Miracle-dz/UniFAR-FTUs huggingface dataset:
Getting an h-Index of 100 in 20 Years or Less!

The title of this tutorial is clickbait! However, a high h-index (relative to the stage of your career), will make you advisor, chair, dean and chancellor happy, and it can help you get a job or get promoted. Moreover, I will show that h-indices are highly susceptible to the Matthew Effect, a high h-index may help you get funding and attract strong collaborators/students, which in turn will likely further increase your h-index! Thus, "kickstarting" your h-index early in your career can pay huge dividends. It goes without saying that optimizing h-index is not the same as optimizing your scientific impact. Note that John Clauser, the 2022 Nobel prize winner in physics has an h-index of just 29. However, a high h-index does mean that you have been prolific at publishing papers, and it also means that the community does read and cite these papers. In this tutorial, I show that beyond pure quality of your research, there are many "tricks" you can do to increase both your paper's chance of acceptance, and increase its number of citations, thus optimizing your h-index. Please note that there are ways to increase your h-index that are clearly disingenuous and bad for the community (citation cartels, ghost authorships etc.). However, I believe that all the ideas presented in this tutorial are legal, moral, and are generally helpful for the entire community.
Learning Autoregressive Model in LSM-Tree based Store

Database-native machine learning operators are highly desired for efficient I/O and computation costs. While most existing machine learning algorithms assume the time series data fully available and readily ordered by timestamps, it is not the case in practice. Commodity time series databases store the data in pages with possibly overlapping time ranges, known as LSM-Tree based storage. Data points in a page could be incomplete, owing to either missing values or out-of-order arrivals, which may be inserted by the imputed or delayed points in the following pages. Likewise, data points in a page could also be updated by others in another page, for dirty data repairing or re-transmission. A straightforward idea is thus to first merge and order the data points by timestamps, and then apply the existing learning algorithms. It is not only costly in I/O but also prevents pre-computation of model learning. In this paper, we propose to offline learn the AR models locally in each page on incomplete data, and online aggregate the stored models in different pages with the consideration of the aforesaid inserted and updated data points. Remarkably, the proposed method has been deployed and included as a function in an open source time series database, Apache IoTDB. Extensive experiments in the system demonstrate that our proposal LSMAR shows up to one order-of-magnitude improvement in learning time cost. It needs only about 10s of milliseconds for learning over 1 million data points.
SLED: Structure Learning based Denoising for Recommendation

In recommender systems, click behaviors play a fundamental role in mining users’ interests and training models (clicked items as positive samples). Such signals are implicit feedback and are arguably less representative of users’ inherent interests. Most existing works denoise implicit feedback by introducing external signals, such as gaze, dwell time, and “like” behaviors. However, such explicit feedback is not always routinely available, or might be problematic to collect on a large scale. In this paper, we identify that an interaction’s related structural patterns in its neighborhood graph are potentially correlated with some outcome of implicit feedback (i.e., users’ ratings after consuming items), analogous to findings in other domains such as social networks. Inspired by this finding, we propose a novel Structure LEarning based Denoising (SLED) framework for denoising recommendation without explicit signals, which consists of two phases: center-aware graph structure learning and denoised recommendation. Phase 1 pre-trains a structural encoder in a self-supervised manner and learns to capture an interaction’s related structural patterns in its neighborhood graph. Phase 2 transfers the structure encoder to downstream recommendation datasets, which helps to down-weight the effect of noisy interactions on user interest modeling and loss calculation. We collect a relatively noisy industrial dataset across several days during a period of product promotion festival. Extensive experiments on this dataset and multiple public datasets demonstrate that the proposed SLED framework can significantly improve the recommendation quality over various base recommendation models.
Rewiring Police Officer Training Networks to Reduce Forecasted Use of Force

Research has shown that police officer involved shootings, misconduct and excessive use of force complaints exhibit network effects, where officers are at greater risk of being involved in these incidents when they socialize with officers who have a history of use of force and misconduct. In this work, we first construct a network survival model for the time-to-event of use of force incidents involving new police trainees. The model includes network effects of the diffusion of risk from field training officer (FTO) to trainee. We then introduce a network rewiring algorithm to maximize the expected time to use of force events upon completion of field training. We study several versions of the algorithm, including constraints that encourage demographic diversity of FTOs. Using data from Indianapolis, we show that rewiring the network can increase the expected time (in days) of a recruit's first use of force incident by 8%. We then discuss the potential benefits and challenges associated with implementing such an algorithm in practice.
User-Regulation Deconfounded Conversational Recommender System with Bandit Feedback

Recent conversational recommender systems (CRSs) have achieved considerable success on addressing the cold-start problem. While they utilize conversational key-terms to efficiently elicit user preferences, most of them, however, neglect that key-terms can also introduce biases. Systems learning key-term-level user preferences may make a biased item recommendation based on an overrated key-term instead of the item itself. As key-term conversation is a crucial part of CRSs, it is important to properly handle such bias resulting from the item-key-term relationship. While many debiasing methods have been proposed for traditional recommender systems, most of them focus on items or item groups re-ranking or re-weighting strategies such as calibration and propensity score, which are not designed to model the relation between item and key-term user preference. There is also no effective way for traditional debiasing methods to measure potentially useful biases through conversational key-terms to enhance the recommendation performance. In this paper, we develop a deconfounded CRS, which enables the user to provide both item and key-term feedback in each round such that we can promisingly capture more accurate relation between key-term-level and item-level user preference to alleviate the bias. To better model the relations and understand such bias, we view CRSs from a causal perspective and introduce a novel structural causal model (SCM) that identifies the confounding effect of key-term-level user preference. Inspired by our causal view, we devise an online backdoor adjustment approximation to alleviate the confounding effect when making item recommendations. Consider that not all biases are harmful, we utilize the useful bias and propose DecUCB, which leverages conversational key-term feedback to regulate the influence of backdoor adjustment adaptively in a personalized fashion. Extensive experiments on real-world datasets demonstrate the advantages of our proposed method in both recommendation performance and bias mitigation.
Epigenetic regulation of Leukocyte associated immunoglobulin-like receptors 1 and 2 by interferon signaling in macrophages and T cells

Background Inhibitory immune receptors are important for maintaining immune homeostasis. We recently identified epigenetic alterations in two members of this group, LAIR1 and LAIR2, in patients with inflammatory tissue damage and recurrent skin and soft tissue infections. We therefore hypothesized that the expression of LAIR1 and LAIR2 may be controlled by immune stimuli acting on discrete transcriptional regulatory elements. Methods We used flow cytometry, qRT-PCR, and RNAseq to assay LAIR1 and LAIR2 expression in human and murine immune cell subsets at baseline and post-treatment with immune mediators, including type I and II interferons, tumor necrosis factor-alpha (TNF-ɑ), and lipopolysaccharide (LPS). Using chromatin immunoprecipitation sequencing (ChIP-seq), we identified candidate transcriptional regulatory elements of LAIR genes and evaluated their regulatory activity using luciferase reporters. Results Both human and murine macrophages significantly upregulate LAIR1 expression as they differentiate from monocytes to macrophages. In response to interferons, LAIR1 protein levels increase, while LPS causes a relative reduction. Regulatory elements flanking LAIR genes exhibit distinct patterns of enhancer activity with variable responses to immune stimuli. These responses are related to discrete sets of transcription factors in inflammatory pathways that correlate with cell-specific LAIR expression patterns. In addition, we identified LAIR1 and LAIR2 regulatory elements that act as foci of 3D genome interactions with other highly active regulatory elements. Conclusions Our findings define the complex regulatory landscapes of human and mouse LAIR genes and reveal new insights into the transcriptional regulatory mechanisms that control the expression of these important immune modulatory proteins.
Urban Region Representation Learning with OpenStreetMap Building Footprints

The prosperity of crowdsourcing geospatial data provides increasing opportunities to understand our cities. In particular, OpenStreetMap (OSM) has become a prominent vault of geospatial data on the Web. In this context, learning urban region representations from OSM data, which is unexplored in previous work, could be profitable for various downstream tasks. In this work, we utilize OSM buildings (footprints) complemented with points of interest (POIs) to learn region representations, as buildings' shapes, spatial distributions, and properties have tight linkages to different urban functions. However, appealing as it seems, urban buildings often exhibit complex patterns to form dense or sparse areas, which brings significant challenges for unsupervised feature extraction. To address the challenges, we propose RegionDCL1, an unsupervised framework to deeply mine urban buildings. In a nutshell, we leverage random points generated by Poisson Disk Sampling to tackle data-sparse areas and utilize triplet loss with a novel adaptive margin to preserve inter-region correlations. Furthermore, we train our model with group-level and region-level contrastive learning, making it adaptive to varying region partitions. Extensive experiments in two global cities demonstrate that RegionDCL consistently outperforms the state-of-the-art counterparts across different region partitions, and outputs effective representations for inferring urban land use and population density.
ProjectSVR: Mapping single-cell RNA-seq data to reference atlases by supported vector regression

The rapid accumulation of public single-cell RNA-seq (scRNA-seq) data and advances in integration algorithms make it possible to build the organ and body-scale cell atlases after overcoming the batch effect originating from different studies, donors, and sequence platforms. However, projecting the query cells onto the carefully constructed reference atlas to rapidly interpret cell states remains challenging. Here, we present ProjectSVR (https://github.com/jarninggau/ProjectSVR), a machine learning-based algorithm for mapping the query cells onto well-constructed reference embeddings. We demonstrate the effectiveness of ProjectSVR in multiple case studies of different tissue and experimental design, including (1) interpreting the decidual immune microenvironment from recurrent pregnancy loss (RPL) patients, (2) resolving the tumor-infiltrated T cell states from patients receiving anti-PD1 treatment, (3) mapping the genetic-perturbed mouse germ cells to integrated mouse testicular cell atlas (mTCA) for inferring their arrested developmental stage, and (4) evaluating in vitro induced meiosis by via mapping the induced germ cells onto mTCA. Taken together, our results show that ProjectSVR is a useful tool for reference mapping and make the scRNA-seq data analysis more convenient when well-annotated atlases emerge.
Efficient Single-Source SimRank Query by Path Aggregation

Single-source SimRank query calculates the similarity between a query node and every node in a graph, which traverses the paths starting from the query node for similarity computation. However, the scale of the paths increases exponentially as path length increases, which decreases the computation efficiency. Sampling-based algorithms reduce computational cost by path sampling, but they need to sample sufficient paths to ensure the accuracy, and the performance might be affected by the large scale of paths. In this paper, we propose VecSim for efficient single-source SimRank query by path aggregation. VecSim first aggregates the paths starting from query node with common arrived nodes step by step to obtain the hitting probabilities, and then aggregates the paths starting from the arrived nodes reversely to obtain the first-meeting probabilities in a similar way, in which only several vectors are maintained. The extra-meeting probabilities are excluded from each step, and an efficient sampling-based algorithm is designed, which estimates the extra-meeting probabilities by sampling paths within a specified length. For further speeding up query processing, we propose a threshold-sieved algorithm, which prunes the entries with small values that contribute little to the final similarity scores by setting a threshold. Extensive experiments are done on four small and four large graphs, which demonstrate that VecSim outperforms the competitors in terms of time and space costs on a comparable accuracy. In particular, VecSim achieves an empirical error of 10-4 level in under 0.1 second over all of these graphs.
Constrained Social Community Recommendation

In online social networks, users with similar interests tend to come together, forming social communities. Nowadays, user-defined communities become a prominent part of online social platforms as people who have joined such communities tend to be more active in social networks. Therefore, recommending explicit communities to users provides great potential to advance online services. In this paper, we focus on the constrained social community recommendation problem in real applications, where each user can only join at most one community. Previous attempts at community recommendation mostly adopt collaborative filtering approaches or random walk-based approaches, while ignoring social relationships between users as well as the local structure of each community. Therefore, they only derive an extremely sparse affinity matrix, which degrades the model performances. To tackle this issue, we propose ComRec which simultaneously captures both global and local information on the extended graph during pre-computation, speeding up the training process on real-world large graphs. In addition, we present a labeling component to improve the expressiveness of our framework. We conduct experiments on three Tencent mobile games to evaluate our proposed method. Extensive experimental results show that our ComRec consistently outperforms other competitors by up to 12.80% and 6.61% in the corresponding evaluation metrics of offline and online experiments, respectively.
CFGL-LCR: A Counterfactual Graph Learning Framework for Legal Case Retrieval

Legal case retrieval, which aims to find relevant cases based on a short case description, serves as an important part of modern legal systems. Despite the success of existing retrieval methods based on Pretrained Language Models, there are still two issues in legal case retrieval that have not been well considered before. First, existing methods underestimate the semantics associations among legal elements, e.g., law articles and crimes, which played an essential role in legal case retrieval. These methods only adopt the pre-training language model to encode the whole legal case, instead of distinguishing different legal elements in the legal case. They randomly split a legal case into different segments, which may break the completeness of each legal element. Second, due to the difficulty in annotating the relevant labels of similar cases, legal case retrieval inevitably faces the problem of lacking training data. In this paper, we propose a counterfactual graph learning framework for legal case retrieval. Concretely, to overcome the above challenges, we transform the legal case document into a graph and model the semantics of the legal elements through a graph neural network. To alleviate the low resource and learn the causal relationship between the semantics of legal elements and relevance, a counterfactual data generator is designed to augment counterfactual data and enhance legal case representation. Extensive experiments based on two publicly available legal benchmarks demonstrate that our CFGL-LCR can significantly outperform previous state-of-the-art methods in legal case retrieval.
A demand-based framework explains prioritization strategies upon transient limitations of different amino acids

Cells require disparate amounts of distinct amino acids, which themselves have discrete biosynthetic costs. However, it remains unclear if and how cells respond differently to their scarcity. To explore this, we re-organized amino acids into distinct groups based on their metabolic origins. Subsequently, using yeast we assessed responses to transient disruptions in amino acid supply, and uncover diverse restoration responses for distinct amino acids. Cells hierarchically prioritize restoring glutamate-, sulfur-, pentose-phosphate- and pyruvate-derived amino acids. Particularly, the strongest response is to the glutamate-derived amino acid arginine. We find that the extent and priority of the restoration response is determined by the composite demand for an amino acid, coupled with low individual biosynthetic costs of that amino acid. We propose that cells employ a conserved strategy guided by the law of demand, to prioritize amino acids restoration upon transient limitation.
GetPt: Graph-enhanced General Table Pre-training with Alternate Attention Network

Tables are widely used for data storage and presentation due to their high flexibility in layout. The importance of tables as information carriers and the complexity of tabular data understanding attract a great deal of research on large-scale pre-training for tabular data. However, most of the works design models for specific types of tables, such as relational tables and tables with well-structured headers, neglecting tables with complex layouts. In real-world scenarios, there are many such tables beyond their target scope that cannot be well supported. In this paper, we propose GetPt, a unified pre-training architecture for general table representation applicable even to tables with complex structures and layouts. First, we convert a table to a heterogeneous graph with multiple types of edges to represent the layout of the table. Based on the graph, a specially designed transformer is applied to jointly model the semantics and structure of the table. Second, we devise the Alternate Attention Network (AAN) to better model the contextual information across multiple granularities of a table including tokens, cells, and the table. To better support a wide range of downstream tasks, we further employ three pre-training objectives and pre-train the model on a large table dataset. We fine-tune and evaluate GetPt model on two representative tasks, table type classification, and table structure recognition. Experiments show that GetPt outperforms existing state-of-the-art methods on these tasks.
Robust OCR Pipeline for Automated Digitization of Mother and Child Protection Cards in India

The Universal Immunization Programme in India has a mandate to fully vaccinate all of India’s 27 million children born annually. The vaccination doses are recorded by frontline health workers on standardized paper-based Mother and Child Protection (MCP) cards, which are manually digitized by data entry operators, resulting in poor data quality, delays, and significant time and resources. In our article, we focus on Optical Character Recognition– (OCR) based automated digitization of MCP card images captured through a smartphone application developed by us. By utilizing a standardized template for the MCP cards, which is available a priori, we register the card images and perform OCR on the extracted region of interest (ROIs). Since the cards with curvature or torn edges had poor ROIs, we built a global–local alignment technique that first approximates the ROI using global homography and then refines using a local homography resulting in improved accuracy. Our pipeline gives a character level accuracy of 98.73% on our dataset against 75.02% by Google Cloud Vision and 79.26% by Azure OCR. We also describe our field testing experience, where the digitized MCP card images were used to provide useful features on the smartphone application for health workers to conduct vaccination sessions.
Combined, yet Separate: cocktails of carriers (not drugs) for α-particle therapy of solid tumors expressing moderate-to-low levels of targetable markers

Alpha-particle radionuclide-antibody conjugates are being clinically evaluated against solid cancers expressing moderate levels of the targeted markers, with promising results. These findings are attributed to the high killing power of alpha-particles in spite of the expected decrease in antibody tumor uptake, that reduces tumor absorbed doses. However, when tumor absorbed doses are reduced, addressing the heterogeneities in delivery of alpha-particles within solid tumors (i.e. enabling uniform irradiation patterns) becomes critical: to maintain efficacy, the fewer alpha-particles delivered within tumors need to traverse/hit as many different cancer cells as possible. This proof-of-concept study describes an approach to complement the antibody- targeted radiotherapy by using a separate carrier to deliver a fraction of the injected radioactivity to tumor regions geographically different than those affected by the antibody; collectively, the two carriers should distribute the alpha-particle emitters, Actinium-225 in particular, more uniformly within tumors maintaining efficacy. Methods We monitored the extent(s) of tumor growth inhibition, onset delay of spontaneous metastases and/or survival on orthotopic MDA-MB-213 and MDA-MB-436 triple negative breast cancer mouse models and on an ectopic BxPC3 pancreatic cancer mouse model, treated systemically with the two separate carriers. Tumors were chosen to express different (but low) levels of HER1, utilized as a model antibody-targeted marker. Results Independent of tumor origin and/or resistance to chemotherapy, the two separate carriers: (a) improved the ‘primary’ tumor growth inhibition, (b) eliminated the formation of spontaneous metastases, and/or (c) prolonged survival, at lower or comparable tumor delivered doses relative to the antibody alone, without noticeable off-target toxicities. Conclusion This tumor-agnostic strategy is timely and could be used to enhance the efficacy of existing alpha-particle radionuclide-antibody treatments without increasing, possibly even reducing, the total administered radioactivity.