215 research outputs found
Vortex-enabled Andreev processes in quantum Hall-superconductor hybrids
Quantum Hall-superconductor heterostructures provide possible platforms for
intrinsically fault-tolerant quantum computing. Motivated by several recent
experiments that successfully integrated these phases, we investigate transport
through a proximitized integer quantum Hall edge--paying particular attention
to the impact of vortices in the superconductor. By examining the downstream
conductance, we identify regimes in which sub-gap vortex levels mediate Andreev
processes that would otherwise be frozen out in a vortex-free setup. Moreover,
we show that at finite temperature, and in the limit of a large number of
vortices, the downstream conductance can average to zero, indicating that the
superconductor effectively behaves like a normal contact. Our results highlight
the importance of considering vortices when using transport measurements to
study superconducting correlations in quantum Hall-superconductor hybrids.Comment: 16 pages, 9 figure
Reset The Boundary: State Activism in Juvenile Transfer Reform
Law and policy are deeply intertwined. States themselves are the main venues to deliberate and implement policies that alter the status quo of juvenile transfer. The policymaking process in some states can increase our ability to understand and predict how others will similarly react. This learning model has been the foundation for juvenile justice reform where lessons are drawn from past successes or failures to keep more youths from incarceration. Legislative and judicial capacity to influence criminal justice reforms are complementary, and there is an ongoing debate on the designated function of the judiciary, whether it should be more active or reserved in front of the high-stakes cases presented to them. My thesis, at least within the context it examines, argues that judicial activism is needed in juvenile transfer reform.
Today, all states allow juveniles to be tried as adults for committing certain serious crimes, such as murder, armed robbery, drug trafficking, and so forth. Juveniles can be transferred to criminal court via three pathways: judicial waiver, prosecutorial direct file, and statutory exclusion. By their literal meanings, a judge, a prosecutor, or the state legislature can make decisions about whom could be waived from juvenile court proceedings. The focus of my research is on the last two pathways because they do not require a hearing to determine a youth\u27s eligibility to be tried as an adult. The U.S. Supreme Court in Kent v. United States ruled that every youth falling within the original juvenile court jurisdiction must be afforded a formal discretionary waiver hearing with a counsel present before the youth could be waived to adult court for criminal prosecution. My research explains why eighteen states have overhauled their juvenile transfer systems, but a few others have not seen the fruition of reform efforts. I focus on two types of legislative initiatives: one is Raise the Age (raising the upper age boundary of the juvenile court); the other is limiting the criminal prosecution of certain offenses. I test the theories explaining principal determinants in shaping criminal laws and correctional policies, using the context of juvenile transfer
Detecting outlier patterns with query-based artificially generated searching conditions
In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this article, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between the nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined in a real-world academic network using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs and is robust to the choice of similarity measures. © 2014 IEEE
Detecting Outlier Patterns with Query-based Artificially Generated Searching Conditions
In the age of social computing, finding interesting network patterns or
motifs is significant and critical for various areas such as decision
intelligence, intrusion detection, medical diagnosis, social network analysis,
fake news identification, national security, etc. However, sub-graph matching
remains a computationally challenging problem, let alone identifying special
motifs among them. This is especially the case in large heterogeneous
real-world networks. In this work, we propose an efficient solution for
discovering and ranking human behavior patterns based on network motifs by
exploring a user's query in an intelligent way. Our method takes advantage of
the semantics provided by a user's query, which in turn provides the
mathematical constraint that is crucial for faster detection. We propose an
approach to generate query conditions based on the user's query. In particular,
we use meta paths between nodes to define target patterns as well as their
similarities, leading to efficient motif discovery and ranking at the same
time. The proposed method is examined on a real-world academic network, using
different similarity measures between the nodes. The experiment result
demonstrates that our method can identify interesting motifs, and is robust to
the choice of similarity measures
Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization
Uplift modeling has shown very promising results in online marketing.
However, most existing works are prone to the robustness challenge in some
practical applications. In this paper, we first present a possible explanation
for the above phenomenon. We verify that there is a feature sensitivity problem
in online marketing using different real-world datasets, where the perturbation
of some key features will seriously affect the performance of the uplift model
and even cause the opposite trend. To solve the above problem, we propose a
novel robustness-enhanced uplift modeling framework with adversarial feature
desensitization (RUAD). Specifically, our RUAD can more effectively alleviate
the feature sensitivity of the uplift model through two customized modules,
including a feature selection module with joint multi-label modeling to
identify a key subset from the input features and an adversarial feature
desensitization module using adversarial training and soft interpolation
operations to enhance the robustness of the model against this selected subset
of features. Finally, we conduct extensive experiments on a public dataset and
a real product dataset to verify the effectiveness of our RUAD in online
marketing. In addition, we also demonstrate the robustness of our RUAD to the
feature sensitivity, as well as the compatibility with different uplift models
Robust Transceiver Design for Covert Integrated Sensing and Communications With Imperfect CSI
We propose a robust transceiver design for a covert integrated sensing and
communications (ISAC) system with imperfect channel state information (CSI).
Considering both bounded and probabilistic CSI error models, we formulate
worst-case and outage-constrained robust optimization problems of joint
trasceiver beamforming and radar waveform design to balance the radar
performance of multiple targets while ensuring communications performance and
covertness of the system. The optimization problems are challenging due to the
non-convexity arising from the semi-infinite constraints (SICs) and the coupled
transceiver variables. In an effort to tackle the former difficulty,
S-procedure and Bernstein-type inequality are introduced for converting the
SICs into finite convex linear matrix inequalities (LMIs) and second-order cone
constraints. A robust alternating optimization framework referred to
alternating double-checking is developed for decoupling the transceiver design
problem into feasibility-checking transmitter- and receiver-side subproblems,
transforming the rank-one constraints into a set of LMIs, and verifying the
feasibility of beamforming by invoking the matrix-lifting scheme. Numerical
results are provided to demonstrate the effectiveness and robustness of the
proposed algorithm in improving the performance of covert ISAC systems
Not All Metrics Are Guilty: Improving NLG Evaluation with LLM Paraphrasing
Most research about natural language generation (NLG) relies on evaluation
benchmarks with limited references for a sample, which may result in poor
correlations with human judgements. The underlying reason is that one semantic
meaning can actually be expressed in different forms, and the evaluation with a
single or few references may not accurately reflect the quality of the model's
hypotheses. To address this issue, this paper presents a novel method, named
Para-Ref, to enhance existing evaluation benchmarks by enriching the number of
references. We leverage large language models (LLMs) to paraphrase a single
reference into multiple high-quality ones in diverse expressions. Experimental
results on representative NLG tasks of machine translation, text summarization,
and image caption demonstrate that our method can effectively improve the
correlation with human evaluation for sixteen automatic evaluation metrics by
+7.82% in ratio. We release the code and data at
https://github.com/RUCAIBox/Para-Ref
Vanishing of the anomalous Hall effect and enhanced carrier mobility in the spin-gapless ferromagnetic Mn2CoGa1-xAlx alloys
Spin gapless semiconductor (SGS) has attracted long attention since its
theoretical prediction, while concrete experimental hints are still lack in the
relevant Heusler alloys. Here in this work, by preparing the series alloys of
Mn2CoGa1-xAlx (x=0, 0.25, 0.5, 0.75 and 1), we identified the vanishing of
anomalous Hall effect in the ferromagnetic Mn2CoGa (or x=0.25) alloy in a wide
temperature interval, accompanying with growing contribution from the ordinary
Hall effect. As a result, comparatively low carrier density (1020 cm-3) and
high carrier mobility (150 cm2/Vs) are obtained in Mn2CoGa (or x=0.25) alloy in
the temperature range of 10-200K. These also lead to a large dip in the related
magnetoresistance at low fields. While in high Al content, despite the
magnetization behavior is not altered significantly, the Hall resistivity is
instead dominated by the anomalous one, just analogous to that widely reported
in Mn2CoAl. The distinct electrical transport behavior of x=0 and x=0.75 (or 1)
is presently understood by their possible different scattering mechanism of the
anomalous Hall effect due to the differences in atomic order and conductivity.
Our work can expand the existing understanding of the SGS properties and offer
a better SGS candidate with higher carrier mobility that can facilitate the
application in the spin-injected related devices
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