Scalable Auction Algorithms for Bipartite Maximum Matching Problems

In this paper, we give new auction algorithms for maximum weighted bipartite matching (MWM) and maximum cardinality bipartite $b$-matching (MCbM). Our algorithms run in $O\left(\log n/\varepsilon^8\right)$ and $O\left(\log n/\varepsilon^2\right)$ rounds, respectively, in the blackboard distributed setting. We show that our MWM algorithm can be implemented in the distributed, interactive setting using $O(\log^2 n)$ and $O(\log n)$ bit messages, respectively, directly answering the open question posed by Demange, Gale and Sotomayor [DNO14]. Furthermore, we implement our algorithms in a variety of other models including the the semi-streaming model, the shared-memory work-depth model, and the massively parallel computation model. Our semi-streaming MWM algorithm uses $O(1/\varepsilon^8)$ passes in $O(n \log n \cdot \log(1/\varepsilon))$ space and our MCbM algorithm runs in $O(1/\varepsilon^2)$ passes using $O\left(\left(\sum_{i \in L} b_i + |R|\right)\log(1/\varepsilon)\right)$ space (where parameters $b_i$ represent the degree constraints on the $b$-matching and $L$ and $R$ represent the left and right side of the bipartite graph, respectively). Both of these algorithms improves \emph{exponentially} the dependence on $\varepsilon$ in the space complexity in the semi-streaming model against the best-known algorithms for these problems, in addition to improvements in round complexity for MCbM. Finally, our algorithms eliminate the large polylogarithmic dependence on $n$ in depth and number of rounds in the work-depth and massively parallel computation models, respectively, improving on previous results which have large polylogarithmic dependence on $n$ (and exponential dependence on $\varepsilon$ in the MPC model).Comment: To appear in APPROX 202

Class-Incremental Learning based on Label Generation

Despite the great success of pre-trained language models, it is still a challenge to use these models for continual learning, especially for the class-incremental learning (CIL) setting due to catastrophic forgetting (CF). This paper reports our finding that if we formulate CIL as a continual label generation problem, CF is drastically reduced and the generalizable representations of pre-trained models can be better retained. We thus propose a new CIL method (VAG) that also leverages the sparsity of vocabulary to focus the generation and creates pseudo-replay samples by using label semantics. Experimental results show that VAG outperforms baselines by a large margin.Comment: 12 pages, ACL 2023 Main Conferenc

Cretaceous to Cenozoic Tectonics of North America: From Intraplate Magmatism to Intracontinental Rifting

The tectonic mechanism driving Cretaceous magmatism in the Gulf of Mexico (GoM) region is debated. This magmatism postdates GoM seafloor spreading by 40 Myr, initiated at 108 Ma and lasted through the Cretaceous. Spanning Texas to Mississippi it consists of igneous rocks with geochemical signatures pointing to a sub-lithospheric mantle origin. Hypotheses for this magmatism include: (1) the Bermuda hotspot; (2) edge-driven convection; (3) lithospheric reactivation; and (4) deep, low-angle subduction. My research shows none are fully satisfactory and that GoM magmatism should be correlated to other Cretaceous – Eocene kimberlites and lamproites from Arkansas to the Northwest Territories. They are located 1000+ km inboard from, and aligned sub-parallel to, the western margin of North America (NA). I propose that the Farallon slabs stagnated in the mantle transition zone in the Early Cretaceous, and generated sporadic, dense, low-degree partial melts by dehydration and decarbonation. As the slabs penetrated the lower mantle, instabilities at slab edges caused upwelling that brought alkali-rich carbonatitic melts to the base of the lithosphere. Subsequently, the NA lithosphere with varying thickness, discontinuities, and compositions interacted with the rising melt, producing a variety of magmatic rocks. This model connects intraplate magmatism with slab stagnation, and provides a critical constraint on the Cretaceous NA history. Following the Laramide orogeny, Cenozoic extension dominated the western US, forming the Rio Grande rift (RGR). Kinematics of the extension is critical to evaluate tectonic models. While the N-trending, right-stepping RGR largely shows orthogonal E-W extension, the NW-striking, oblique Tusas segment preserves W- and SW-trending slip directions. While a multi-directional extension model is possible, a continuous E-W extension with reactivation of pre-existing weakness can alternatively explain slip direction variations. Initial extension on reactivated faults was recorded by W-trending slickenlines. Subsequently, extension was re-oriented to SW-trending, i.e., pure dip-slip, due to local stress rotation across heterogeneities. Then, the Embudo accommodation zone began to accommodate E-W extension, causing diffuse, SSW-directed slip on the Tusas segment. The early extension along the Tusas segment was abandoned once the Embudo transfer fault formed. This study highlights the significance of obliquity and inherited heterogeneity in the kinematic evolution of rifts.Earth and Atmospheric Sciences, Department o

Class Incremental Learning via Likelihood Ratio Based Task Prediction

Class incremental learning (CIL) is a challenging setting of continual learning, which learns a series of tasks sequentially. Each task consists of a set of unique classes. The key feature of CIL is that no task identifier (or task-id) is provided at test time. Predicting the task-id for each test sample is a challenging problem. An emerging theory-guided approach (called TIL+OOD) is to train a task-specific model for each task in a shared network for all tasks based on a task-incremental learning (TIL) method to deal with catastrophic forgetting. The model for each task is an out-of-distribution (OOD) detector rather than a conventional classifier. The OOD detector can perform both within-task (in-distribution (IND)) class prediction and OOD detection. The OOD detection capability is the key to task-id prediction during inference. However, this paper argues that using a traditional OOD detector for task-id prediction is sub-optimal because additional information (e.g., the replay data and the learned tasks) available in CIL can be exploited to design a better and principled method for task-id prediction. We call the new method TPL (Task-id Prediction based on Likelihood Ratio). TPL markedly outperforms strong CIL baselines and has negligible catastrophic forgetting. The code of TPL is publicly available at https://github.com/linhaowei1/TPL

Slogans, Brands and Purchase Behaviour of Students

Purpose: The aim of this paper is to extend the understanding of the influence of slogans (e.g. “Dare for More”) on brand awareness and purchase behaviour of students. Design/methodology/approach: Data were collected thorough 34 in-depth face-to-face interviews with university students, using the Customer Decision Process (CDP) model as an approach. Findings: Our research confirmed that conciseness, rhythm and jingle are key features strengthening customers’ recall and recognition, both being moderators of slogans’ power. The role and influence of slogans depend on the stage of the customer decision making process. Key influencers remain product quality, popularity and price, but appropriate and memorable slogans enhance products’ differentiation and sale. Practical implications: Our findings deliver a particular justification for marketers not to promise young consumers too much through slogans, as this leads to too high expectations adversely influencing their post-purchase feelings. During the Information Search, slogans can create or strengthen or weaken the willingness to buy the advertised product, depending on the slogan, thus emphasising the need for care over slogan design and use. Originality/value: This research expands the understanding of slogans and brand awareness from the perspective of their impact on purchase behaviour. Our results revealed that the model approach to shopping behaviour does not confirm the belief that slogans influence consumers the most during the phase of Evaluation of Alternatives. Slogans provide a reference point for young consumers to decide whether they are satisfied or dissatisfied with their purchase during the Post Purchase phase and provide information during the Information Search phase. Our results add to the literature in terms of the criteria determining consumers’ recognition and recall of slogans

A Novel STAP Algorithm for Airborne MIMO Radar Based on Temporally Correlated Multiple Sparse Bayesian Learning

In a heterogeneous environment, to efficiently suppress clutter with only one snapshot, a novel STAP algorithm for multiple-input multiple-output (MIMO) radar based on sparse representation, referred to as MIMOSR-STAP in this paper, is presented. By exploiting the waveform diversity of MIMO radar, each snapshot at the tested range cell can be transformed into multisnapshots for the phased array radar, which can estimate the high-resolution space-time spectrum by using multiple measurement vectors (MMV) technique. The proposed approach is effective in estimating the spectrum by utilizing Temporally Correlated Multiple Sparse Bayesian Learning (TMSBL). In the sequel, the clutter covariance matrix (CCM) and the corresponding adaptive weight vector can be efficiently obtained. MIMOSR-STAP enjoys high accuracy and robustness so that it can achieve better performance of output signal-to-clutter-plus-noise ratio (SCNR) and minimum detectable velocity (MDV) than the single measurement vector sparse representation methods in the literature. Thus, MIMOSR-STAP can deal with badly inhomogeneous clutter scenario more effectively, especially suitable for insufficient independent and identically distributed (IID) samples environment

The relationship between atrial fibrillation and NLRP3 inflammasome: a gut microbiota perspective

Atrial fibrillation (AF) is a common clinical arrhythmia whose pathogenesis has not been fully elucidated, and the inflammatory response plays an important role in the development of AF. The inflammasome is an important component of innate immunity and is involved in a variety of pathophysiologic processes. The NLRP3 inflammasome is by far the best studied and validated inflammasome that recognizes multiple pathogens through pattern recognition receptors of innate immunity and mediates inflammatory responses through activation of Caspase-1. Several studies have shown that NLRP3 inflammasome activation contributes to the onset and development of AF. Ecological dysregulation of the gut microbiota has been associated with the development of AF, and some evidence suggests that gut microbiota components, functional byproducts, or metabolites may induce or exacerbate the development of AF by directly or indirectly modulating the NLRP3 inflammasome. In this review, we report on the interconnection of NLRP3 inflammasomes and gut microbiota and whether this association is related to the onset and persistence of AF. We discuss the potential value of pharmacological and dietary induction in the management of AF in the context of the association between the NLRP3 inflammasome and gut microbiota. It is hoped that this review will lead to new therapeutic targets for the future management of AF