Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Augmented Graph Neural Networks for Personality Detection

Personality detection is a fundamental task for user psychology research. One of the biggest challenges in personality detection lies in the quantitative limitation of labeled data collected by completing the personality questionnaire, which is very time-consuming and labor-intensive. Most of the existing works are mainly devoted to learning the rich representations of posts based on labeled data. However, they still suffer from the inherent weakness of the amount limitation of labels, which potentially restricts the capability of the model to deal with unseen data. In this paper, we construct a heterogeneous personality graph for each labeled and unlabeled user and develop a novel psycholinguistic augmented graph neural network to detect personality in a semi-supervised manner, namely Semi-PerGCN. Specifically, our model first explores a supervised Personality Graph Neural Network (PGNN) to refine labeled user representation on the heterogeneous graph. For the remaining massive unlabeled users, we utilize the empirical psychological knowledge of the Linguistic Inquiry and Word Count (LIWC) lexicon for multi-view graph augmentation and perform unsupervised graph consistent constraints on the parameters shared PGNN. During the learning process of finite labeled users, noise-invariant learning on a large scale of unlabeled users is combined to enhance the generalization ability. Extensive experiments on three real-world datasets, Youtube, PAN2015, and MyPersonality demonstrate the effectiveness of our Semi-PerGCN in personality detection, especially in scenarios with limited labeled users.
Formation of a Polycomb-Domain in the Absence of Strong Polycomb Response Elements

Polycomb group response elements (PREs) in Drosophila are DNA-elements that recruit Polycomb proteins (PcG) to chromatin and regulate gene expression. PREs are easily recognizable in the Drosophila genome as strong peaks of PcG-protein binding over discrete DNA fragments; many small but statistically significant PcG peaks are also observed in PcG domains. Surprisingly, in vivo deletion of the four characterized strong PREs from the PcG regulated invected-engrailed (inv-en) gene complex did not disrupt the formation of the H3K27me3 domain and did not affect inv-en expression in embryos or larvae suggesting the presence of redundant PcG recruitment mechanism. Further, the 3D-structure of the inv-en domain was only minimally altered by the deletion of the strong PREs. A reporter construct containing a 7.5kb en fragment that contains three weak peaks but no large PcG peaks forms an H3K27me3 domain and is PcG-regulated. Our data suggests a model for the recruitment of PcG-complexes to Drosophila genes via interactions with multiple, weak PREs spread throughout an H3K27me3 domain.
Rethinking Peculiar Images by Diffusion Models: Revealing Local Minima's Role

Recent significant advancements in diffusion models have revolutionized image generation, enabling the synthesis of highly realistic images with text-based guidance. These breakthroughs have paved the way for constructing datasets via generative artificial intelligence (AI), offering immense potential for various applications. However, two critical challenges hinder the widespread adoption of synthesized data: computational cost and the generation of peculiar images. While computational costs have improved through various approaches, the issue of peculiar image generation remains relatively unexplored. Existing solutions rely on heuristics, extra training, or AI-based post-processing to mitigate this problem. In this paper, we present a novel approach to address both issues simultaneously. We establish that both gradient descent and diffusion sampling are specific cases of the generalized expectation maximization algorithm. We hypothesize and empirically demonstrate that peculiar image generation is akin to the local minima problem in optimization. Inspired by optimization techniques, we apply naive momentum and positive-negative momentum to diffusion sampling. Last, we propose new metrics to evaluate the peculiarity. Experimental results show momentum effectively prevents peculiar image generation without extra computation.
FedCD: Federated Semi-Supervised Learning with Class Awareness Balance via Dual Teachers

Recent advancements in deep learning have greatly improved the efficiency of auxiliary medical diagnostics. However, concerns over patient privacy and data annotation costs restrict the viability of centralized training models. In response, federated semi-supervised learning has garnered substantial attention from medical institutions. However, it faces challenges arising from knowledge discrepancies among local clients and class imbalance in non-independent and identically distributed data. Existing methods like class balance adaptation for addressing class imbalance often overlook low-confidence yet valuable rare samples in unlabeled data and may compromise client privacy. To address these issues, we propose a novel framework with class awareness balance and dual teacher distillation called FedCD. FedCD introduces a global-local framework to balance and purify global and local knowledge. Additionally, we introduce a novel class awareness balance module to effectively explore potential rare classes and encourage balanced learning in unlabeled clients. Importantly, our approach prioritizes privacy protection by only exchanging network parameters during communication. Experimental results on two medical datasets under various settings demonstrate the effectiveness of FedCD. The code is available at https://github.com/YunzZ-Liu/FedCD.
IINet: Implicit Intra-inter Information Fusion for Real-Time Stereo Matching

Recently, there has been a growing interest in 3D CNN-based stereo matching methods due to their remarkable accuracy. However, the high complexity of 3D convolution makes it challenging to strike a balance between accuracy and speed. Notably, explicit 3D volumes contain considerable redundancy. In this study, we delve into more compact 2D implicit network to eliminate redundancy and boost real-time performance. However, simply replacing explicit 3D networks with 2D implicit networks causes issues that can lead to performance degradation, including the loss of structural information, the quality decline of inter-image information, as well as the inaccurate regression caused by low-level features. To address these issues, we first integrate intra-image information to fuse with inter-image information, facilitating propagation guided by structural cues. Subsequently, we introduce the Fast Multi-scale Score Volume (FMSV) and Confidence Based Filtering (CBF) to efficiently acquire accurate multi-scale, noise-free inter-image information. Furthermore, combined with the Residual Context-aware Upsampler (RCU), our Intra-Inter Fusing network is meticulously designed to enhance information transmission on both feature-level and disparity-level, thereby enabling accurate and robust regression. Experimental results affirm the superiority of our network in terms of both speed and accuracy compared to all other fast methods.
Strategic sourcing and collaborative planning in Internet-enabled supply chain networks producing multigeneration products

The design and planning of supply chain networks supporting production and distribution of multiple product generations overlapping with each other is of critical importance in the high-tech industry. We address the strategic supply chain network design problem in rapidly changing industry segments, where the selection of partners such as component suppliers, contract manufacturers and logistics providers is done, based on the capabilities of the partners for supporting the strategic needs of the current and near future generations of a finished product. We develop a mixed integer-programming model for integrated planning and scheduling across the supply chain and show how such a model may be used for making decisions related to introduction and rollovers of finished products and components from one generation to another. We assume that all stakeholders in the supply chain collaborate and share information on their capacities, schedules and cost structures. Based on this information the model addresses the issue of partner selection and planning for optimal profit. The model was solved using optimization tools from ILOG. Managerial insights are obtained by performing a series of simulated experiments on the model developed. For example, we show that an expensive supplier possessing the ability to develop and supply components required across a number of generations, might be preferred against a cheaper supplier supplying components suitable for a specific generation of the product. In addition, we show how the supply chain network configuration changes over the lifecycle of the product, wherein cheaper overseas suppliers slowly replace responsive and expensive local suppliers as the product matures. We also develop a framework to quantify and compare the costs and benefits of pursuing alternative product introduction plans and deadlines. Such a framework might be employed to determine the optimal product introduction schedule. We show here that in some cases it might not be profitable to launch a product in the market after a certain period of time.
Causal Representation Learning via Counterfactual Intervention

Existing causal representation learning methods are based on the causal graph they build. However, due to the omission of bias within the causal graph, they essentially encourage models to learn biased causal effects in latent space. In this paper, we propose a novel causally disentangling framework that aims to learn unbiased causal effects. We first introduce inductive and dataset biases into traditional causal graph for the physical concepts of interest. Then, we eliminate the negative effects from these two biases by counterfactual intervention with reweighted loss function for learning unbiased causal effects. Finally, we employ the causal effects into the VAE to endow the latent representations with causality. In particular, we highlight that removing biases in this paper is regarded as a part of learning process for unbiased causal effects, which is crucial for causal disentanglement performance improvement. Through extensive experiments on real-world and synthetic datasets, we show that our method outperforms different baselines and obtains the state-of-the-art results for achieving causal representation learning.
Towards event ordering in digital forensics

In criminal investigation and criminal justice, investigators are usually faced with several reports, which contain a set of events of interest. Often, it is important to be able to order these events so that relevant queries can be posed on this ordering, such as "was X at location Y when the murder took place? ". However, ordering of these events is very difficult, especially if very few events are anchored in time, i.e., few events are associated with an explicit time. Manual extraction of all the events of interest from these reports is tedious. On the other hand, automated extraction is inaccurate at best, in the sense that either several events that may not be important could be included. This ultimately gives a large set of events to consider, and imposing an ordering on this set can yield a large tree structure, where nodes represent an event of interest, and an edge (i, j) indicates that event i occurred before or at the same time as event j, and the root node represents a special "start" event. In this paper, we investigate two techniques for automating extraction of events, and then ordering these. We compare the efficiency of the techniques through the size of the tree structure obtained
Neuronal merlin influences ERBB2 receptor expression on Schwann cells through neuregulin 1 type III signalling

Merlin mutations in Neurofibromatosis type 2 cause tumorigenic transformation of Schwann cells, leading to schwannoma development. Schulz et al. show that loss of neuronally expressed merlin alone increases the susceptibility of adjacent Schwann cells to mitogenic signals through the Neuregulin1-ErbB2/3 pathway.
Ontology Design for Information Integration in Disaster Management

One of the most pressing issues in improving disaster management is that of information integration. With many organizations involved in the disaster, crossing regional or even national borders, information exchange is crucial but cumbersome due to differing vocabularies and representations both at human language and IT level. Carefully designed ontologies can be instrumental in addressing this problem, by providing a reference model for humans, and with that the basis for information integration at IT level. We devise an ontology stack covering the description of damages (caused by the disaster), resources (available to organizations fighting the disaster), and their connection (e.g. which resources are relevant for which damage). To ensure sustainable modeling, we follow the guiding principles of the foundational ontology DOLCE. We identify the most relevant design choices, and we discuss their solution. The resulting ontologies form part of a prototype that integrates information in a service-oriented architecture. We show how our ontologies are used to implement the relevant queries for information within that prototype.
Robust crowd bias correction via dual knowledge transfer from multiple overlapping sources

One of the largest constituents of big data is the crowdsourced or user-generated data which contain a wide range of valuable information. However, they are inherently biased and possibly spammed, making trustworthy information extraction an imperative task. As a special case, we study reviewer-posted ratings for products. The biased ratings can lead to disappointed customers due to overrated products, and reduced revenues of business owners caused by undeserved negative ratings. To distill objective product quality measurements, most existing methods try to infer unbiased ratings from the raw ratings alone, and may not overcome the inherent bias to recover the underlying true ratings. Though improved bias corrections have been achieved with domain expert helps, the overhead of expert efforts can be rather expensive in practice. We exploit the variety of big data and adopt a multiple source mining approach, which finds trustworthy measurements without domain expert, but with knowledge crowdsourced and transferred from external domains. We address the challenges that the multiple data sources are 1) inherently heterogeneous, 2) at most only partially overlapping and 3) biased by themselves. We explore and analyze the strengths and weaknesses of various knowledge transfer strategies. We then propose Consensus Ranking Dual Transfer (CRDT) to handle the above challenges by identifying "anchor reviewers" as a bridge for robust "dual transfer", and removing bias in individual sources via consensus ranking aggregation. Experiments on real-world rating datasets demonstrate that the proposed approach can deliver more robust bias correcting effects than the baselines and can identify abnormal reviewers.
SINR-based k-coverage probability in cellular networks with arbitrary shadowing

We give numerically tractable, explicit integral expressions for the distribution of the signal-to-interference-and-noise-ratio (SINR) experienced by a typical user in the downlink channel from the k-th strongest base stations of a cellular network modelled by Poisson point process on the plane. Our signal propagation-loss model comprises of a power-law path-loss function with arbitrarily distributed shadowing, independent across all base stations, with and without Rayleigh fading. Our results are valid in the whole domain of SINR, in particular for SINR <; 1, where one observes multiple coverage. In this latter aspect our paper complements previous studies reported in [1].
A novel effective model for solving 3D forward scattering problems in a homogeneous background

A new model for the solution of 3D forward scattering problems, particularly suitable for lossy scenarios, is presented. The proposed model is a suitable extension to the vectorial case of the contrast source-extended Born method recently proposed with reference to the 2D scalar case. Based on the proposed model, a new series is introduced which represents a convenient may to solve the forward problem in many cases. Moreover, analytical tools are provided to foresee the actual behavior of the series; they are also of interest for the corresponding inverse problem.
Long Noncoding RNA HOTAIR Is a Prognostic Marker for Esophageal Squamous Cell Carcinoma Progression and Survival

Background It is currently unclear whether the expression of HOX transcript antisense RNA (HOTAIR) correlates with the progression of esophageal cancer. The aim of this study was to examine HOTAIR expression in patients with esophageal squamous cell cancer (ESCC) and explore its clinical significance. Methods Differences in the expression of HOTAIR were examined via in situ hybridization (ISH) and quantitative reverse transcriptase PCR (qRT-PCR). The prognostic significance was evaluated using Kaplan–Meier and Cox regression analyses. Proliferation, colony formation and migration assays were performed in ESCC cell lines to determine the function of HOTAIR in the progression of ESCC in vitro. Results A notably higher level of HOTAIR expression was found in ESCC tissues. High expression levels of HOTAIR in ESCC patients correlated positively with clinical stage, TNM classification, histological differentiation and vital status. HOTAIR expression was found to be an independent prognostic factor in ESCC patients. ESCC patients who expressed high levels of HOTAIR had substantially lower overall 5-year survival rates than HOTAIR-negative patients. In vitro assays of ESCC cell lines demonstrated that HOTAIR mediated the proliferation, colony formation and migratory capacity of ESCC cells. Conclusion HOTAIR is a potential biomarker for ESCC prognosis, and the dysregulation of HOTAIR may play an important role in ESCC progression.
Systematic Reconstruction of the Past , Present , and Future of Internet Path and Delay DK

Delay and path between arbitrary points in the Internet are not always measurable and often hard to estimate or predict. In this work we use the path stitching algorithm to systematically reconstruct the past, present, and future of Internet path and delay. The input to path stitching is the traceroute output data from CAIDA’s Skitter and Ark projects and DIMES and routing tables from RIPE and RouteViews. Without any additional active measurement, path stitching can produce delay and path estimates between random host pairs. Comparison of the estimated delay with PingER data demonstrates that the estimates are close enough to live measurements. The six years of the reconstructed history of the delay distribution between random pairs of /24 prefixes demonstrates how the overall Internet delay performance has evolved over time. Path stitching relies on AS-level path inference. By changing the AS path we can answer what-if scenarios of AS path change on end-to-end delay performance. Our results demonstrate that path stitching is a powerful and versatile tool for a wide range of applications. It also opens up a new direction in performance estimation that takes full advantage of structural properties of the Internet.
PANEL 1 VALUING DIVERSITY: THE CHALLENGE FACING IT MANAGERS AND EDUCATORS

The ability ofcompanies to efectively compete in the years ahead will be determined in large measure by their success in employing productive workers in a labor management characterized by scarcity, skill deficiencies, and demographic diversity. The most successful companies will be those that meet this challenge creatively and aggressively. discussion. will discuss her past years she an of in the and implementation of a curriculum, teaching materials, and methods to help students learn about the and opportunities of valuing diversity in the These efforts were of her work in developing the ethics curriculum module within the school. She will also describe her current efforts to identify faculty development needs and design and implement faculty development strategies and programs. Gentile will present a framework for categorizing the pedagogical challenges associated with implementing diversity education programs and a template for individual or institutional faculty development. She will also summarize the successes and pitfalls Harvard Business School has experienced in its efforts.
Extending parameterized problem-tracing questions for Java with personalized guidance

Problem-tracing questions are popular among teachers of various programming languages. In an assessment mode these questions allows to evaluate student knowledge of language semantics. In a self-assessment mode, they provide an excellent learning tool. A 2004 ITiCSE working group report [4] stressed the importance of this type of questions to build foundation of higher-level knowledge. Yet the use of problem-tracing questions is still limited due to a large authoring overhead. To resolve this bottleneck, we explored the idea of parameterized question generation [2]. We developed QuizPACK [1], a system which can generate parameterized problem-tracing questions for C programming language. We also developed QuizGuide [1], a personalized guidance system for QuizPACK, which models student knowledge and guides students individually to most appropriate questions to try. The results of our studies demonstrated that QuizPACK strongly benefits student knowledge and that QuizGuide personalized guidance technology increased student ability to answer questions correctly and encouraged them to use the system more extensively (which, in turn, positively impacted their knowledge) [1]. However, parameterized questions in area of C programming were not as diverse from the complexity point of view as parameterized questions explored in other areas such as physics [2]. As a result, it was left unclear whether personalized guidance technology can successfully guide students to a broader range of questions from relatively simple to very difficult. The work reported in this poster expands our work on parameterized questions to a more sophisticated domain of object-oriented Java programming, which allowed us to introduce questions of much broader. Capitalizing on our experience with QuizPACK, we developed QuizJET (Java Evaluation Toolkit), which supports authoring, delivery, and evaluation of parameterized questions for Java [3]. We also implemented JavaGuide system (Figure 1), which provides personalized guidance for QuizJET questions. We assessed the impact of adaptive navigation support to student work with questions of different complexity as well as the impact of this technology on weaker and stronger students. The results of two classroom studies indicate that personalized guidance encouraged students to use parameterized questions more extensively and also helped them to access right questions at the right time. Students were 2.5 times more likely to answer a quiz correctly with personalized guidance than without such it. In addition, we found that personalized guidance especially benefited weak students to achieve scores comparable with the scores of strong students on each complexity level of questions.