1,537 research outputs found
Large Magnetoresistance in Compensated Semimetals TaAs and NbAs
We report large magnetoresistance (MR) at low temperatures in
single-crystalline nonmagnetic compounds TaAs and NbAs. Both compounds
exhibit parabolic-field-dependent MR larger than 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 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 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 -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
- …