Large Magnetoresistance in Compensated Semimetals TaAs2_2 and NbAs2_2

We report large magnetoresistance (MR) at low temperatures in single-crystalline nonmagnetic compounds TaAs$_2$ and NbAs$_2$. Both compounds exhibit parabolic-field-dependent MR larger than $5\times10^3$ in a magnetic field of 9 Tesla at 2 K. The MR starts to deviate from parabolic dependence above 10 T and intends to be saturated in 45 T for TaAs$_2$ at 4.2 K. The Hall resistance measurements and band structural calculations reveal their compensated semimetal characteristics. The large MR at low temperatures is ascribed to a resonance effect of the balanced electrons and holes with large mobilities. We also discuss the relation of the MR and samples' quality for TaAs$_2$ and other semimetals. We found that the magnitudes of MR are strongly dependent on the samples' quality for different compounds.Comment: 26 pages, 11 figures, 2 table

Parameterized Complexity of Multi-winner Determination: More Effort Towards Fixed-Parameter Tractability

We study the parameterized complexity of Winners Determination for three prevalent $k$-committee selection rules, namely the minimax approval voting (MAV), the proportional approval voting (PAV), and the Chamberlin-Courant's approval voting (CCAV). It is known that Winners Determination for these rules is NP-hard. Moreover, these problems have been studied from the parameterized complexity point of view with respect to some natural parameters recently. However, many results turned out to be W[1]-hard or W[2]-hard. Aiming at deriving more fixed-parameter algorithms, we revisit these problems by considering more natural and important single parameters, combined parameters, and structural parameters.Comment: 31 pages, 2 figures, AAMAS 201

ResumeNet: A Learning-based Framework for Automatic Resume Quality Assessment

Recruitment of appropriate people for certain positions is critical for any companies or organizations. Manually screening to select appropriate candidates from large amounts of resumes can be exhausted and time-consuming. However, there is no public tool that can be directly used for automatic resume quality assessment (RQA). This motivates us to develop a method for automatic RQA. Since there is also no public dataset for model training and evaluation, we build a dataset for RQA by collecting around 10K resumes, which are provided by a private resume management company. By investigating the dataset, we identify some factors or features that could be useful to discriminate good resumes from bad ones, e.g., the consistency between different parts of a resume. Then a neural-network model is designed to predict the quality of each resume, where some text processing techniques are incorporated. To deal with the label deficiency issue in the dataset, we propose several variants of the model by either utilizing the pair/triplet-based loss, or introducing some semi-supervised learning technique to make use of the abundant unlabeled data. Both the presented baseline model and its variants are general and easy to implement. Various popular criteria including the receiver operating characteristic (ROC) curve, F-measure and ranking-based average precision (AP) are adopted for model evaluation. We compare the different variants with our baseline model. Since there is no public algorithm for RQA, we further compare our results with those obtained from a website that can score a resume. Experimental results in terms of different criteria demonstrate the effectiveness of the proposed method. We foresee that our approach would transform the way of future human resources management.Comment: ICD

Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbations

Counterfactual explanations (CFEs) exemplify how to minimally modify a feature vector to achieve a different prediction for an instance. CFEs can enhance informational fairness and trustworthiness, and provide suggestions for users who receive adverse predictions. However, recent research has shown that multiple CFEs can be offered for the same instance or instances with slight differences. Multiple CFEs provide flexible choices and cover diverse desiderata for user selection. However, individual fairness and model reliability will be damaged if unstable CFEs with different costs are returned. Existing methods fail to exploit flexibility and address the concerns of non-robustness simultaneously. To address these issues, we propose a conceptually simple yet effective solution named Counterfactual Explanations with Minimal Satisfiable Perturbations (CEMSP). Specifically, CEMSP constrains changing values of abnormal features with the help of their semantically meaningful normal ranges. For efficiency, we model the problem as a Boolean satisfiability problem to modify as few features as possible. Additionally, CEMSP is a general framework and can easily accommodate more practical requirements, e.g., casualty and actionability. Compared to existing methods, we conduct comprehensive experiments on both synthetic and real-world datasets to demonstrate that our method provides more robust explanations while preserving flexibility.Comment: Accepted by CIKM 202