135 research outputs found
On Bayesian Oracle Properties
When model uncertainty is handled by Bayesian model averaging (BMA) or
Bayesian model selection (BMS), the posterior distribution possesses a
desirable "oracle property" for parametric inference, if for large enough data
it is nearly as good as the oracle posterior, obtained by assuming
unrealistically that the true model is known and only the true model is used.
We study the oracle properties in a very general context of quasi-posterior,
which can accommodate non-regular models with cubic root asymptotics and
partial identification. Our approach for proving the oracle properties is based
on a unified treatment that bounds the posterior probability of model
mis-selection. This theoretical framework can be of interest to Bayesian
statisticians who would like to theoretically justify their new model selection
or model averaging methods in addition to empirical results. Furthermore, for
non-regular models, we obtain nontrivial conclusions on the choice of prior
penalty on model complexity, the temperature parameter of the quasi-posterior,
and the advantage of BMA over BMS.Comment: 31 page
A Unified Framework for Testing High Dimensional Parameters: A Data-Adaptive Approach
High dimensional hypothesis test deals with models in which the number of
parameters is significantly larger than the sample size. Existing literature
develops a variety of individual tests. Some of them are sensitive to the dense
and small disturbance, and others are sensitive to the sparse and large
disturbance. Hence, the powers of these tests depend on the assumption of the
alternative scenario. This paper provides a unified framework for developing
new tests which are adaptive to a large variety of alternative scenarios in
high dimensions. In particular, our framework includes arbitrary hypotheses
which can be tested using high dimensional -statistic based vectors. Under
this framework, we first develop a broad family of tests based on a novel
variant of the -norm with . We then combine these
tests to construct a data-adaptive test that is simultaneously powerful under
various alternative scenarios. To obtain the asymptotic distributions of these
tests, we utilize the multiplier bootstrap for -statistics. In addition, we
consider the computational aspect of the bootstrap method and propose a novel
low-cost scheme. We prove the optimality of the proposed tests. Thorough
numerical results on simulated and real datasets are provided to support our
theory
Boosting Cross-Domain Speech Recognition with Self-Supervision
The cross-domain performance of automatic speech recognition (ASR) could be
severely hampered due to the mismatch between training and testing
distributions. Since the target domain usually lacks labeled data, and domain
shifts exist at acoustic and linguistic levels, it is challenging to perform
unsupervised domain adaptation (UDA) for ASR. Previous work has shown that
self-supervised learning (SSL) or pseudo-labeling (PL) is effective in UDA by
exploiting the self-supervisions of unlabeled data. However, these
self-supervisions also face performance degradation in mismatched domain
distributions, which previous work fails to address. This work presents a
systematic UDA framework to fully utilize the unlabeled data with
self-supervision in the pre-training and fine-tuning paradigm. On the one hand,
we apply continued pre-training and data replay techniques to mitigate the
domain mismatch of the SSL pre-trained model. On the other hand, we propose a
domain-adaptive fine-tuning approach based on the PL technique with three
unique modifications: Firstly, we design a dual-branch PL method to decrease
the sensitivity to the erroneous pseudo-labels; Secondly, we devise an
uncertainty-aware confidence filtering strategy to improve pseudo-label
correctness; Thirdly, we introduce a two-step PL approach to incorporate target
domain linguistic knowledge, thus generating more accurate target domain
pseudo-labels. Experimental results on various cross-domain scenarios
demonstrate that the proposed approach effectively boosts the cross-domain
performance and significantly outperforms previous approaches.Comment: Accepted by IEEE/ACM Transactions on Audio, Speech and Language
Processing (TASLP), 202
EmotionPrompt: Leveraging Psychology for Large Language Models Enhancement via Emotional Stimulus
Large language models (LLMs) have achieved significant performance in many
fields such as reasoning, language understanding, and math problem-solving, and
are regarded as a crucial step to artificial general intelligence (AGI).
However, the sensitivity of LLMs to prompts remains a major bottleneck for
their daily adoption. In this paper, we take inspiration from psychology and
propose EmotionPrompt to explore emotional intelligence to enhance the
performance of LLMs. EmotionPrompt operates on a remarkably straightforward
principle: the incorporation of emotional stimulus into prompts. Experimental
results demonstrate that our EmotionPrompt, using the same single prompt
templates, significantly outperforms original zero-shot prompt and
Zero-shot-CoT on 8 tasks with diverse models: ChatGPT, Vicuna-13b, Bloom, and
T5. Further, EmotionPrompt was observed to improve both truthfulness and
informativeness. We believe that EmotionPrompt heralds a novel avenue for
exploring interdisciplinary knowledge for humans-LLMs interaction.Comment: Work in progress; 9 page
Phases and magnetism at the microscale in compounds containing nominal Pb10-xCux(PO4)6O
Achieving superconductivity at room temperature could lead to substantial
advancements in industry and technology. Recently, a compound known as Cu-doped
lead-apatite, Pb10-xCux(PO4)6O (0.9 < x < 1.1), referred to as "LK-99", has
been reported to exhibit unusual electrical and magnetic behaviors that appear
to resemble a superconducting transition above room temperature. In this work
we collected multiphase samples containing the nominal Pb10-xCux(PO4)6O phase
(no superconductivity observed in our measured samples), synthesized by three
independent groups, and studied their chemical, magnetic, and electrical
properties at the microscale to overcome difficulties in bulk measurements.
Through the utilization of optical, scanning electron, atomic force, and
scanning diamond nitrogen-vacancy microscopy techniques, we are able to
establish a link between local magnetic properties and specific microscale
chemical phases. Our findings indicate that while the Pb10-xCux(PO4)6O phase
seems to have a mixed magnetism contribution, a significant fraction of the
diamagnetic response can be attributed to Cu-rich regions (e.g., Cu2S derived
from a reagent used in the synthesis). Additionally, our electrical
measurements reveal the phenomenon of current path switch and a change in
resistance states of Cu2S. This provides a potential explanation for the
electrical behavior observed in compounds related to Pb10-xCux(PO4)6O.Comment: 14 pages, 5 figures; Physical Review Material
Comparison between the influence of roxadustat and recombinant human erythropoietin treatment on blood pressure and cardio-cerebrovascular complications in patients undergoing peritoneal dialysis
IntroductionRoxadustat treatment in PD patients is equivalent to ESAs in increasing hemoglobin (Hb). But blood pressure, cardiovascular parameters, cardio-cerebrovascular complications and prognosis in the two groups before and after treatment has not been sufficiently discussed.MethodsSixty PD patients who were treated with roxadustat for renal anemia in our PD center recruited from June 2019 to April 2020 as roxadustat group. PD patients treated with rHuEPO were enrolled at a 1:1 ratio as rHuEPO group using the method of propensity score matching. Hb, blood pressure, cardiovascular parameters, cardio-cerebrovascular complications and prognosis were compared between the two group. All patients were followed up for at least 24 months.ResultsThere were no significant differences in baseline clinical data or laboratory values between roxadustat group and rHuEPO group. After 24 months of follow-up, there was no significant difference in Hb levels (p > 0.05). There were no significant changes in blood pressure, or the incidence of nocturnal hypertension before and after treatment in roxadustat group (p > 0.05), while blood pressure significantly increased in rHuEPO group after treatment (p < 0.05). Compared with roxadustat group after follow-up, rHuEPO group had a higher incidence of hypertension, the levels of cardiovascular parameters were worse and cardio-cerebrovascular complications had a higher incidence (p < 0.05). Cox regression analysis showed age, systolic blood pressure, fasting blood glucose, and rHuEPO use before baseline were risk factors for cardio-cerebrovascular complications in PD patients, while treatment with roxadustat was a protective factor for cardiovascular and cerebrovascular complications.ConclusionCompared with rHuEPO, roxadustat had less influence on blood pressure or cardiovascular parameters, and it was associated with a lower risk of cardio-cerebrovascular complications in patients undergoing PD. Roxadustat has a cardio-cerebrovascular protective advantage in PD patients with renal anemia
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