AFS: An Attention-based mechanism for Supervised Feature Selection

As an effective data preprocessing step, feature selection has shown its effectiveness to prepare high-dimensional data for many machine learning tasks. The proliferation of high di-mension and huge volume big data, however, has brought major challenges, e.g. computation complexity and stability on noisy data, upon existing feature-selection techniques. This paper introduces a novel neural network-based feature selection architecture, dubbed Attention-based Feature Selec-tion (AFS). AFS consists of two detachable modules: an at-tention module for feature weight generation and a learning module for the problem modeling. The attention module for-mulates correlation problem among features and supervision target into a binary classification problem, supported by a shallow attention net for each feature. Feature weights are generated based on the distribution of respective feature se-lection patterns adjusted by backpropagation during the train-ing process. The detachable structure allows existing off-the-shelf models to be directly reused, which allows for much less training time, demands for the training data and requirements for expertise. A hybrid initialization method is also intro-duced to boost the selection accuracy for datasets without enough samples for feature weight generation. Experimental results show that AFS achieves the best accuracy and stability in comparison to several state-of-art feature selection algo-rithms upon both MNIST, noisy MNIST and several datasets with small samples.Comment: 9 pages, 5 figures, published in the AAAI 201

MELA: Multilingual Evaluation of Linguistic Acceptability

Recent benchmarks for Large Language Models (LLMs) have mostly focused on application-driven tasks such as complex reasoning and code generation, and this has led to a scarcity in purely linguistic evaluation of LLMs. Against this background, we introduce Multilingual Evaluation of Linguistic Acceptability -- MELA, the first multilingual benchmark on linguistic acceptability with 48K samples covering 10 languages from a diverse set of language families. We establish baselines of commonly used LLMs along with supervised models, and conduct cross-lingual transfer and multi-task learning experiments with XLM-R. In pursuit of multilingual interpretability, we analyze the weights of fine-tuned XLM-R to explore the possibility of identifying transfer difficulty between languages. Our results show that ChatGPT benefits much from in-context examples but still lags behind fine-tuned XLM-R, while the performance of GPT-4 is on par with fine-tuned XLM-R even in zero-shot setting. Cross-lingual and multi-task learning experiments show that unlike semantic tasks, in-language training data is crucial in acceptability judgements. Results in layerwise probing indicate that the upper layers of XLM-R become a task-specific but language-agnostic region for multilingual acceptability judgment. We also introduce the concept of conflicting weight, which could be a potential indicator for the difficulty of cross-lingual transfer between languages. Our data will be available at https://github.com/sjtu-compling/MELA.Comment: Work in progres

ArguGPT: evaluating, understanding and identifying argumentative essays generated by GPT models

AI generated content (AIGC) presents considerable challenge to educators around the world. Instructors need to be able to detect such text generated by large language models, either with the naked eye or with the help of some tools. There is also growing need to understand the lexical, syntactic and stylistic features of AIGC. To address these challenges in English language teaching, we first present ArguGPT, a balanced corpus of 4,038 argumentative essays generated by 7 GPT models in response to essay prompts from three sources: (1) in-class or homework exercises, (2) TOEFL and (3) GRE writing tasks. Machine-generated texts are paired with roughly equal number of human-written essays with three score levels matched in essay prompts. We then hire English instructors to distinguish machine essays from human ones. Results show that when first exposed to machine-generated essays, the instructors only have an accuracy of 61% in detecting them. But the number rises to 67% after one round of minimal self-training. Next, we perform linguistic analyses of these essays, which show that machines produce sentences with more complex syntactic structures while human essays tend to be lexically more complex. Finally, we test existing AIGC detectors and build our own detectors using SVMs and RoBERTa. Results suggest that a RoBERTa fine-tuned with the training set of ArguGPT achieves above 90% accuracy in both essay- and sentence-level classification. To the best of our knowledge, this is the first comprehensive analysis of argumentative essays produced by generative large language models. Machine-authored essays in ArguGPT and our models will be made publicly available at https://github.com/huhailinguist/ArguGP

Revisiting Acceptability Judgements

In this work, we revisit linguistic acceptability in the context of large language models. We introduce CoLAC - Corpus of Linguistic Acceptability in Chinese, the first large-scale acceptability dataset for a non-Indo-European language. It is verified by native speakers and is the first acceptability dataset that comes with two sets of labels: a linguist label and a crowd label. Our experiments show that even the largest InstructGPT model performs only at chance level on CoLAC, while ChatGPT's performance (48.30 MCC) is also much below supervised models (59.03 MCC) and human (65.11 MCC). Through cross-lingual transfer experiments and fine-grained linguistic analysis, we provide detailed analysis of the model predictions and demonstrate for the first time that knowledge of linguistic acceptability can be transferred across typologically distinct languages, as well as be traced back to pre-training. Our dataset is publicly available at \url{https://github.com/huhailinguist/CoLAC}

A kinesin-13 family kinesin in Trypanosoma brucei regulates cytokinesis and cytoskeleton morphogenesis by promoting microtubule bundling.

The early branching eukaryote Trypanosoma brucei divides uni-directionally along the longitudinal cell axis from the cell anterior toward the cell posterior, and the cleavage furrow ingresses along the cell division plane between the new and the old flagella of a dividing bi-flagellated cell. Regulation of cytokinesis in T. brucei involves actomyosin-independent machineries and trypanosome-specific signaling pathways, but the molecular mechanisms underlying cell division plane positioning remain poorly understood. Here we report a kinesin-13 family protein, KIN13-5, that functions downstream of FPRC in the cytokinesis regulatory pathway and determines cell division plane placement. KIN13-5 localizes to multiple cytoskeletal structures, interacts with FPRC, and depends on FPRC for localization to the site of cytokinesis initiation. Knockdown of KIN13-5 causes loss of microtubule bundling at both ends of the cell division plane, leading to mis-placement of the cleavage furrow and unequal cytokinesis, and at the posterior cell tip, causing the formation of a blunt posterior. In vitro biochemical assays demonstrate that KIN13-5 bundles microtubules, providing mechanistic insights into the role of KIN13-5 in cytokinesis and posterior morphogenesis. Altogether, KIN13-5 promotes microtubule bundle formation to ensure cleavage furrow placement and to maintain posterior cytoskeleton morphology in T. brucei

The cooperative roles of two kinetoplastid-specific kinesins in cytokinesis and in maintaining cell morphology in bloodstream trypanosomes.

The cytoskeleton of Trypanosoma brucei, a unicellular eukaryote and a parasitic protozoan, is defined by the subpellicular microtubule corset that is arranged underneath the plasma membrane. We recently identified two orphan kinesins, TbKIN-C and TbKIN-D, that cooperate to regulate the organization of the subpellicular microtubule corset and thereby maintain cell morphology in the procyclic form of T. brucei. In this report, we characterize the function of TbKIN-C and TbKIN-D in the bloodstream form of T. brucei and investigate their functional cooperation in both the bloodstream and procyclic forms. TbKIN-C and TbKIN-D form a tight complex in vivo in the bloodstream form. TbKIN-C is strongly enriched at the posterior tip of the cell, whereas TbKIN-D is distributed throughout the cell body at all cell cycle stages. RNAi of TbKIN-C or TbKIN-D in the bloodstream form inhibits cell proliferation and leads to cell death, due to cytokinesis defects. RNAi of TbKIN-C and TbKIN-D also results in defects in basal body segregation, but does not affect the synthesis and segregation of the flagellum and the flagellum attachment zone (FAZ) filament. Knockdown of TbKIN-C and TbKIN-D does not disrupt the organization of the subpellicular microtubule corset, but produces multinucleated cells with an enlarged flagellar pocket and misplaced flagella. Interestingly, depletion of TbKIN-C results in rapid degradation of TbKIN-D and, similarly, knockdown of TbKIN-C destabilizes TbKIN-D, suggesting that formation of TbKIN-C/TbKIN-D complex stabilizes both kinesins and is required for the two kinesins to execute their essential cellular functions. Altogether, our results demonstrate the essential role of the two kinesins in cell morphogenesis and cytokinesis in the bloodstream form and the requirement of heteromeric complex formation for maintaining the stability of the two kinesins

CRL4^WDR1 Controls Polo-like Kinase Protein Abundance to Promote Bilobe Duplication, Basal Body Segregation and Flagellum Attachment in <i>Trypanosoma brucei</i>

<div><p>The Polo-like kinase homolog in <i>Trypanosoma brucei</i>, TbPLK, plays essential roles in basal body segregation, flagellum attachment and cytokinesis. The level of TbPLK protein is tightly controlled, but the underlying mechanism remains elusive. Here, we report a Cullin-RING ubiquitin ligase composed of Cullin4, the DNA damage-binding protein 1 homolog TbDDB1 and a WD40-repeat protein WDR1 that controls TbPLK abundance in the basal body and the bilobe. WDR1, through its C-terminal domain, interacts with the PEST motif in TbPLK and, through its N-terminal WD40 motif, binds to TbDDB1. Depletion of WDR1 inhibits bilobe duplication and basal body segregation, disrupts the assembly of the new flagellum attachment zone filament and detaches the new flagellum. Consistent with its role in TbPLK degradation, depletion of WDR1 causes excessive accumulation of TbPLK in the basal body and the bilobe, leading to continuous phosphorylation of TbCentrin2 in the bilobe at late cell cycle stages. Together, these results identify a novel WD40-repeat protein as a TbPLK receptor in the Cullin4-DDB1 ubiquitin ligase complex for degrading TbPLK in the basal body and the bilobe after the G1/S cell cycle transition, thereby promoting bilobe duplication, basal body separation and flagellum-cell body adhesion.</p></div

WDR1 forms a complex with TbCUL4 and TbDDB1.

<p>(<b>A</b>). Alignment of the putative DWD box in WDR1 with the DWD box of fission yeast and human WD40-repeat proteins that have been confirmed to bind to DDB1. The three highly conserved residues are highlighted in red, and other conserved residues are in green. The consensus sequence of the DWD box is shown at the top of the aligned sequences. Tb, <i>T</i>. <i>brucei</i>; Sp, <i>Schizosaccharomyces pombe</i>; Hs, <i>Homo sapiens</i>. (<b>B</b>). WDR1 interacts with TbCUL4 but not other Cullin proteins, in <i>T</i>. <i>brucei</i>, as demonstrated by co-immunoprecipitation. WDR1-3HA and each of the five PTP-tagged Cullin proteins were co-expressed from their respective endogenous locus in <i>T</i>. <i>brucei</i>. Immunoprecipitation was performed by incubating the cell lysate with IgG beads, and immunoprecipitated proteins were then immunoblotted with anti-HA antibody and anti-Protein A (α-ProtA) antibody, respectively. (<b>C</b>). WDR1 interacts with TbDDB1 <i>in vivo</i> in <i>T</i>. <i>brucei</i>, as demonstrated by co-immunoprecipitation. WDR1-3HA and TbDDB1-PTP were co-expressed in <i>T</i>. <i>brucei</i>, and immunoprecipitation and Western blotting were performed as described in panel B. (<b>D</b>). WDR1, TbCUL4, TbDDB1 and TbPLK form a complex in <i>T</i>. <i>brucei</i>, as demonstrated by co-immunoprecipitation. WDR1-3HA, TbCUL4-PTP and TbDDB1-3Myc were co-expressed from their respective endogenous locus in <i>T</i>. <i>brucei</i>. Immunoprecipitation of WDR1-3HA was carried out by incubating the cell lysate with EZview Red anti-HA affinity gel, and immunoprecipitated proteins were immunoblotted with anti-HA antibody, anti-Myc antibody, anti-TbPLK antibody and anti-Protein A (α-ProtA) antibody to detect WDR1-3HA, TbDDB1-3Myc, TbPLK and TbCUL4-PTP, respectively. (<b>E</b>). The N-terminal domain of WDR1 mediates the interaction with TbDDB1, as demonstrated by <i>in vitro</i> GST pull-down. The N-terminal fragment (1–300 aa) of WDR1, which contains the WD40 motif, was expressed as a GST-fusion protein in <i>E</i>. <i>coli</i>, purified and used to pull down TbDDB1-3HA from <i>T</i>. <i>brucei</i> cell lysate. Purified GST-WDR1^1-300 and GST were stained by coomassie brilliant blue (CBB).</p