain in rural areas where locational disadvantage makes it more difficult to access services. These measures can aid local policy makers and planners when allocating resources and interventions to support households who may experience food insecurity.
Psychosocial Predictors of COVID-19 Vaccine Uptake among Pregnant Women: A Cross-Sectional Study in Greece

Background: Unvaccinated pregnant women with symptomatic COVID-19 have been found to have a higher risk of iatrogenic preterm births, intensive care unit admission, and invasive ventilation. Objective: To estimate the vaccination rate of pregnant women against the COVID-19 and to evaluate psychosocial factors associated with vaccine uptake among them. Methods: We conducted an anonymous cross-sectional study with a convenience sample in Greece from December 2021 to March 2022. We measured socio-demographic data of pregnant women, COVID-19-related vaccination status, worry about the side effects of COVID-19 vaccines, trust in COVID-19 vaccines, and COVID-19-related stress. Results: The study population included 812 pregnant women with a mean age of 31.6 years. Among the pregnant women, 58.6% had received a COVID-19 vaccine. The most important reasons that pregnant women were not vaccinated were doubts about the safety and effectiveness of COVID-19 vaccines (31.4%), fear that COVID-19 vaccines could be harmful to fetus (29.4%), and fear of adverse side effects of COVID-19 vaccines (29.4%). Increased danger and contamination fears, increased fears about economic consequences, and higher levels of trust in COVID-19 vaccines were related with COVID-19 vaccine uptake. On the other hand, increased compulsive checking and reassurance seeking and increased worry about the adverse side effects of COVID-19 vaccines reduced the likelihood of pregnant women being vaccinated against the COVID-19. Conclusions: An understanding of the psychosocial factors associated with COVID-19 vaccine uptake in pregnant women is paramount to persuade women to get vaccinated against the COVID-19. There is a need for targeted educational campaigns to increase knowledge about COVID-19 vaccines and reduce COVID-19 vaccine hesitancy in pregnancy.
Leaf trait network architecture shifts with species-richness and climate across forests at continental scale.

Variation in the architecture of trait networks among ecosystems has been rarely quantified, but can provide high resolution of the contrasting adaptation of the whole phenotype. We constructed leaf trait networks (LTNs) from 35 structural, anatomical and compositional leaf traits for 394 tree species in nine forests from tropical to cold-temperate zones in China. Our analyses supported the hypothesis that LTNs would increase in modular complexity across forests in parallel with species-richness and climatic warmth and moisture, due to reduced phenotypic constraints and greater opportunities for niche differentiation. Additionally, we found that within LTNs, leaf economics traits including leaf thickness would have central importance, acting as hub traits with high connectivity due to their contributions to multiple functions. Across the continent, the greater species richness and trait diversity observed in forests under resource-rich climates enable greater complexity in whole phenotype structure and function as indicated by the trait network architecture.
Estrogen receptors observed at extranuclear neuronal sites and in glia in the nucleus accumbens core and shell of the female rat: Evidence for localization to catecholaminergic and GABAergic neurons

Estrogens affect dopamine‐dependent diseases/behavior and have rapid effects on dopamine release and receptor availability in the nucleus accumbens (NAc). Low levels of nuclear estrogen receptor (ER) α and ERβ are seen in the NAc, which cannot account for the rapid effects of estrogens in this region. G‐protein coupled ER 1 (GPER1) is observed at low levels in the NAc shell, which also likely does not account for the array of estrogens’ effects in this region. Prior studies demonstrated membrane‐associated ERs in the dorsal striatum; these experiments extend those findings to the NAc core and shell. Single‐ and dual‐immunolabeling electron microscopy determined whether ERα, ERβ, and GPER1 are at extranuclear sites in the NAc core and shell and whether ERα and GPER1 were localized to catecholaminergic or γ‐aminobutyric acid‐ergic (GABAergic) neurons. All three ERs are observed, almost exclusively, at extranuclear sites in the NAc, and similarly distributed in the core and shell. ERα, ERβ, and GPER1 are primarily in axons and axon terminals suggesting that estrogens affect transmission in the NAc via presynaptic mechanisms. About 10% of these receptors are found on glia. A small proportion of ERα and GPER1 are localized to catecholaminergic terminals, suggesting that binding at these ERs alters release of catecholamines, including dopamine. A larger proportion of ERα and GPER1 are localized to GABAergic dendrites and terminals, suggesting that estrogens alter GABAergic transmission to indirectly affect dopamine transmission in the NAc. Thus, the localization of ERs could account for the rapid effects of estrogen in the NAc.
Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data

Among the many sources of event data available today, a prominent one is user interaction data. User activity may be recorded during the use of an application or website, resulting in a type of user interaction data often called click data. An obstacle to the analysis of click data using process mining is the lack of a case identifier in the data. In this paper, we show a case and user study for event-case correlation on click data, in the context of user interaction events from a mobility sharing company. To reconstruct the case notion of the process, we apply a novel method to aggregate user interaction data in separate user sessions-interpreted as cases-based on neural networks. To validate our findings, we qualitatively discuss the impact of process mining analyses on the resulting well-formed event log through interviews with process experts.
Automatic Parameter Optimization Using Genetic Algorithm in Deep Reinforcement Learning for Robotic Manipulation Tasks

Learning agents can make use of Reinforcement Learning (RL) to decide their actions by using a reward function. However, the learning process is greatly inﬂuenced by the elect of values of the parameters used in the learning algorithm. This work proposed a Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) based method, which makes use of the Genetic Algorithm (GA) to ﬁne-tune the parameters’ values. This method (GA-DRL) experimented on six robotic manipulation tasks: fetch-reach; fetch-slide; fetch-push; fetch-pick and place; door-opening; and aubo-reach. Analysis of these results demonstrated a signiﬁcant increase in performance and a decrease in learning time. Also, we compare and provide evidence that GA-DRL is better than the existing methods.
On the Importance of Performing App Analysis Within Peer Groups

The competing nature of the app market motivates us to shift our focus on apps that provide similar functionalities and directly compete with each other (i.e., peer apps). In this work, we study the ratings and the review text of 100 Android apps across 10 peer app groups. We highlight the importance of performing peer-app analysis by showing that it can provide a unique perspective over performing a global analysis of apps (i.e., mixing apps from multiple categories). First, we observe that comparing user ratings within peer groups can provide very different results from comparing user ratings from a global perspective. Then, we show that peer-app analysis provides a different perspective to spot the dominant topics in the user reviews, and to understand the impact of the topics on user ratings. Our findings suggest that future efforts may pay more attention to performing and supporting app analysis from a peer group context. For example, app store owners may consider an additional rating mechanism that normalizes app ratings within peer groups, and future research may help developers understand the characteristics of specific peer groups and prioritize their efforts.
Semantic Representation and Dependency Learning for Multi-Label Image Recognition

Recently many multi-label image recognition (MLR) works have made significant progress by introducing pre-trained object detection models to generate lots of proposals or utilizing statistical label co-occurrence enhance the correlation among different categories. However, these works have some limitations: (1) the effectiveness of the network significantly depends on pre-trained object detection models that bring expensive and unaffordable computation; (2) the network performance degrades when there exist occasional co-occurrence objects in images, especially for the rare categories. To address these problems, we propose a novel and effective semantic representation and dependency learning (SRDL) framework to learn category-specific semantic representation for each category and capture semantic dependency among all categories. Specifically, we design a category-specific attentional regions (CAR) module to generate channel/spatial-wise attention matrices to guide model to focus on semantic-aware regions. We also design an object erasing (OE) module to implicitly learn semantic dependency among categories by erasing semantic-aware regions to regularize the network training. Extensive experiments and comparisons on two popular MLR benchmark datasets (i.e., MS-COCO and Pascal VOC 2007) demonstrate the effectiveness of the proposed framework over current state-of-the-art algorithms.
From 2D Images to 3D Model: Weakly Supervised Multi-View Face Reconstruction with Deep Fusion

While weakly supervised multi-view face reconstruction (MVR) is garnering increased attention, one critical issue still remains open: how to effectively fuse multiple image information to reconstruct high-precision 3D models. In this regard, we propose a novel model called Deep Fusion MVR (DF-MVR) to reconstruct high-precision 3D facial shapes from multi-view images. Specifically, we introduce MulEn-Unet, a multi-view encoding to single decoding framework with skip connections and attention. This design allows for the extraction, integration, and compensation of deep features with attention from multi-view images. Furthermore, we adopt the involution kernel to enrich deep fusion features with channel features. In addition, we develop the face parse network to learn, identify, and emphasize the critical common face area within multi-view images. Experiments on Pixel-Face and Bosphorus datasets indicate the superiority of our model. Without 3D annotation, DF-MVR achieves 5.2% and 3.0% RMSE improvement over the existing weakly supervised MVRs respectively on Pixel-Face and Bosphorus dataset. Code will be available publicly at https://github.com/weiguangzhao/DF_MVR.
Learning Purely Tactile In-Hand Manipulation with a Torque-Controlled Hand

We show that a purely tactile dextrous in-hand manipulation task with continuous regrasping, requiring permanent force closure, can be learned from scratch and executed robustly on a torque-controlled humanoid robotic hand. The task is rotating a cube without dropping it, but in contrast to OpenAI's seminal cube manipulation task [1], the palm faces downwards and no cameras but only the hand's position and torque sensing are used. Although the task seems simple, it combines for the first time all the challenges in execution as well as learning that are important for using in-hand manipulation in real-world applications. We efficiently train in a precisely modeled and identified rigid body simulation with off-policy deep reinforcement learning, significantly sped up by a domain adapted curriculum, leading to a moderate 600 CPU hours of training time. The resulting policy is robustly transferred to the real humanoid DLR Hand-II, e.g., reaching more than 46 full $2\pi$ rotations of the cube in a single run and allowing for disturbances like different cube sizes, hand orientation, or pulling a finger.
Fair and Argumentative Language Modeling for Computational Argumentation

Although much work in NLP has focused on measuring and mitigating stereotypical bias in semantic spaces, research addressing bias in computational argumentation is still in its infancy. In this paper, we address this research gap and conduct a thorough investigation of bias in argumentative language models. To this end, we introduce ABBA, a novel resource for bias measurement specifically tailored to argumentation. We employ our resource to assess the effect of argumentative fine-tuning and debiasing on the intrinsic bias found in transformer-based language models using a lightweight adapter-based approach that is more sustainable and parameter-efficient than full fine-tuning. Finally, we analyze the potential impact of language model debiasing on the performance in argument quality prediction, a downstream task of computational argumentation. Our results show that we are able to successfully and sustainably remove bias in general and argumentative language models while preserving (and sometimes improving) model performance in downstream tasks. We make all experimental code and data available at https://github.com/umanlp/FairArgumentativeLM.
Contextual Representation Learning beyond Masked Language Modeling

Currently, masked language modeling (e.g., BERT) is the prime choice to learn contextualized representations. Due to the pervasiveness, it naturally raises an interesting question: how do masked language models (MLMs) learn contextual representations? In this work, we analyze the learning dynamics of MLMs and find that it adopts sampled embeddings as anchors to estimate and inject contextual semantics to representations, which limits the efficiency and effectiveness of MLMs. To address these problems, we propose TACO, a simple yet effective representation learning approach to directly model global semantics. To be specific, TACO extracts and aligns contextual semantics hidden in contextualized representations to encourage models to attend global semantics when generating contextualized representations. Experiments on the GLUE benchmark show that TACO achieves up to 5x speedup and up to 1.2 points average improvement over MLM.
Parameterized Differential Dynamic Programming

Differential Dynamic Programming (DDP) is an efficient trajectory optimization algorithm relying on second-order approximations of a system's dynamics and cost function, and has recently been applied to optimize systems with time-invariant parameters. Prior works include system parameter estimation and identifying the optimal switching time between modes of hybrid dynamical systems. This paper generalizes previous work by proposing a general parameterized optimal control objective and deriving a parametric version of DDP, titled Parameterized Differential Dynamic Programming (PDDP). A rigorous convergence analysis of the algorithm is provided, and PDDP is shown to converge to a minimum of the cost regardless of initialization. The effects of varying the optimization to more effectively escape local minima are analyzed. Experiments are presented applying PDDP on multiple robotics systems to solve model predictive control (MPC) and moving horizon estimation (MHE) tasks simultaneously. Finally, PDDP is used to determine the optimal transition point between flight regimes of a complex urban air mobility (UAM) class vehicle exhibiting multiple phases of flight.
Reliable Visualization for Deep Speaker Recognition

In spite of the impressive success of convolutional neural networks (CNNs) in speaker recognition, our understanding to CNNs' internal functions is still limited. A major obstacle is that some popular visualization tools are difficult to apply, for example those producing saliency maps. The reason is that speaker information does not show clear spatial patterns in the temporal-frequency space, which makes it hard to interpret the visualization results, and hence hard to confirm the reliability of a visualization tool. In this paper, we conduct an extensive analysis on three popular visualization methods based on CAM: Grad-CAM, Score-CAM and Layer-CAM, to investigate their reliability for speaker recognition tasks. Experiments conducted on a state-of-the-art ResNet34SE model show that the Layer-CAM algorithm can produce reliable visualization, and thus can be used as a promising tool to explain CNN-based speaker models. The source code and examples are available in our project page: http://project.cslt. org/.
The Complexity of Markov Equilibrium in Stochastic Games

We show that computing approximate stationary Markov coarse correlated equilibria (CCE) in general-sum stochastic games is computationally intractable, even when there are two players, the game is turn-based, the discount factor is an absolute constant, and the approximation is an absolute constant. Our intractability results stand in sharp contrast to normal-form games where exact CCEs are efficiently computable. A fortiori, our results imply that there are no efficient algorithms for learning stationary Markov CCE policies in multi-agent reinforcement learning (MARL), even when the interaction is two-player and turn-based, and both the discount factor and the desired approximation of the learned policies is an absolute constant. In turn, these results stand in sharp contrast to single-agent reinforcement learning (RL) where near-optimal stationary Markov policies can be efficiently learned. Complementing our intractability results for stationary Markov CCEs, we provide a decentralized algorithm (assuming shared randomness among players) for learning a nonstationary Markov CCE policy with polynomial time and sample complexity in all problem parameters. Previous work for learning Markov CCE policies all required exponential time and sample complexity in the number of players.
Learning Cooperative Dynamic Manipulation Skills from Human Demonstration Videos

This article proposes a method for learning and robotic replication of dynamic collaborative tasks from o ﬄ ine videos. The objective is to extend the concept of learning from demonstration (LfD) to dynamic scenarios, beneﬁting from widely available or easily producible o ﬄ ine videos. To achieve this goal, we decode important dynamic information, such as the Conﬁguration Dependent Sti ﬀ ness (CDS), which reveals the contribution of arm pose to the arm endpoint sti ﬀ ness, from a three-dimensional human skeleton model. Next, through encoding of the CDS via Gaussian Mixture Model (GMM) and decoding via Gaussian Mixture Regression (GMR), the robot’s Cartesian impedance proﬁle is estimated and replicated. We demonstrate the proposed method in a collaborative sawing task with leader-follower structure, considering environmental constraints and dynamic uncertainties. The experimental setup includes two Panda robots, which replicate the leader-follower roles and the impedance proﬁles extracted from a two-persons sawing video.
Multimodal Quasi-AutoRegression: forecasting the visual popularity of new fashion products

Estimating the preferences of consumers is of utmost importance for the fashion industry as appropriately leveraging this information can be beneficial in terms of profit. Trend detection in fashion is a challenging task due to the fast pace of change in the fashion industry. Moreover, forecasting the visual popularity of new garment designs is even more demanding due to lack of historical data. To this end, we propose MuQAR, a Multimodal Quasi-AutoRegressive deep learning architecture that combines two modules: (1) a multimodal multilayer perceptron processing categorical, visual and textual features of the product and (2) a Quasi-AutoRegressive neural network modelling the “target” time series of the product’s attributes along with the “exogenous” time series of all other attributes. We utilize computer vision, image classification and image captioning, for automatically extracting visual features and textual descriptions from the images of new products. Product design in fashion is initially expressed visually and these features represent the products’ unique characteristics without interfering with the creative process of its designers by requiring additional inputs (e.g. manually written texts). We employ the product’s target attributes time series as a proxy of temporal popularity patterns, mitigating the lack of historical data, while exogenous time series help capture trends among interrelated attributes. We perform an extensive ablation analysis on two large-scale image fashion datasets, Mallzee-P and SHIFT15m to assess the adequacy of MuQAR and also use the Amazon Reviews: Home and Kitchen dataset to assess generalization to other domains. A comparative study on the VISUELLE dataset shows that MuQAR is capable of competing and surpassing the domain’s current state of the art by 4.65% and 4.8% in terms of WAPE and MAE, respectively.
Enhance Incomplete Utterance Restoration by Joint Learning Token Extraction and Text Generation

This paper introduces a model for incomplete utterance restoration (IUR) called JET (Joint learning token Extraction and Text generation). Different from prior studies that only work on extraction or abstraction datasets, we design a simple but effective model, working for both scenarios of IUR. Our design simulates the nature of IUR, where omitted tokens from the context contribute to restoration. From this, we construct a Picker that identifies the omitted tokens. To support the picker, we design two label creation methods (soft and hard labels), which can work in cases of no annotation data for the omitted tokens. The restoration is done by using a Generator with the help of the Picker on joint learning. Promising results on four benchmark datasets in extraction and abstraction scenarios show that our model is better than the pretrained T5 and non-generative language model methods in both rich and limited training data settings.