Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Base Question Answering by Case-based Reasoning over Subgraphs

Question answering (QA) over knowledge bases (KBs) is challenging because of the diverse, essentially unbounded, types of reasoning patterns needed. However, we hypothesize in a large KB, reasoning patterns required to answer a query type reoccur for various entities in their respective subgraph neighborhoods. Leveraging this structural similarity between local neighborhoods of different subgraphs, we introduce a semiparametric model (CBR-SUBG) with (i) a nonparametric component that for each query, dynamically retrieves other similar $k$-nearest neighbor (KNN) training queries along with query-specific subgraphs and (ii) a parametric component that is trained to identify the (latent) reasoning patterns from the subgraphs of KNN queries and then apply them to the subgraph of the target query. We also propose an adaptive subgraph collection strategy to select a query-specific compact subgraph, allowing us to scale to full Freebase KB containing billions of facts. We show that CBR-SUBG can answer queries requiring subgraph reasoning patterns and performs competitively with the best models on several KBQA benchmarks. Our subgraph collection strategy also produces more compact subgraphs (e.g. 55\% reduction in size for WebQSP while increasing answer recall by 4.85\%)\footnote{Code, model, and subgraphs are available at \url{https://github.com/rajarshd/CBR-SUBG}}.
Self-Evolutionary Clustering

Deep clustering outperforms conventional clustering by mutually promoting representation learning and cluster assignment. However, most existing deep clustering methods suffer from two major drawbacks. First, most cluster assignment methods are based on simple distance comparison and highly dependent on the target distribution generated by a handcrafted nonlinear mapping. These facts largely limit the possible performance that deep clustering methods can reach. Second, the clustering results can be easily guided towards wrong direction by the misassigned samples in each cluster. The existing deep clustering methods are incapable of discriminating such samples. To address these issues, a novel modular Self-Evolutionary Clustering (Self-EvoC) framework is constructed, which boosts the clustering performance by classification in a self-supervised manner. Fuzzy theory is used to score the sample membership with probability which evaluates the intermediate clustering result certainty of each sample. Based on which, the most reliable samples can be selected and augmented. The augmented data are employed to fine-tune an off-the-shelf deep network classifier with the labels from the clustering, which results in a model to generate the target distribution. The proposed framework can efficiently discriminate sample outliers and generate better target distribution with the assistance of self-supervised classifier. Extensive experiments indicate that the Self-EvoC remarkably outperforms state-of-the-art deep clustering methods on three benchmark datasets.
Universal 1-Bit Compressive Sensing for Bounded Dynamic Range Signals

A universal 1-bit compressive sensing (CS) scheme consists of a measurement matrix A such that for all signals x belonging to a particular class, x can be approximately recovered from sign(Ax). 1-bit CS models extreme quantization effects where only one bit of information is revealed per measurement. We focus on universal support recovery for 1-bit CS in the case of sparse signals with bounded dynamic range. Specifically, a vector x ∈ℝn is said to have sparsity k if it has at most k nonzero entries and dynamic range R if the ratio between its largest and smallest nonzero entries is at most R in magnitude. Our main result shows that if the entries of the measurement matrix Ax are i.i. d. Gaussians, then the number of measurements needs to be ${{\tilde \Omega }}\left({R{k^{3/2}}}\right)$ to recover the support of k-sparse signals with dynamic range R using 1-bit CS. This contrasts with the known lower bound of ${{\tilde \Omega }}\left({{k^2}\log n}\right)$ for the number of measurements to recover the support of arbitrary k-sparse signals. In broad scaling regimes of interest, our lower bounds match upper bounds implicit in prior works up to logarithmic factors.
Provably convergent quasistatic dynamics for mean-field two-player zero-sum games

In this paper, we study the problem of finding mixed Nash equilibrium for mean-field two-player zero-sum games. Solving this problem requires optimizing over two probability distributions. We consider a quasistatic Wasserstein gradient flow dynamics in which one probability distribution follows the Wasserstein gradient flow, while the other one is always at the equilibrium. Theoretical analysis are conducted on this dynamics, showing its convergence to the mixed Nash equilibrium under mild conditions. Inspired by the continuous dynamics of probability distributions, we derive a quasistatic Langevin gradient descent method with inner-outer iterations, and test the method on different problems, including training mixture of GANs.
Message passing all the way up

The message passing framework is the foundation of the immense success enjoyed by graph neural networks (GNNs) in recent years. In spite of its elegance, there exist many problems it provably cannot solve over given input graphs. This has led to a surge of research on going"beyond message passing", building GNNs which do not suffer from those limitations -- a term which has become ubiquitous in regular discourse. However, have those methods truly moved beyond message passing? In this position paper, I argue about the dangers of using this term -- especially when teaching graph representation learning to newcomers. I show that any function of interest we want to compute over graphs can, in all likelihood, be expressed using pairwise message passing -- just over a potentially modified graph, and argue how most practical implementations subtly do this kind of trick anyway. Hoping to initiate a productive discussion, I propose replacing"beyond message passing"with a more tame term,"augmented message passing".
Secure Joint Communication and Sensing

This work considers mitigation of information leakage between communication and sensing operations in joint communication and sensing systems. Specifically, a discrete memoryless state-dependent broadcast channel model is studied in which (i) the presence of feedback enables a transmitter to simultaneously achieve reliable communication and channel state estimation; (ii) one of the receivers is treated as an eavesdropper whose state should be estimated but which should remain oblivious to a part of the transmitted information. The model abstracts the challenges behind security for joint communication and sensing if one views the channel state as a characteristic of the receiver, e.g., its location. For independent and identically distributed (i.i.d.) states, perfect output feedback, and when part of the transmitted message should be kept secret, a partial characterization of the secrecy-distortion region is developed. The characterization is exact when the broadcast channel is either physically-degraded or reversely-physically-degraded. The characterization is also extended to the situation in which the entire transmitted message should be kept secret. The benefits of a joint approach compared to separation-based secure communication and state-sensing methods are illustrated with a binary joint communication and sensing model.
Automatically Generating Counterfactuals for Relation Exaction

The goal of relation extraction (RE) is to extract the semantic relations between/among entities in the text. As a fundamental task in natural language processing, it is crucial to ensure the robustness of RE models. Despite the high accuracy current deep neural models have achieved in RE tasks, they are easily affected by spurious correlations. One so-lution to this problem is to train the model with counterfactually augmented data (CAD) such that it can learn the causation rather than the confounding. However, no attempt has been made on generating counterfactuals for RE tasks. In this paper, we formulate the problem of automatically generating CAD for RE tasks from an entity-centric viewpoint, and develop a novel approach to derive contextual counterfactuals for entities. Speciﬁcally, we exploit two elementary topological properties, i.e., the centrality and the shortest path, in syntactic and semantic dependency graphs, to ﬁrst identify and then intervene on the contextual causal features for entities. We conduct a comprehensive evaluation on four RE datasets by combining our proposed approach with a variety of back-bone RE models. The results demonstrate that our approach not only improves the performance of the backbones, but also makes them more robust in the out-of-domain test.
How I Learned to Stop Worrying and Love Retraining

Many Neural Network Pruning approaches consist of several iterative training and pruning steps, seemingly losing a significant amount of their performance after pruning and then recovering it in the subsequent retraining phase. Recent works of Renda et al. (2020) and Le&Hua (2021) demonstrate the significance of the learning rate schedule during the retraining phase and propose specific heuristics for choosing such a schedule for IMP (Han et al., 2015). We place these findings in the context of the results of Li et al. (2020) regarding the training of models within a fixed training budget and demonstrate that, consequently, the retraining phase can be massively shortened using a simple linear learning rate schedule. Improving on existing retraining approaches, we additionally propose a method to adaptively select the initial value of the linear schedule. Going a step further, we propose similarly imposing a budget on the initial dense training phase and show that the resulting simple and efficient method is capable of outperforming significantly more complex or heavily parameterized state-of-the-art approaches that attempt to sparsify the network during training. These findings not only advance our understanding of the retraining phase, but more broadly question the belief that one should aim to avoid the need for retraining and reduce the negative effects of 'hard' pruning by incorporating the sparsification process into the standard training.
Distributionally Robust Optimization Approaches for a Stochastic Mobile Facility Fleet Sizing, Routing, and Scheduling Problem

We propose two distributionally robust optimization (DRO) models for a mobile facility (MF) fleet-sizing, routing, and scheduling problem (MFRSP) with time-dependent and random demand as well as methodologies for solving these models. Specifically, given a set of MFs, a planning horizon, and a service region, our models aim to find the number of MFs to use (i.e., fleet size) within the planning horizon and a route and time schedule for each MF in the fleet. The objective is to minimize the fixed cost of establishing the MF fleet plus a risk measure (expectation or mean conditional value at risk) of the operational cost over all demand distributions defined by an ambiguity set. In the first model, we use an ambiguity set based on the demand’s mean, support, and mean absolute deviation. In the second model, we use an ambiguity set that incorporates all distributions within a 1-Wasserstein distance from a reference distribution. To solve the proposed DRO models, we propose a decomposition-based algorithm. In addition, we derive valid lower bound inequalities that efficiently strengthen the master problem in the decomposition algorithm, thus improving convergence. We also derive two families of symmetry-breaking constraints that improve the solvability of the proposed models. Finally, we present extensive computational experiments comparing the operational and computational performance of the proposed models and a stochastic programming model, demonstrating when significant performance improvements could be gained, and derive insights into the MFRSP.
A New Look at Spike-Timing-Dependent Plasticity Networks for Spatio-Temporal Feature Learning

We present new theoretical foundations for unsupervised Spike-Timing-Dependent Plasticity (STDP) learning in spiking neural networks (SNNs). In contrast to empirical parameter search used in most previous works, we provide novel theoretical grounds for SNN and STDP parameter tuning which considerably reduces design time. Using our generic framework, we propose a class of global, action-based and convolutional SNN-STDP architectures for learning spatio-temporal features from event-based cameras. We assess our methods on the N-MNIST, the CIFAR10-DVS and the IBM DVS128 Gesture datasets, all acquired with a real-world event camera. Using our framework, we report significant improvements in classification accuracy compared to both conventional state-of-the-art event-based feature descriptors (+8.2% on CIFAR10-DVS), and compared to state-of-the-art STDP-based systems (+9.3% on N-MNIST, +7.74% on IBM DVS128 Gesture). Our work contributes to both ultra-low-power learning in neuromorphic edge devices, and towards a biologically-plausible, optimization-based theory of cortical vision.
Permutation-Equivariant and Proximity-Aware Graph Neural Networks With Stochastic Message Passing

Graph neural networks (GNNs) are emerging machine learning models on graphs. Permutation-equivariance and proximity-awareness are two important properties highly desirable for GNNs. Both properties are needed to tackle some challenging graph problems, such as finding communities and leaders. In this paper, we first analytically show that the existing GNNs, mostly based on the message-passing mechanism, cannot simultaneously preserve the two properties. Then, we propose Stochastic Message Passing (SMP) model, a general and simple GNN to maintain both proximity-awareness and permutation-equivariance. In order to preserve node proximities, we augment the existing GNNs with stochastic node representations. We theoretically prove that the mechanism can enable GNNs to preserve node proximities, and at the same time, maintain permutation-equivariance with certain parametrization. We report extensive experimental results on ten datasets and demonstrate the effectiveness and efficiency of SMP for various typical graph mining tasks, including graph reconstruction, node classification, and link prediction.
Clade-wide variation in bite-force performance is determined primarily by size, not ecology

Performance traits are tightly linked to the fitness of organisms. However, because studies of variation in performance traits generally focus on just one or several closely related species, we are unable to draw broader conclusions about how and why these traits vary across clades. One important performance trait related to many aspects of an animal’s life history is bite-force. Here, we use a clade-wide phylogenetic comparative approach to investigate relationships between size, head dimensions and bite-force among lizards and tuatara (lepidosaurs), using the largest bite-force dataset collated to date for any taxonomic group. We test four predictions: that bite-force will be greater in larger species, and for a given body size, bite-force will be greatest in species with acrodont tooth attachment, herbivorous diets, and non-burrowing habits. We show that bite-force is strongly related to body and head size across lepidosaurs and, as predicted, larger species have the greatest bite-forces. Contrary to our other predictions, tooth attachment, diet and habit have little predictive power when accounting for size. Herbivores bite more forcefully simply because they are larger. Our results also highlight priorities for future sampling to further enhance our understanding of broader evolutionary patterns.
Cellular and immunological mechanisms influence host-adapted phenotypes in a vector-borne microparasite

Predicting pathogen emergence and spillover risk requires understanding the determinants of a pathogens' host range and the traits involved in host competence. While host competence is often considered a fixed species-specific trait, it may be variable if pathogens diversify across hosts. Balancing selection can lead to maintenance of pathogen polymorphisms (multiple-niche-polymorphism; MNP). The causative agent of Lyme disease, Borrelia burgdorferi (Bb), provides a model to study the evolution of host adaptation, as some Bb strains defined by their outer surface protein C (ospC) genotype, are widespread in white-footed mice and others are associated with non-rodent vertebrates (e.g. birds). To identify the mechanisms underlying potential strain × host adaptation, we infected American robins and white-footed mice, with three Bb strains of different ospC genotypes. Bb burdens varied by strain in a host-dependent fashion, and strain persistence in hosts largely corresponded to Bb survival at early infection stages and with transmission to larvae (i.e. fitness). Early survival phenotypes are associated with cell adhesion, complement evasion and/or inflammatory and antibody-mediated removal of Bb, suggesting directional selective pressure for host adaptation and the potential role of MNP in maintaining OspC diversity. Our findings will guide future investigations to inform eco-evolutionary models of host adaptation for microparasites.
SIFAT KIMIA TANAH DYSTRUDEPTS DAN PERTUMBUHAN AKAR TANAMAN KELAPA SAWIT (Elaeis guineensis Jacq.) YANG DIAPLIKASI MULSA ORGANIK Mucuna bracteata

One effort to improve chemical properties of Dystrudepts is to use an organic mulch Mucuna bracteata (MB). The study aims to determine the effect of application of organic mulch MB on soil chemical properties and plant root growth of oil palm. The research was conducted in experimental station and the soil laboratory, Faculty of Agriculture, Riau University, Pekanbaru. The research was conducted in September 2014 – February 2015. The research was non factorial experiment that arrangge by Randomized Complete Design (RAL) with treatment control, M 1 (15 kg of organic mulch/plant), M 2 (30 kg organic mulch/plant), and M 3 (45 kg organic mulch/plant). The results showed that application of an organic mulch MB with more increasing dose resulted more increasing of pH value, C-organic, N-total, KTK, KB and root growth. Keywords: organic mulch, Dystrudepts, Mucuna bracteata
Sarcastic or Assertive: How Should Brands Reply to Consumers’ Uncivil Comments on Social Media in the Context of Brand Activism?

Brands are increasingly embracing social activism and adopting positions on controversial issues, prompting some consumers to react by making uncivil comments on social media. How should brands reply to such incivility while maintaining their positions and protecting their reputations? Two common types of reply include either a sarcastic or an assertive tone, but the effects of these types of communication on consumers’ attitudes toward brands remain largely unexplored. Results from a series of five studies exploring different causes (LGBT+ phobia, sexism, and racial equity) show that consumers evaluate brands that reply using an assertive tone more favorably than those using a sarcastic tone, which can be partially explained by the perceived aggressiveness of sarcasm. Additionally, support for a brand's stance acts as a boundary condition on the effect the type of reply adopted by the brand has on consumer attitudes toward the brand. So, the more someone supports a brand's stance, the less their perception of aggressiveness will negatively influence their attitude to that brand. We discuss the implications of these findings for marketing theory and practice.
The Unanticipated Dynamics of Promoting Crowdfunding Donation Campaigns on Social Media

Crowdfunding is an online method of fundraising from a large audience. Digital Word of Mouth (DWOM) via social media has become a popular promotion platform for crowdfunding campaigns due to its negligible nominal cost. While one may expect that promoting these campaigns on social media may steadily increase donations, the exact dynamics of such promotions have not been studied for donation-based crowdfunding. We collect panel data on several unique donation campaigns from a major donation-based crowdfunding website (gofundme.com) and analyze them employing a variety of econometric techniques. We specifically provide empirical evidence that promoting crowdfunded charitable campaigns using social media follows three phases throughout a campaign's lifecycle. Our results indicate that the general pattern of behavior is the same for charitable campaigns as it is for reward-based campaigns. This suggests that the psychological motives outlined in the literature are important for both types of campaigns. Because the economic motives are not present, this finding would not be clearly anticipated. We show that the contributing role of social media in a campaign's success varies over time and that it is most helpful in the first ten days of initiating a campaign. We also provide preliminary evidence that promoting charitable campaigns on social media can lead to slacktivism, an unexpected consequence of using social media as a promotion tool resulting in less donations and more social media flurry. We also find that if a campaign does not reach at least 70 percent of its goal after twenty days since launch, it is not likely to be successful. Fundraisers and charitable marketers can use our findings in gauging the effectiveness of raising awareness about their campaigns in the online world. They could also streamline the timing of social media promotions to enhance their impact on collecting donations for charitable causes.
Sponsorship Disclosure of Influencers – A Curse or a Blessing?

Influencer marketing has become increasingly important in the field of marketing communication as an effective way to reach the appropriate target group. Using their own social media channels, influencers often give the impression that they have a personal rather than a commercial relationship with the brand and the products they promote. Therefore, when influencers post sponsored content, consumers often experience difficulty making accurate distinctions as to whether the influencer is offering a personal recommendation or doing a promotion. Given this issue, we examine to what extent sponsored posts of different influencer types affect consumers’ evaluations of the sponsoring brand and the influencer as well as what role the sponsorship disclosure plays therein. Across four experimental studies, we demonstrate that sponsored posts of mega influencers increase consumers’ persuasion knowledge relative to the posts of nano influencers, which decreases the trustworthiness of those posts and in turn negatively impact both brand and influencer evaluations. Interestingly, our results reveal that this indirect effect is only present when the sponsorship is not disclosed. Thus, more transparency by disclosing such sponsorship can eliminate the negative impacts of persuasion knowledge on the trustworthiness of posts and subsequent evaluations of the brand and the influencer. These findings have important implications for marketing practice and research.