Miracle-dz/UniFAR-FTUs huggingface dataset:
RSVD-CUR Decomposition for Matrix Triplets

We propose a restricted SVD based CUR (RSVD-CUR) decomposition for matrix triplets ( A, B, G ). Given matrices A , B , and G of compatible dimensions, such a decomposition provides a coordinated low-rank approximation of the three matrices using a subset of their rows and columns. We pick the subset of rows and columns of the original matrices by applying either the discrete empirical interpolation method (DEIM) or the L-DEIM scheme on the orthogonal and nonsingular matrices from the restricted singular value decomposition of the matrix triplet. We investigate the connections between a DEIM type RSVD-CUR approximation and a DEIM type CUR factorization, and a DEIM type generalized CUR decomposition. We provide an error analysis that shows that the accuracy of the proposed RSVD-CUR decomposition is within a factor of the approximation error of the restricted singular value decomposition of given matrices. An RSVD-CUR factorization may be suitable for applications where we are interested in approximating one data matrix relative to two other given matrices. Two applications that we discuss include multi-view/label dimension reduction, and data perturbation problems of the form A E = A + BFG , where BFG is a nonwhite noise matrix. In numerical experiments, we show the advantages of the new method over the standard CUR approximation for these applications.
Domain-Aware Contrastive Knowledge Transfer for Multi-domain Imbalanced Data

In many real-world machine learning applications, samples belong to a set of domains e.g., for product reviews each review belongs to a product category. In this paper, we study multi-domain imbalanced learning (MIL), the scenario that there is imbalance not only in classes but also in domains. In the MIL setting, different domains exhibit different patterns and there is a varying degree of similarity and divergence among domains posing opportunities and challenges for transfer learning especially when faced with limited or insufficient training data.We propose a novel domain-aware contrastive knowledge transfer method called DCMI to (1) identify the shared domain knowledge to encourage positive transfer among similar domains (in particular from head domains to tail domains); (2) isolate the domain-specific knowledge to minimize the negative transfer from dissimilar domains. We evaluated the performance of DCMI on three different datasets showing significant improvements in different MIL scenarios.
Modeling COVID-19 optimal testing strategies in long-term care facilities: An optimization-based approach (preprint)

Long-term care facilities have been widely affected by the COVID-19 pandemic. Retirement homes are particularly vulnerable due to the higher mortality risk of infected elderly individuals. Once an outbreak occurs, suppressing the spread of the virus in retirement homes is challenging because the residents are in contact with each other, and isolation measures cannot be widely enforced. Regular testing strategies, on the other hand, have been shown to effectively prevent outbreaks in retirement homes. However, high frequency testing may consume substantial staff working time, which results in a trade-off between the time invested in testing, and the time spent providing essential care to residents. Thus, developing an optimal testing strategy is crucial to proactively detect infections while guaranteeing efficient use of limited staff time in these facilities. Although numerous efforts have been made to prevent the virus from spreading in long-term care facilities, this is the first study to develop testing strategies based on formal optimization methods. This paper proposes two novel optimization models for testing schedules. The models aim to minimize the risk of infection in retirement homes, considering the trade-off between the probability of infection and staff workload. We employ a probabilistic approach in conjunction with the optimization models, to compute the risk of infection, including contact rates, incidence status, and the probability of infection of the residents. To solve the models, we propose an enhanced local search algorithm by leveraging the symmetry property of the optimal solution. We perform several experiments with realistically sized instances and show that the proposed approach can derive optimal testing strategies.
GP-BART: a novel Bayesian additive regression trees approach using Gaussian processes

The Bayesian additive regression trees (BART) model is an ensemble method extensively and successfully used in regression tasks due to its consistently strong predictive performance and its ability to quantify uncertainty. BART combines"weak"tree models through a set of shrinkage priors, whereby each tree explains a small portion of the variability in the data. However, the lack of smoothness and the absence of an explicit covariance structure over the observations in standard BART can yield poor performance in cases where such assumptions would be necessary. The Gaussian processes Bayesian additive regression trees (GP-BART) model is an extension of BART which addresses this limitation by assuming Gaussian process (GP) priors for the predictions of each terminal node among all trees. The model's effectiveness is demonstrated through applications to simulated and real-world data, surpassing the performance of traditional modeling approaches in various scenarios.
Positive and Negative Critiquing for VAE-based Recommenders

Providing explanations for recommended items allows users to refine the recommendations by critiquing parts of the explanations. As a result of revisiting critiquing from the perspective of multimodal generative models, recent work has proposed M&Ms-VAE, which achieves state-of-the-art performance in terms of recommendation, explanation, and critiquing. M&Ms-VAE and similar models allow users to negatively critique (i.e., explicitly disagree). However, they share a significant drawback: users cannot positively critique (i.e., highlight a desired feature). We address this deficiency with M&Ms-VAE+, an extension of M&Ms-VAE that enables positive and negative critiquing. In addition to modeling users' interactions and keyphrase-usage preferences, we model their keyphrase-usage dislikes. Moreover, we design a novel critiquing module that is trained in a self-supervised fashion. Our experiments on two datasets show that M&Ms-VAE+ matches or exceeds M&Ms-VAE in recommendation and explanation performance. Furthermore, our results demonstrate that representing positive and negative critiques differently enables M&Ms-VAE+ to significantly outperform M&Ms-VAE and other models in positive and negative multi-step critiquing.
QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration

The significant success of Deep Neural Networks (DNNs) is highly promoted by the multiple sophisticated DNN libraries. On the contrary, although some work have proved that Quadratic Deep Neuron Networks (QDNNs) show better non-linearity and learning capability than the first-order DNNs, their neuron design suffers certain drawbacks from theoretical performance to practical deployment. In this paper, we first proposed a new QDNN neuron architecture design, and further developed QuadraLib, a QDNN library to provide architecture optimization and design exploration for QDNNs. Extensive experiments show that our design has good performance regarding prediction accuracy and computation consumption on multiple learning tasks.
Scalable tube model predictive control of uncertain linear systems using ellipsoidal sets

This work proposes a novel robust model predictive control (MPC) algorithm for linear systems affected by dynamic model uncertainty and exogenous disturbances. The uncertainty is modeled using a linear fractional perturbation structure with a time-varying perturbation matrix, enabling the algorithm to be applied to a large model class. The MPC controller constructs a state tube as a sequence of parameterized ellipsoidal sets to bound the state trajectories of the system. The proposed approach results in a semidefinite program to be solved online, whose size scales linearly with the order of the system. The design of the state tube is formulated as an offline optimization problem, which offers flexibility to impose desirable features such as robust invariance on the terminal set. This contrasts with most existing tube MPC strategies using polytopic sets in the state tube, which are difficult to design and whose complexity grows combinatorially with the system order. The algorithm guarantees constraint satisfaction, recursive feasibility, and stability of the closed loop. The advantages of the algorithm are demonstrated using two simulation studies.
On the Computational Consequences of Cost Function Design in Nonlinear Optimal Control

Optimal control is an essential tool for stabilizing complex nonlinear systems. However, despite the extensive impacts of methods such as receding horizon control, dynamic programming and reinforcement learning, the design of cost functions for a particular system often remains a heuristic-driven process of trial and error. In this paper we seek to gain insights into how the choice of cost function interacts with the underlying structure of the control system and impacts the amount of computation required to obtain a stabilizing controller. We treat the cost design problem as a two-step process where the designer specifies outputs for the system that are to be penalized and then modulates the relative weighting of the inputs and the outputs in the cost. To characterize the computational burden associated to obtaining a stabilizing controller with a particular cost, we bound the prediction horizon required by receding horizon methods and the number of iterations required by dynamic programming methods to meet this requirement. Our theoretical results highlight a qualitative separation between what is possible, from a design perspective, when the chosen outputs induce either minimum-phase or non-minimum-phase behavior. Simulation studies indicate that this separation also holds for modern reinforcement learning methods.
MoEfication: Transformer Feed-forward Layers are Mixtures of Experts

Recent work has shown that feed-forward networks (FFNs) in pre-trained Transformers are a key component, storing various linguistic and factual knowledge. However, the computational patterns of FFNs are still unclear. In this work, we study the computational patterns of FFNs and observe that most inputs only activate a tiny ratio of neurons of FFNs. This phenomenon is similar to the sparsity of the human brain, which drives research on functional partitions of the human brain. To verify whether functional partitions also emerge in FFNs, we propose to convert a model into its MoE version with the same parameters, namely MoEfication. Specifically, MoEfication consists of two phases: (1) splitting the parameters of FFNs into multiple functional partitions as experts, and (2) building expert routers to decide which experts will be used for each input. Experimental results show that MoEfication can conditionally use 10% to 30% of FFN parameters while maintaining over 95% original performance for different models on various downstream tasks. Besides, MoEfication brings two advantages: (1) it significantly reduces the FLOPS of inference, i.e., 2x speedup with 25% of FFN parameters, and (2) it provides a fine-grained perspective to study the inner mechanism of FFNs. The source code of this paper can be obtained from https://github.com/thunlp/MoEfication.
Safety and Efficacy of the New Combination Iron Chelation Regimens in Patients with Transfusion-Dependent Thalassemia and Severe Iron Overload

The aim of this study is the evaluation of the safety and the efficacy of long-term combination therapy deferasirox plus desferrioxamine and deferasirox plus deferiprone in a large group of transfusion-dependent thalassemia patients with high values of serum ferritin and/or magnetic resonance, indicative of severe liver and cardiac iron accumulation. Sixteen adults with transfusion-dependent thalassemia were treated simultaneously with deferasirox plus desferrioxamine, while another 42 patients (seven children) were treated with deferasirox plus deferiprone. The hepatic and cardiac iron overload was assessed prior to treatment and then annually with magnetic resonance imaging, and the serum ferritin was measured monthly. Adverse events were checked at each transfusion visit. The safety of both the combinations was consistent with established monotherapies. Both treatments were able to decrease the serum ferritin and liver iron concentration over time, depending on the level of compliance with therapy. Cardiac iron measured as R2* did not significantly change in patients treated with deferasirox plus desferrioxamine. Most patients with MRI indicative of myocardial siderosis at the beginning of treatment reached normal values of cardiac iron at the last determination if treated with deferasirox plus desferrioxamine. The greatest limitation of these therapies was low patient adherence to the two drugs, which is not surprising considering that the need for an intensive chelation is generally linked to previous issues of compliance.
Assessment of Salinity Tolerance Deploying Antioxidant Defense Systems in Gerbera Jamesonii

Inconsistency in the environment exposes plants to various abiotic stresses. This results in damage of a plant’s cellular components due to excessive accumulation of unstable reactive oxygen. Besides, it also disrupts enzymatic/non-enzymatic detoxification mechanisms in plants making them more sensitive. Salinity is one such abiotic stress which disrupts regular physiological mechanisms in plants. In this study, we examined the effects of salinity using NaCl in four different genotypes of Gerbera jamesonii cv Bolus, an important ornamental plant of family Compositae. We hypothesized that, upon treatment with NaCl (50, 100, 150 200mM concentration), alterations in the morphological features along with elevated levels of H2O2, MDA, proline, and degradation of chlorophyll will be observed. The enzymatic antioxidant defenses were also hypothesized to differ among genotypes based on their level of tolerance. These parameters were monitored on the 5th and 20th day of NaCl treatment and results were recorded. The observations suggest that 1. the Lattara genotype of Gerbera is sensitive to NaCl and 2. Faith is tolerant, while 3. Alcatras and Basic are moderately tolerant. These findings accompanied by further research on the physiological parameters responsible for attaining salinity tolerance may help in developing salt-tolerant varieties in Gerbera. Future studies on decoding molecular networks associated with the antioxidative defense system in Gerbera can help improve breeding and create novel germplasm in various ornamentally important plants besides Gerbera.
Party on dude, but not if you’re a top academic achieving student: how being named a top party school changes the academic profile of a university

ABSTRACT This study examines how being named the top party school in the nation by the Princeton Review effects the quality of students at a university. The results indicate that being named the top party school lowers the number of top-tier students who choose to attend the university as measured by academic test scores. The study further finds that being named a top party school has no effect on student applications; however, this designation does lower the number of students who are admitted and who chose to enroll after the institution is named the top party school. These findings suggest that the publicity of being named the top party school enhances a school’s undesired reputation, thereby influencing student enrollment decisions, particularly among top-tier students.
A Web-based IT Framework for Campus Innovation

with the rapid development of Information and Communication Technology (ICT), new technologies are changing the society. However, current situation of education in ICT technologies focus too much on the theoretical but ignores the practice, which does not dig out the potential of innovation. While, actually, a lot of ICT technologies are researched and utilized in campus where have a favorable environment and enough resources. Thus, constructing digital campus and an open platform for student to innovate and learn new technologies are quite necessary. Therefore, this paper analyzes the requirements for an open innovation platform and put forward a set of design principles. According to the principle, we propose a web-based IT framework for campus innovation platform. Then some innovation and education use cases are also evaluated and discussed based on the platform.
OPINION MINING AND SEMANTIC EXTRACTION IN E-GOVT- SMART-G: A TEXT MINING APPROACH.

SMART-G refers to the Simple, Modular, Adaptive, Reliable, and Transparent Govt. Information and communication technology has a greater chance to enhance the method by that governments can involve individual in formulation of public policy and Rules. Nowadays m ost of the government rules could currently be in digital type (and typically obtainable online), thanks to their complexity and variety, differentiating those relevant to a selected context may be a non-trivial task. Similarly, with the existence of variety of electronic on-line blogs, social networking sites and forums, the chance of gathering people‟ petitions and stakeholders‟ views on government policy and proposals has increased to a greater extent, however the amount and therefore the complexness of analyzing unstructured knowledge makes the job tougher. O n the other hand, text mining has come back a protracted manner straightforward keyword search, and come up with a discipline capable of addressing way more advanced tasks. Here in this paper we discuss and explain how text-mining techniques can help in retrieval of information and relationships from text data sources, further which assist policy makers in discovering associations between policies and people’ views expressed in electronic public forums and blogs etc. We also discuss here, an integrated text mining based SMART-G architecture for egovernance decision support in addition to a discussion on the Indian scenario.
A software defect prediction method with metric compensation based on feature selection and transfer learning

Cross-project software defect prediction solves the problem of insufficient training data for traditional defect prediction, and overcomes the challenge of applying models learned from multiple different source projects to target project. At the same time, two new problems emerge: (1) too many irrelevant and redundant features in the model training process will affect the training efficiency and thus decrease the prediction accuracy of the model; (2) the distribution of metric values will vary greatly from project to project due to the development environment and other factors, resulting in lower prediction accuracy when the model achieves cross-project prediction. In the proposed method, the Pearson feature selection method is introduced to address data redundancy, and the metric compensation based transfer learning technique is used to address the problem of large differences in data distribution between the source project and target project. In this paper, we propose a software defect prediction method with metric compensation based on feature selection and transfer learning. The experimental results show that the model constructed with this method achieves better results on area under the receiver operating characteristic curve (AUC) value and F1-measure metric.
Broadband circularly polarized monopole antenna with modified characteristics

In this letter, a novel wideband circularly polarized (CP) monopole antenna (CPMA) with modified characteristics is proposed. The CPMA comprises a rectangular ground with an L‐shaped strip, an inverted L‐shaped monopole, and an asymmetric U‐shaped strip as a parasitic element. Note that the proposed CPMA is fed by a coaxial probe instead of the traditional microstrip feed. The L‐shaped strip in the rectangular‐ground and parasitic asymmetric U‐shaped strip are used simultaneously to excite multiple CP resonant modes, which increase the CPMA's CP bandwidth. Both the simulated and measured results validate the proposed design. The proposed CPMA features a −10‐dB impedance bandwidth ranging from 2.06 to 4.05 GHz (65.1% centered at 3.055 GHz), and a wide 3‐dB axial ratio bandwidth ranging from 1.78 to 4.44 GHz (85.5% centered at 3.11 GHz), which has a potential application in the satellite digital audio radio service system.
Experimental techniques and terminology in gas‐phase ion spectroscopy

Abstract This perspective gives an overview of the action spectroscopy methods for measurements of electronic, vibrational, and rotational spectra of mass‐selected ions in the gas phase. We classify and give a short overview of the existing experimental approaches in this field. There is currently a plethora of names used for, essentially, the same techniques. Hence within this overview, we scrutinized the notations and suggested terms to be generally used. The selection was either driven by making the name unique and straightforward or the term being the most broadly used one. We believe that a simplification and a unification of the notation in ion spectroscopy can make this field better accessible for experts outside the mass spectrometry community where the applications of gas‐phase action ion spectroscopy can make a large impact.
Naming the Identified Feature Implementation Blocks from Software Source Code

—Identifying software identifiers that implement a particular feature of a software product is known as feature identification. Feature identification is one of the most critical and popular processes performed by software engineers during software maintenance activity. However, a meaningful name must be assigned to the Identified Feature Implementation Block (IFIB) to complete the feature identification process. The feature naming process remains a challenging task, where the majority of existing approaches manually assign the name of the IFIB. In this paper, the approach called FeatureClouds was proposed, which can be exploited by software developers to name the IFIBs from software code. FeatureClouds approach incorporates word clouds visualization technique to name Feature Blocks (FBs) by using the most frequent words across these blocks. FeatureClouds had evaluated by assessing its added benefit to the current approaches in the literature, where limited tool support was supplied to software developers to distinguish feature names of the IFIBs. For validity, FeatureClouds had applied to draw shapes and ArgoUML software. The findings showed that the proposed approach achieved promising results according to well-known metrics in terms of Precision and Recall.