bluuebunny/crossref_metadata_2025 huggingface dataset:
REMIT: Reinforced Multi-Interest Transfer for Cross-Domain Recommendation
Authors: Sun Caiqi , Gu Jiewei , Hu Binbin , Dong Xin , Li Hai , Cheng Lei , Mo Linjian
Published: June 2023

Cold-start problem is one of the most challenging problems for recommender systems. One promising solution to this problem is cross-domain recommendation (CDR) which leverages rich information from an auxiliary source domain to improve the performance of recommender system in the target domain. In particular, the family of embedding and mapping methods for CDR is very effective, which explicitly learn a mapping function from source embeddings to target embeddings to transfer user’s preferences. Recent works usually transfer an overall source embedding by modeling a common or personalized preference bridge for all users. However, a unified user embedding cannot reflect the user’s multiple interests in auxiliary source domain. In this paper, we propose a novel framework called reinforced multi-interest transfer for CDR (REMIT). Specifically, we first construct a heterogeneous information network and employ different meta-path based aggregations to get user’s multiple interests in source domain, then transform different interest embeddings with different meta-generated personalized bridge functions for each user. To better coordinate the transformed user interest embeddings and the item embedding in target domain, we systematically develop a reinforced method to dynamically assign weights to transformed interests for different training instances and optimize the performance of target model. In addition, the REMIT is a general framework that can be applied upon various base models in target domain. Our extensive experimental results on large real-world datasets demonstrate the superior performance and compatibility of REMIT.
Fair-CDA: Continuous and Directional Augmentation for Group Fairness
Authors: Sun Rui , Zhou Fengwei , Dong Zhenhua , Xie Chuanlong , Hong Lanqing , Li Jiawei , Zhang Rui , Li Zhen , Li Zhenguo
Published: June 2023

In this work, we propose Fair-CDA, a fine-grained data augmentation strategy for imposing fairness constraints. We use a feature disentanglement method to extract the features highly related to the sensitive attributes. Then we show that group fairness can be achieved by regularizing the models on transition paths of sensitive features between groups. By adjusting the perturbation strength in the direction of the paths, our proposed augmentation is controllable and auditable. To alleviate the accuracy degradation caused by fairness constraints, we further introduce a calibrated model to impute labels for the augmented data. Our proposed method does not assume any data generative model and ensures good generalization for both accuracy and fairness. Experimental results show that Fair-CDA consistently outperforms state-of-the-art methods on widely-used benchmarks, e.g., Adult, CelebA and MovieLens. Especially, Fair-CDA obtains an 86.3% relative improvement for fairness while maintaining the accuracy on the Adult dataset. Moreover, we evaluate Fair-CDA in an online recommendation system to demonstrate the effectiveness of our method in terms of accuracy and fairness.
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
Authors: Tan Yue , Liu Yixin , Long Guodong , Jiang Jing , Lu Qinghua , Zhang Chengqi
Published: June 2023

Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing to the advantages of federated learning, federated graph learning (FGL) enables clients to train strong GNN models in a distributed manner without sharing their private data. A core challenge in federated systems is the non-IID problem, which also widely exists in real-world graph data. For example, local data of clients may come from diverse datasets or even domains, e.g., social networks and molecules, increasing the difficulty for FGL methods to capture commonly shared knowledge and learn a generalized encoder. From real-world graph datasets, we observe that some structural properties are shared by various domains, presenting great potential for sharing structural knowledge in FGL. Inspired by this, we propose FedStar, an FGL framework that extracts and shares the common underlying structure information for inter-graph federated learning tasks. To explicitly extract the structure information rather than encoding them along with the node features, we define structure embeddings and encode them with an independent structure encoder. Then, the structure encoder is shared across clients while the feature-based knowledge is learned in a personalized way, making FedStar capable of capturing more structure-based domain-invariant information and avoiding feature misalignment issues. We perform extensive experiments over both cross-dataset and cross-domain non-IID FGL settings, demonstrating the superiority of FedStar.
Unbalanced CO-optimal Transport
Authors: Tran Quang Huy , Janati Hicham , Courty Nicolas , Flamary Rémi , Redko Ievgen , Demetci Pinar , Singh Ritambhara
Published: June 2023

Optimal transport (OT) compares probability distributions by computing a meaningful alignment between their samples. CO-optimal transport (COOT) takes this comparison further by inferring an alignment between features as well. While this approach leads to better alignments and generalizes both OT and Gromov-Wasserstein distances, we provide a theoretical result showing that it is sensitive to outliers that are omnipresent in real-world data. This prompts us to propose unbalanced COOT for which we provably show its robustness to noise in the compared datasets. To the best of our knowledge, this is the first such result for OT methods in incomparable spaces. With this result in hand, we provide empirical evidence of this robustness for the challenging tasks of heterogeneous domain adaptation with and without varying proportions of classes and simultaneous alignment of samples and features across two single-cell measurements.
Linear Regularizers Enforce the Strict Saddle Property
Authors: Ubl Matthew , Hale Matthew , Yazdani Kasra
Published: June 2023

Satisfaction of the strict saddle property has become a standard assumption in non-convex optimization, and it ensures that many first-order optimization
algorithms will almost always escape saddle points. However, functions exist in machine learning that do not satisfy this property, such as the loss function of a neural network with at least two hidden layers. First-order methods such as gradient descent may converge to non-strict saddle points of such functions, and there do not currently exist any first-order methods that reliably escape non-strict saddle points. To address this need, we demonstrate that regularizing a function with a linear term enforces the strict saddle property, and we provide justification for only regularizing locally, i.e., when the norm of the gradient falls below a certain threshold. We analyze bifurcations that may result from this form of regularization, and then we provide a selection rule for regularizers that depends only on the gradient of an objective function. This rule is shown to guarantee that gradient descent will escape the neighborhoods around a broad class of non-strict saddle points, and this behavior is demonstrated on numerical examples of non-strict saddle points common in the optimization literature.
Effective Continual Learning for Text Classification with Lightweight Snapshots
Authors: Wang Jue , Dong Dajie , Shou Lidan , Chen Ke , Chen Gang
Published: June 2023

Continual learning is known for suffering from catastrophic forgetting, a phenomenon where previously learned concepts are forgotten upon learning new tasks. A natural remedy is to use trained models for old tasks as ‘teachers’ to regularize the update of the current model to prevent such forgetting. However, this requires storing all past models, which is very space-consuming for large models, e.g. BERT, thus impractical in real-world applications. To tackle this issue, we propose to construct snapshots of seen tasks whose key knowledge is captured in lightweight adapters. During continual learning, we transfer knowledge from past snapshots to the current model through knowledge distillation, allowing the current model to review previously learned knowledge while learning new tasks. We also design representation recalibration to better handle the class-incremental setting. Experiments over various task sequences show that our approach effectively mitigates catastrophic forgetting and outperforms all baselines.
Efficient Exploration in Resource-Restricted Reinforcement Learning
Authors: Wang Zhihai , Pan Taoxing , Zhou Qi , Wang Jie
Published: June 2023

In many real-world applications of reinforcement learning (RL), performing actions requires consuming certain types of resources that are non-replenishable in each episode. Typical applications include robotic control with limited energy and video games with consumable items. In tasks with non-replenishable resources, we observe that popular RL methods such as soft actor critic suffer from poor sample efficiency. The major reason is that, they tend to exhaust resources fast and thus the subsequent exploration is severely restricted due to the absence of resources. To address this challenge, we first formalize the aforementioned problem as a resource-restricted reinforcement learning, and then propose a novel resource-aware exploration bonus (RAEB) to make reasonable usage of resources. An appealing feature of RAEB is that, it can significantly reduce unnecessary resource-consuming trials while effectively encouraging the agent to explore unvisited states. Experiments demonstrate that the proposed RAEB significantly outperforms state-of-the-art exploration strategies in resource-restricted reinforcement learning environments, improving the sample efficiency by up to an order of magnitude.
Towards In-Distribution Compatible Out-of-Distribution Detection
Authors: Wu Boxi , Jiang Jie , Ren Haidong , Du Zifan , Wang Wenxiao , Li Zhifeng , Cai Deng , He Xiaofei , Lin Binbin , Liu Wei
Published: June 2023

Deep neural network, despite its remarkable capability of discriminating targeted in-distribution samples, shows poor performance on detecting anomalous out-of-distribution data. To address this defect, state-of-the-art solutions choose to train deep networks on an auxiliary dataset of outliers. Various training criteria for these auxiliary outliers are proposed based on heuristic intuitions. However, we find that these intuitively designed outlier training criteria can hurt in-distribution learning and eventually lead to inferior performance. To this end, we identify three causes of the in-distribution incompatibility: contradictory gradient, false likelihood, and distribution shift. Based on our new understandings, we propose a new out-of-distribution detection method by adapting both the top-design of deep models and the loss function. Our method achieves in-distribution compatibility by pursuing less interference with the probabilistic characteristic of in-distribution features. On several benchmarks, our method not only achieves the state-of-the-art out-of-distribution detection performance but also improves the in-distribution accuracy.
Semi-supervised Learning with Support Isolation by Small-Paced Self-Training
Authors: Xie Zheng , Sun Hui , Li Ming
Published: June 2023

In this paper, we address a special scenario of semi-supervised learning, where the label missing is caused by a preceding filtering mechanism, i.e., an instance can enter a subsequent process in which its label is revealed if and only if it passes the filtering mechanism. The rejected instances are prohibited to enter the subsequent labeling process due to economical or ethical reasons, making the support of the labeled and unlabeled distributions isolated from each other. In this case, semi-supervised learning approaches which rely on certain coherence of the labeled and unlabeled distribution would suffer from the consequent distribution mismatch, and hence result in poor prediction performance. In this paper, we propose a Small-Paced Self-Training framework, which iteratively discovers labeled and unlabeled instance subspaces with bounded Wasserstein distance. We theoretically prove that such a framework may achieve provably low error on the pseudo labels during learning. Experiments on both benchmark and pneumonia diagnosis tasks show that our method is effective.
Label-Specific Feature Augmentation for Long-Tailed Multi-Label Text Classification
Authors: Xu Pengyu , Xiao Lin , Liu Bing , Lu Sijin , Jing Liping , Yu Jian
Published: June 2023

Multi-label text classification (MLTC) involves tagging a document with its most relevant subset of labels from a label set. In real applications, labels usually follow a long-tailed distribution, where most labels (called as tail-label) only contain a small number of documents and limit the performance of MLTC. To facilitate this low-resource problem, researchers introduced a simple but effective strategy, data augmentation (DA). However, most existing DA approaches struggle in multi-label settings. The main reason is that the augmented documents for one label may inevitably influence the other co-occurring labels and further exaggerate the long-tailed problem. To mitigate this issue, we propose a new pair-level augmentation framework for MLTC, called Label-Specific Feature Augmentation (LSFA), which merely augments positive feature-label pairs for the tail-labels. LSFA contains two main parts. The first is for label-specific document representation learning in the high-level latent space, the second is for augmenting tail-label features in latent space by transferring the documents second-order statistics (intra-class semantic variations) from head labels to tail labels. At last, we design a new loss function for adjusting classifiers based on augmented datasets. The whole learning procedure can be effectively trained. Comprehensive experiments on benchmark datasets have shown that the proposed LSFA outperforms the state-of-the-art counterparts.
Neighborhood-Regularized Self-Training for Learning with Few Labels
Authors: Xu Ran , Yu Yue , Cui Hejie , Kan Xuan , Zhu Yanqiao , Ho Joyce , Zhang Chao , Yang Carl
Published: June 2023

Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been successfully applied in semi-supervised learning tasks, but one drawback of self-training is that it is vulnerable to the label noise from incorrect pseudo labels. Inspired by the fact that samples with similar labels tend to share similar representations, we develop a neighborhood-based sample selection approach to tackle the issue of noisy pseudo labels. We further stabilize self-training via aggregating the predictions from different rounds during sample selection. Experiments on eight tasks show that our proposed method outperforms the strongest self-training baseline with 1.83% and 2.51% performance gain for text and graph datasets on average. Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36.8% and saves 57.3% of the time when compared with the best baseline. Our code and appendices will be uploaded to: https://github.com/ritaranx/NeST.
Learning-Assisted Algorithm Unrolling for Online Optimization with Budget Constraints
Authors: Yang Jianyi , Ren Shaolei
Published: June 2023

Online optimization with multiple budget constraints is challenging since the online decisions over a short time horizon are coupled together by strict inventory constraints. The existing manually-designed algorithms cannot achieve satisfactory average performance for this setting because they often need a large number of time steps for convergence and/or may violate the inventory constraints. In this paper, we propose a new machine learning (ML) assisted unrolling approach, called LAAU (Learning-Assisted Algorithm Unrolling), which unrolls the agent’s online decision pipeline and leverages an ML model for updating the Lagrangian multiplier online. For efficient training via backpropagation, we derive gradients of the decision pipeline over time. We also provide the average cost bounds for two cases when training data is available offline and collected online, respectively. Finally, we present numerical results to highlight that LAAU can outperform the existing baselines.
ADEPT: A DEbiasing PrompT Framework
Authors: Yang Ke , Yu Charles , Fung Yi R. , Li Manling , Ji Heng
Published: June 2023

Several works have proven that finetuning is an applicable approach for debiasing contextualized word embeddings. Similarly, discrete prompts with semantic meanings have shown to be effective in debiasing tasks. With unfixed mathematical representation at the token level, continuous prompts usually surpass discrete ones at providing a pre-trained language model (PLM) with additional task-specific information. Despite this, relatively few efforts have been made to debias PLMs by prompt tuning with continuous prompts compared to its discrete counterpart. Furthermore, for most debiasing methods that alter a PLM's original parameters, a major problem is the need to not only decrease the bias in the PLM but also to ensure that the PLM does not lose its representation ability. Finetuning methods typically have a hard time maintaining this balance, as they tend to violently remove meanings of attribute words (like the words developing our concepts of "male" and "female" for gender), which also leads to an unstable and unpredictable training process. In this paper, we propose ADEPT, a method to debias PLMs using prompt tuning while maintaining the delicate balance between removing biases and ensuring representation ability. To achieve this, we propose a new training criterion inspired by manifold learning and equip it with an explicit debiasing term to optimize prompt tuning. In addition, we conduct several experiments with regard to the reliability, quality, and quantity of a previously proposed attribute training corpus in order to obtain a clearer prototype of a certain attribute, which indicates the attribute's position and relative distances to other words on the manifold. We evaluate ADEPT on several widely acknowledged debiasing benchmarks and downstream tasks, and find that it achieves competitive results while maintaining (and in some cases even improving) the PLM's representation ability. We further visualize words' correlation before and after debiasing a PLM, and give some possible explanations for the visible effects.
Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery
Authors: Yang Yiqin , Hu Hao , Li Wenzhe , Li Siyuan , Yang Jun , Zhao Qianchuan , Zhang Chongjie
Published: June 2023

Offline reinforcement learning (RL) enables the agent to effectively learn from logged data, which significantly extends the applicability of RL algorithms in real-world scenarios where exploration can be expensive or unsafe. Previous works have shown that extracting primitive skills from the recurring and temporally extended structures in the logged data yields better learning. However, these methods suffer greatly when the primitives have limited representation ability to recover the original policy space, especially in offline settings. In this paper, we give a quantitative characterization of the performance of offline hierarchical learning and highlight the importance of learning lossless primitives. To this end, we propose to use a flow-based structure as the representation for low-level policies. This allows us to represent the behaviors in the dataset faithfully while keeping the expression ability to recover the whole policy space. We show that such lossless primitives can drastically improve the performance of hierarchical policies. The experimental results and extensive ablation studies on the standard D4RL benchmark show that our method has a good representation ability for policies and achieves superior performance in most tasks.
Boosted Dynamic Neural Networks
Authors: Yu Haichao , Li Haoxiang , Hua Gang , Huang Gao , Shi Humphrey
Published: June 2023

Early-exiting dynamic neural networks (EDNN), as one type of dynamic neural networks, has been widely studied recently. A typical EDNN has multiple prediction heads at different layers of the network backbone. During inference, the model will exit at either the last prediction head or an intermediate prediction head where the prediction confidence is higher than a predefined threshold. To optimize the model, these prediction heads together with the network backbone are trained on every batch of training data. This brings a train-test mismatch problem that all the prediction heads are optimized on all types of data in training phase while the deeper heads will only see difficult inputs in testing phase. Treating training and testing inputs differently at the two phases will cause the mismatch between training and testing data distributions. To mitigate this problem, we formulate an EDNN as an additive model inspired by gradient boosting, and propose multiple training techniques to optimize the model effectively. We name our method BoostNet. Our experiments show it achieves the state-of-the-art performance on CIFAR100 and ImageNet datasets in both anytime and budgeted-batch prediction modes. Our code is released at https://github.com/SHI-Labs/Boosted-Dynamic-Networks.
Interventional SHAP Values and Interaction Values for Piecewise Linear Regression Trees
Authors: Zern Artjom , Broelemann Klaus , Kasneci Gjergji
Published: June 2023

In recent years, game-theoretic Shapley values have gained increasing attention with respect to local model explanation by feature attributions. While the approach using Shapley values is model-independent, their (exact) computation is usually intractable, so efficient model-specific algorithms have been devised including approaches for decision trees or their ensembles in general. Our work goes further in this direction by extending the interventional TreeSHAP algorithm to piecewise linear regression trees, which gained more attention in the past few years. To this end, we introduce a decomposition of the contribution function based on decision paths, which allows a more comprehensible formulation of SHAP algorithms for tree-based models. Our algorithm can also be readily applied to computing SHAP interaction values of these models. In particular, as the main contribution of this paper, we provide a more efficient approach of interventional SHAP for tree-based models by precomputing statistics of the background data based on the tree structure.
Memorization Weights for Instance Reweighting in Adversarial Training
Authors: Zhang Jianfu , Hong Yan , Zhao Qibin
Published: June 2023

Adversarial training is an effective way to defend deep neural networks (DNN) against adversarial examples. However, there are atypical samples that are rare and hard to learn, or even hurt DNNs' generalization performance on test data. In this paper, we propose a novel algorithm to reweight the training samples based on self-supervised techniques to mitigate the negative effects of the atypical samples.
Specifically, a memory bank is built to record the popular samples as prototypes and calculate the memorization weight for each sample, evaluating the "typicalness" of a sample. All the training samples are reweigthed based on the proposed memorization weights to reduce the negative effects of atypical samples. Experimental results show the proposed method is flexible to boost state-of-the-art adversarial training methods, improving both robustness and standard accuracy of DNNs.
Delving into the Adversarial Robustness of Federated Learning
Authors: Zhang Jie , Li Bo , Chen Chen , Lyu Lingjuan , Wu Shuang , Ding Shouhong , Wu Chao
Published: June 2023

In Federated Learning (FL), models are as fragile as centrally trained models against adversarial examples. However, the adversarial robustness of federated learning remains largely unexplored. This paper casts light on the challenge of adversarial robustness of federated learning. To facilitate a better understanding of the adversarial vulnerability of the existing FL methods, we conduct comprehensive robustness evaluations on various attacks and adversarial training methods. Moreover, we reveal the negative impacts induced by directly adopting adversarial training in FL, which seriously hurts the test accuracy, especially in non-IID settings. In this work, we propose a novel algorithm called Decision Boundary based Federated Adversarial Training (DBFAT), which consists of two components (local re-weighting and global regularization) to improve both accuracy and robustness of FL systems. Extensive experiments on multiple datasets demonstrate that DBFAT consistently outperforms other baselines under both IID and non-IID settings.
Let the Data Choose: Flexible and Diverse Anchor Graph Fusion for Scalable Multi-View Clustering
Authors: Zhang Pei , Wang Siwei , Li Liang , Zhang Changwang , Liu Xinwang , Zhu En , Liu Zhe , Zhou Lu , Luo Lei
Published: June 2023

In the past few years, numerous multi-view graph clustering algorithms have been proposed to enhance the clustering performance by exploring information from multiple views. Despite the superior performance, the high time and space expenditures limit their scalability. Accordingly, anchor graph learning has been introduced to alleviate the computational complexity. However, existing approaches can be further improved by the following considerations: (i) Existing anchor-based methods share the same number of anchors across views. This strategy violates the diversity and flexibility of multi-view data distribution. (ii) Searching for the optimal anchor number within hyper-parameters takes much extra tuning time, which makes existing methods impractical. (iii) How to flexibly fuse multi-view anchor graphs of diverse sizes has not been well explored in existing literature. To address the above issues, we propose a novel anchor-based method termed Flexible and Diverse Anchor Graph Fusion for Scalable Multi-view Clustering (FDAGF) in this paper. Instead of manually tuning optimal anchor with massive hyper-parameters, we propose to optimize the contribution weights of a group of pre-defined anchor numbers to avoid extra time expenditure among views. Most importantly, we propose a novel hybrid fusion strategy for multi-size anchor graphs with theoretical proof, which allows flexible and diverse anchor graph fusion. Then, an efficient linear optimization algorithm is proposed to solve the resultant problem. Comprehensive experimental results demonstrate the effectiveness and efficiency of our proposed framework. The source code is available at https://github.com/Jeaninezpp/FDAGF.
Dynamic Ensemble of Low-Fidelity Experts: Mitigating NAS “Cold-Start”
Authors: Zhao Junbo , Ning Xuefei , Liu Enshu , Ru Binxin , Zhou Zixuan , Zhao Tianchen , Chen Chen , Zhang Jiajin , Liao Qingmin , Wang Yu
Published: June 2023

Predictor-based Neural Architecture Search (NAS) employs an architecture performance predictor to improve the sample efficiency. However, predictor-based NAS suffers from the severe ``cold-start'' problem, since a large amount of architecture-performance data is required to get a working predictor. In this paper, we focus on exploiting information in cheaper-to-obtain performance estimations (i.e., low-fidelity information) to mitigate the large data requirements of predictor training. Despite the intuitiveness of this idea, we observe that using inappropriate low-fidelity information even damages the prediction ability and different search spaces have different preferences for low-fidelity information types. To solve the problem and better fuse beneficial information provided by different types of low-fidelity information, we propose a novel dynamic ensemble predictor framework that comprises two steps. In the first step, we train different sub-predictors on different types of available low-fidelity information to extract beneficial knowledge as low-fidelity experts. In the second step, we learn a gating network to dynamically output a set of weighting coefficients conditioned on each input neural architecture, which will be used to combine the predictions of different low-fidelity experts in a weighted sum. The overall predictor is optimized on a small set of actual architecture-performance data to fuse the knowledge from different low-fidelity experts to make the final prediction. We conduct extensive experiments across five search spaces with different architecture encoders under various experimental settings. For example, our methods can improve the Kendall's Tau correlation coefficient between actual performance and predicted scores from 0.2549 to 0.7064 with only 25 actual architecture-performance data on NDS-ResNet. Our method can easily be incorporated into existing predictor-based NAS frameworks to discover better architectures. Our method will be implemented in Mindspore (Huawei 2020), and the example code is published at https://github.com/A-LinCui/DELE.
Does It Pay to Optimize AUC?
Authors: Zhou Baojian , Skiena Steven
Published: June 2023

The Area Under the ROC Curve (AUC) is an important model metric for evaluating binary classifiers, and many algorithms have been proposed to optimize AUC approximately. It raises the question of whether the generally insignificant gains observed by previous studies are due to inherent limitations of the metric or the inadequate quality of optimization.

To better understand the value of optimizing for AUC, we present an efficient algorithm, namely AUC-opt, to find the provably optimal AUC linear classifier in R2, which runs in O(n+n- log n+n-) where n+ and n- are the number of positive and negative samples respectively. Furthermore, it can be naturally extended to Rd in O(n+n-d-1 log (n+n-)) by recursively calling AUC-opt in lower-dimensional spaces. We prove the problem is NP-complete when d is not fixed, reducing from the open hemisphere problem.

Compared with other methods, experiments show that AUC-opt achieves statistically significant improvements between 17 to 40 in R2 and 4 to 42 in R3 of 50 t-SNE training datasets. However, generally, the gain proves insignificant on most testing datasets compared to the best standard classifiers. Similar observations are found for nonlinear AUC methods under real-world datasets.
Quantized Feature Distillation for Network Quantization
Authors: Zhu Ke , He Yin-Yin , Wu Jianxin
Published: June 2023

Neural network quantization aims to accelerate and trim full-precision neural network models by using low bit approximations. Methods adopting the quantization aware training (QAT) paradigm have recently seen a rapid growth, but are often conceptually complicated. This paper proposes a novel and highly effective QAT method, quantized feature distillation (QFD). QFD first trains a quantized (or binarized) representation as the teacher, then quantize the network using knowledge distillation (KD). Quantitative results show that QFD is more flexible and effective (i.e., quantization friendly) than previous quantization methods. QFD surpasses existing methods by a noticeable margin on not only image classification but also object detection, albeit being much simpler. Furthermore, QFD quantizes ViT and Swin-Transformer on MS-COCO detection and segmentation, which verifies its potential in real world deployment. To the best of our knowledge, this is the first time that vision transformers have been quantized in object detection and image segmentation tasks.
རིག་མཛོད་དུས་དེབ་འཐོན་རིམ་དང་པ།
Authors: རིག་མཛོད་དུས་དེབ།
Published: June 2023

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རྫོང་ཁའི་བྱ་ཚིག་ལས་གྲུབ་པའི་མིང་ལུ་དཔྱད་པ།
Authors: ཚེ་རིང་། དཀོན་མཆོག་
Published: June 2023

འབྲུག་རྒྱལ་ཡོངས་རྫོང་ཁ་གོང་འཕེལ་ལུ་དམིགས་ཏེ་བྲིས་ཡོད་པའི་ རྫོང་ཁའི་ནང་བྱ་ཚིག་ལས་གྲུབ་པའི་མིང་ལུ་ དཔྱད་པ་ཟེར་མི་རྩོམ་ཤོག་འདི་ནང་ བོད་འབྲུག་གི་སྔ་ཕྱིའི་མཁས་པ་ཚུ་གིས་ གསུངས་ཡོད་མི་མིང་གྲུབ་ཚུལ་དང་ དཔེ་རྙིང་ཚུ་ག་བསྡུར་རྐྱབ་ཏེ་ ཞིབ་འཚོལ་འབད་ཡོདཔ་མ་ཚད་ འབྲུག་གཞུང་ཤེས་རིག་ལྷན་ཁག་གིས་ བཟོ་སྟེ་ཡོད་ མི་རྫོང་ཁའི་སློབ་དེབ་ཚུ་ལུ་ཡང་ དབྱེ་ཞིབ་མཐིིལ་ཕྱིན་འབད་དེ་ བྱ་བའི་མིང་ཚུ་ དུས་གསུམ་ག་ཅི་ལས་གྲུབ་ཨིན་ན་ གསལ་ཁ་བསྟན་ཏེ་བྲིས་ཡོདཔ་ཨིན། དེ་བཟུམ་སྦེ་ དངོས་པོ་བདག་དང་འབྲེལ་བ་གཙོ་བོར་བསྟན་པའི་མིང་གྲུབ་ ཚུལ་དང་། དངོས་པོ་གཞན་དང་འབྲེལ་བ་གཙོ་བོར་བསྟན་པའི་མིང་གྲུབ་ཚུལ། བྱ་བའི་ངོ་བོ་གཙོར་བསྟན་གྱིི་མིང་ གྲུབ་ཚུལ། ལས་ཀྱི་དངོས་པོ་གཙོ་བོར་བསྟན་པའི་མིང་གྲུབ་ཚུལ། བྱས་འབྲས་གཙོ་བོར་བསྟན་པའི་མིང་གྲུབ་ཚུལ། མིང་མཐའ་ཞུགས་མ་ཞུགས་ཀྱི་སྒོ་ལས་ བྱ་ཚིག་མིང་སྦེ་གྲུབ་ཚུལ་ཚུ་གི་སྐོར་ལས་ དབྱེ་དཔྱད་འབད་ཡི། དཔྱད་ རྩོམ་འདི་འབྲི་དགོ་མི་འདི་ཡང་གཙོ་བོ་ར་ དུས་ཀྱི་འཕོ་བསྒྱུར་དང་བསྟུན་ རྫོང་ཁའི་སྐད་ཡིག་ལྷབ་སྦྱང་འབད་མི་ཚུ་ གིས་ མིང་ཚིག་འདི་ལུ་དབྱེ་བ་མ་ཕྱེ་བར་ ཡི་གུའི་སྡེབ་སྦྱོར་ཚུ་ཡང་ཕྱི་འགྱུར་སྦེ་ལག་ལེན་འཐབ་དོ་ཡོདཔ་ལས་ དེ་གི་ དཀའ་ངལ་ཚུ་བསལ་ཚུགས་པའི་རེ་བ་བསྐྱེད་དེ་བྲིས་ཡོདཔ་ཨིན།
Performance and Virulence of the Entomopathogenic Fungi Beauveria bassiana Grown in Media Derived from Biodegradable Agricultural Wastes Enriched with Cricket Powder
Authors: Afandhi Aminudin , Rachmawati Rina , Syib’li Muhammad Akhid , Zain Huurul Ainindya Ulihta
Published: June 2023

Entomopathogenic fungi (EPF) can be an eco-friendly alternative to control pests. To produce large-scale EPF requires underused economical substrates containing high carbon and nitrogen; hence the production could be cost-effective. This study investigates the effects of organic agricultural waste, i.e., rice bran, rice husks, tea dregs, and wheat bran enriched with cricket powder as culture media on the mycelial growth, sporulation, and conidia viability of Beauveria bassiana and the fungal pathogenicity towards Spodoptera litura larvae. For each type of medium, five independent cultures of B. bassiana are treated as replications. B. bassiana grown on a PDA medium is treated as a control. This study shows that rice bran enriched with cricket powder became the most suitable waste medium to support the mass production of virulent B. bassiana. Therefore, rice bran media enriched with cricket powder can be used as an appropriate medium for the mass production of B. bassiana.
Aetiological spectrum of patients of pleural effusion – An observational study
Authors: KL Bansal , S Bansal , S Bansal , S Sahay
Published: June 2023

Background: Pleural effusion is a clinical condition that is a manifestation of various aetiologies and pathologies. The aetiologies vary in different geographical areas and different age groups. The goal of this study was to evaluate the aetiological spectrum of patients with pleural effusion presenting in the Department of TB & Respiratory Medicine. Materials and methods: 120 patients were involved in current study in which the detection of pleural effusion has been done based on the history, physical examination, and radiography. Pleural fluid was aspirated under USG guidance and then analyzed for various parameters. Results: The study showed male preponderance (70%). The commonest age group involved was between 31 to 40 years (25.83%). Most cases showed exudative effusion (96%). Tuberculosis was the most common aetiology (80%). Conclusion: Different geographical areas may have a different aetiological spectrum of pleural effusion. Tuberculosis is the most prevalent pleural effusion cause in our area involving middle age group with a male preponderance. Keywords: transudate; tuberculosis; exudate; pleural effusion; adenosine deaminase
Study of mast cell density, distribution and morphology in skin lesions of leprosy
Authors: H Shruthi , AN Roopa , MR Amrutha , BS Madhusudan
Published: June 2023

Background: Mast cells are found at all levels of dermis, grouped around blood vessels, nerves and appendages. An increase in the mast cell number has been reported in various cutaneous diseases. Mast cells respond to diverse range of stimuli like neuronal impulses, allergens, antigens, hormones, cytokines from T cells and keratinocytes and thus play a role in manifestation of dermatological disorders. The objective was to study mast cell density in various subsets of Hansen’s disease and analyse whether the distribution and density vary amongst the subsets. Methods: A total of 60 skin biopsies diagnosed as leprosy were included in this study. 10 control slides were also studied for mast cell density and distribution. Histopathological diagnosis was made on the biopsy and mast cell density and distribution was assessed on toluidine blue stained sections and compared with those of controls. Results: All the subsets of Hansen’s disease studied showed an increase in the number of mast cells. The highest mean mast cell count was noted in lepromatous leprosy (66/mm2), followed by erythema nodosum leprosum (42.6/mm2) and borderline lepromatous leprosy (40.8/mm2). The mean mast cell count showed progressive increase over the immunological spectrum of tuberculoid leprosy to lepromatous leprosy. The mast cells showed spindle and ovoid morphology in all the lesions (active) while round morphology was seen in controls. Conclusion: Tissue mast cell number, distribution and morphology vary depending on the degree of activation in different leprosy lesions. This may have a bearing on the diagnosis and management. Keywords: mast cells; lepromatous leprosy; tuberculoid leprosy; borderline tuberculoid; indeterminate leprosy
Prevalence and determinants of obesity among school going adolescents: A systematic review
Authors: N Mangrola , K Patel , PK Patel , D Chauhan , N Patel
Published: June 2023

Adolescence is considered as critical period for the development of obesity. The frequency of childhood obesity has increased over the last 3 decades, and it has emerged as a public health concern in multiple places around the world. A number of factors interact in a complex way to cause obesity, which is still not fully understood. Therefore, the purpose of this review was to identify and assess the scientific literature on the prevalence of obesity, and behavioural, contextual and biological factors associated with obesity in adolescents. The search was carried out using PubMed, Web of Science, Embase and Scopus considering articles published from the establishment of the databanks until December, 2022. Data on the prevalence of overweight and obesity among children and adolescents, and articles on the determinants and factors affecting obesity were reviewed. The results obtained and the association observed among the factors studied will be helpful to support the planning, implementation and evaluation of preventive activities and interventions. Keywords: adolescents; childhood; intervention; obesity; overweight
A rare association of brucellosis and pulmonary hydatid cyst in a patient with human immunodeficiency virus
Authors: S Kanugula , Y Pasam , D Baddula
Published: June 2023

Background: Hydatid disease, or cystic echinococcosis, is a parasitic infection caused by a tapeworm. Pulmonary hydatid infection is the second common manifestation of hydatid disease. We present a patient who was diagnosed with recurrent hydatid cyst in lung and later diagnosed with brucellosis, which is a highly contagious zoonotic disease. Case presentation: A 47-years-old male was diagnosed with pulmonary hydatid disease after a computed tomography (CT) chest-plain was done. As the patient had high intermittent fever for 4 months which could not be explained by an intact cyst in the followup, the patient was diagnosed with brucellosis with both IgG and IgM antibodies positive. Conclusions: Hydatid cyst present with varied symptomatology. A high degree of clinical suspicion combined with meticulous history and clinical examination supported by laboratory investigations are required for its diagnosis. This case highlights the importance of considering brucella as a differential diagnosis. Keywords: hydatid cyst; brucellosis; co-infection; HIV; echinococcus
ÜNİVERSİTE ÖĞRENCİLERİNDE CİNSİYET, PSİKOLOJİK KIRILGANLIK VE ÖZ ŞEFKATİN PSİKOLOJİK İYİ OLUŞ İLE İLİŞKİSİNİN İNCELENMESİ
Authors: Köksal Zeynep , Topkaya Nursel , Şahin Ertuğrul
Published: June 2023

Bu çalışmanın amacı, üniversite öğrencilerinde psikolojik kırılganlık, öz şefkat ve cinsiyetin psikolojik iyi oluş ile ilişkisini incelemektir. Araştırmanın katılımcılarını Ondokuz Mayıs Üniversitesine bağlı farklı fakültelerde öğrenim görmekte olan ve uygun örnekleme yöntemiyle seçilen 430 (Ort. = 22.02, Ss. = 2.20) üniversite öğrencisi oluşturmuştur. Katılımcılar veri toplama aracı olarak Öz Şefkat Ölçeği, Psikolojik Kırılganlık Ölçeği, Psikolojik İyi Oluş Ölçeği ve Kişisel Bilgi Formundan oluşan bir anketi cevaplamıştır. Veri analizi sürecinde betimsel istatistikler, Pearson momentler çarpımı korelasyon katsayısı analizi, çoklu doğrusal regresyon analizi ve göreceli önem analizi kullanılmıştır. Araştırma sonucunda öz şefkat düzeyleri yüksek ve psikolojik kırılganlık düzeyleri düşük olan üniversite öğrencilerinin psikolojik iyi oluş düzeylerinin yüksek olduğu bulunmuştur. Ayrıca göreceli önem analizi sonuçları üniversite öğrencilerinde psikolojik iyi oluşun en önemli yordayıcısının öz şefkat olduğunu göstermiştir. Üniversite öğrencilerine yönelik psikolojik iyi oluşu artırmaya yönelik koruyucu ve önleyici psikoeğitim programlarında psikolojik kırılganlık düzeylerini azaltmaya ve öz şefkat düzeylerini artırmaya yönelik etkinliklere yer verilebilir.
GASTRONOMİ VE MUTFAK SANATLARI ÖĞRENCİLERİNİN STAJ SÜREÇLERİNDE YAŞADIKLARI İŞ STRESİNİN BELİRLENMESİ
Authors: Pekerşen Yeliz , Sırıklı İsmet Kutay
Published: June 2023

Mutfak ve turizm sektörü stresli bir meslek grubu olarak tanımlanmaktadır. Bunun sebebi doğrudan müşterilerle ilgilenme ve hizmet sektörünün doğası gereği iş yükünün fazla olmasından kaynaklanmaktadır. Bu çalışmanın amacı; Türkiye’de Gastronomi ve Mutfak Sanatları lisans programı bölümünde okuyup, staj yapmış öğrencilerin stres düzeylerinin boyutlarının ortaya koyulmasıdır. Böylelikle öğrencilerin yaşadığı stres etkenlerinin boyutlarını ve yeni mesleğe başlayan kişilerin bu süreci nasıl yönettikleri açığa çıkarılacaktır. Çalışma yöntemi olarak nitel araştırma deseninde içerik analizi yapılmıştır. Yarı yapılandırılmış görüşme formlarıyla gastronomi ve mutfak sanatları bölümünde okuyan 25 öğrenci ile görüşülmüştür. Sorulara yönelik cevaplar doğrultusunda temalar ve alt temalar oluşturularak literatürde tartışılmıştır. Çalışma sonuçları, öğrencilerin aşçılık/şeflik mesleğinin stresli bir süreç olduğunu ifade etmektedir. İş sorumluluğu olarak mutfak temizliği, mutfak kontrolü, depo sorumluluğu, yemek yapmak, hazırlık yapmak gibi ağır bir iş yükünün içerisine girmektedirler. Ek olarak stres etkeni olarak iş yoğunluğu ve üstleri tarafından beğenilmeme korkusu yanıtları baskıyla ilişkilenmektedir. Stres belirtileri olarak öğrenciler sinirli, dikkatsiz ve ne yapacağını bilememe gibi cevaplar vermişlerdir. Stres yönetimleri de genel olarak, kontrol etme ve yok sayma üzerine şekillenmektedir. Öğrenciler stres yönetimi sağlayabilmenin yollarını tam olarak bilemediklerini ifade ederek eğitim alınmasının doğru olduğunu ifade etmektedirler. Ayrıca şef ve yöneticilerin davranışlarının düzenlenmesi, ücret arttırımı ve çalışma saatlerinin düşürülmesi stres düzeyini azaltacağı düşünülmektedir.
Aras Nehri boyunca yayılış gösteren kara kavak popülasyonlarının genetik yapısı
Authors: ÖZDEMİR DEĞİRMENCİ Funda , ULUĞ Asiye
Published: June 2023

Kara kavak Türkiye’nin Doğu ve İç Anadolu bölgelerinde yayılış gösteren ve odun üretiminde yoğun bir şekilde kullanılan ekonomik açıdan önemli bir ağaç türüdür. Tüm dünyada ıslah programlarında ham madde üretimi için model organizma olarak kullanılmasının yanı sıra çevresel plastisitesi ve biyoçeşitliliğe sağladığı katkı vasıtasıyla başta Avrupa olmak üzere tüm dünyada doğal popülasyonları ve plantasyonları yoğun bir şekilde kullanılmaktadır. Doğal olarak yayılış gösterdikleri nehir kıyılarında tarla açma ve hidroelektrik santrali kurma gibi insan faaliyetlerinin artmasına paralel olarak türün habitatı günden güne azalmakta ve türün devamlılığı tehdit edilmektedir. Bu çalışmada Aras nehri boyunca yayılış gösteren kara kavak popülasyonlarındaki bireylere 16 mikrosatellit markör ile genetik kimlik verilmiştir. Popülasyonlar içerisinde yüksek oranda beklenen ve gözlenen heterozigotluk bulunmuştur. Aras nehrinin Iğdır-Kars ve Erzurum-Kars arasındaki kısımlarından örneklenen popülasyonların iki genetik gruptan köken aldığı açığa çıkarılmıştır. Bu iki genetik grup arasında yüksek oranda gen akışı sebebiyle düşük oranda genetik farklılaşma olduğu gözlemlenmiştir. Elde edilen genetik veriler Aras nehri boyunca yayılış gösteren kara kavak popülasyonları için in-situ/ex-situ koruma programlarının geliştirilmesinin yanı sıra tür ile gerçekleştirilecek ıslah programlarında da kullanılabilir.
Dağ kekiği (Origanum syriacum L. var. bevanii (Holmes) Ietswaart)’nin in vivo ve in vitro koşullarda yetiştirilen bitkilerinin ve kalluslarının uçucu yağ bileşenlerinin bir kıyaslaması
Authors: TÜRKER A. Haluk
Published: June 2023

Bu araştırma, Origanum syriacum var. bevanii taksonunun donör olarak kullanılan in vivo bitkileri ile in vitro koşullarda yetiştirilen rejenerant bitkileri ve kalluslarının uçucu yağ bileşenlerinin belirlenmesi ve kıyaslanması amacıyla yürütülmüştür. Doğal ortamından alınan çeliklerle yetiştirilen donör bitkiler ve bunlardan alınan çeşitli eksplantlarla farklı konsantrasyonlarda bitki büyüme düzenleyicileri ilave edilen in vitro ortamda yetiştirilen rejenerant bitkiler ve kallusların uçucu yağ bileşenleri GC/MS (Gaz Kromatografisi - Kütle Spektrometrisi) cihazı ile belirlenmiştir. Araştırma sonucunda, donör ve rejenerant bitkiler ve kallusların uçucu yağ bileşenlerinin birbirinden oldukça farklı olduğu belirlenmiştir. Ana bileşenler olarak, donör bitkilerde %36,28 Carvacrol ve %19,76 ortho-Cymene tespit edilmişken rejenerant bitkilerde %26,49 Carbamic Acid ve %19,67 Carvacrol tespit edilmiştir. Tamamen farklı olarak, kalluslardaki ana bileşenler %64,82 Ethanol, %57,23 1,2-Propanediol, %55,11 L(+)-Lactic acid, %53,53 Methyltartronic acid, %52,16 4-Penten-2-ol ve %38,47 2-Methoxycinnamic acid olarak tespit edilmiştir. Basit fenolik bileşiklerin oluşumunda başlangıç bileşenlerinden olan 2-Methoxycinnamic acid, sap boğumu eksplantından gelişen kallusta yaprak eksplantından gelişen diğer kalluslara göre en yüksek oranda bulunmakta (%38,4) ve ana bileşeni oluşturmaktadır. Dolayısıyla analiz sonuçlarına göre sap boğumu eksplantından gelişen kallusun sekonder metabolitlerin oluşumunda ümitvar sonuçlar verdiği tespit edilmiştir.
Korean Restaurants’ Consumer Needs Based on Marketing Mix Through the Kano Model
Authors: Zavira Caesarilla Maggie , Ismoyowati Dyah , Yuliando Henry
Published: June 2023

A proliferation of Korean restaurants in Yogyakarta can be seen over the past few years, each with its unique concept. However, a survey on Google Star Rating suggests that not all of these restaurants are sustainable. The significant discrepancy between expectations and actual satisfaction of consumers is likely a contributing factor. Moreover, intense competition exists between these restaurants. This study aims to identify which aspects of marketing—such as product, price, place, promotion, people, process, and physical evidence—influence Korean restaurants’ consumer needs. Using the Kano model, consumer satisfaction was analyzed by classifying consumer needs into attractive, must-be, one-dimensional, indifferent, reverse, or questionable categories. Purposive and snowball sampling methods were employed to select 100 respondents to complete a questionnaire. The results unveiled that four of the seven marketing mix attributes needed by Korean restaurants’ consumers fell into the one-dimensional category. The features consisted of physical evidence (clean dining room), people (clean and tidy employee appearance), product (good taste), and process (timely service). Furthermore, the attributes of unique offers (D1); fast service (F2); menu information, ingredients, how to cook it (E3); and affordable food prices (B1) contributed to greater consumer satisfaction. These attributes boosted consumer satisfaction by 80%, 79.8%, 74.5%, and 73.7%, respectively.