103 research outputs found
RAIN: Your Language Models Can Align Themselves without Finetuning
Large language models (LLMs) often demonstrate inconsistencies with human
preferences. Previous research gathered human preference data and then aligned
the pre-trained models using reinforcement learning or instruction tuning, the
so-called finetuning step. In contrast, aligning frozen LLMs without any extra
data is more appealing. This work explores the potential of the latter setting.
We discover that by integrating self-evaluation and rewind mechanisms,
unaligned LLMs can directly produce responses consistent with human preferences
via self-boosting. We introduce a novel inference method, Rewindable
Auto-regressive INference (RAIN), that allows pre-trained LLMs to evaluate
their own generation and use the evaluation results to guide backward rewind
and forward generation for AI safety. Notably, RAIN operates without the need
of extra data for model alignment and abstains from any training, gradient
computation, or parameter updates; during the self-evaluation phase, the model
receives guidance on which human preference to align with through a
fixed-template prompt, eliminating the need to modify the initial prompt.
Experimental results evaluated by GPT-4 and humans demonstrate the
effectiveness of RAIN: on the HH dataset, RAIN improves the harmlessness rate
of LLaMA 30B over vanilla inference from 82% to 97%, while maintaining the
helpfulness rate. Under the leading adversarial attack llm-attacks on Vicuna
33B, RAIN establishes a new defense baseline by reducing the attack success
rate from 94% to 19%
Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation
The ability to scene understanding in adverse visual conditions, e.g.,
nighttime, has sparked active research for RGB-Thermal (RGB-T) semantic
segmentation. However, it is essentially hampered by two critical problems: 1)
the day-night gap of RGB images is larger than that of thermal images, and 2)
the class-wise performance of RGB images at night is not consistently higher or
lower than that of thermal images. we propose the first test-time adaptation
(TTA) framework, dubbed Night-TTA, to address the problems for nighttime RGBT
semantic segmentation without access to the source (daytime) data during
adaptation. Our method enjoys three key technical parts. Firstly, as one
modality (e.g., RGB) suffers from a larger domain gap than that of the other
(e.g., thermal), Imaging Heterogeneity Refinement (IHR) employs an interaction
branch on the basis of RGB and thermal branches to prevent cross-modal
discrepancy and performance degradation. Then, Class Aware Refinement (CAR) is
introduced to obtain reliable ensemble logits based on pixel-level distribution
aggregation of the three branches. In addition, we also design a specific
learning scheme for our TTA framework, which enables the ensemble logits and
three student logits to collaboratively learn to improve the quality of
predictions during the testing phase of our Night TTA. Extensive experiments
show that our method achieves state-of-the-art (SoTA) performance with a 13.07%
boost in mIoU
Muon Flux Measurement at China Jinping Underground Laboratory
China Jinping Underground Laboratory (CJPL) is ideal for studying solar-,
geo-, and supernova neutrinos. A precise measurement of the cosmic-ray
background would play an essential role in proceeding with the R\&D research
for these MeV-scale neutrino experiments. Using a 1-ton prototype detector for
the Jinping Neutrino Experiment (JNE), we detected 264 high-energy muon events
from a 645.2-day dataset at the first phase of CJPL (CJPL-I), reconstructed
their directions, and measured the cosmic-ray muon flux to be
cms. The observed angular distributions indicate the leakage of
cosmic-ray muon background and agree with the simulation accounting for Jinping
mountain's terrain. A survey of muon fluxes at different laboratory locations
situated under mountains and below mine shaft indicated that the former is
generally a factor of larger than the latter with the same vertical
overburden. This study provides a convenient back-of-the-envelope estimation
for muon flux of an underground experiment
A two years longitudinal study of a transgenic Huntington disease monkey
BACKGROUND: A two-year longitudinal study composed of morphometric MRI measures and cognitive behavioral evaluation was performed on a transgenic Huntington’s disease (HD) monkey. rHD1, a transgenic HD monkey expressing exon 1 of the human gene encoding huntingtin (HTT) with 29 CAG repeats regulated by a human polyubiquitin C promoter was used together with four age-matched wild-type control monkeys. This is the first study on a primate model of human HD based on longitudinal clinical measurements. RESULTS: Changes in striatal and hippocampal volumes in rHD1 were observed with progressive impairment in motor functions and cognitive decline, including deficits in learning stimulus-reward associations, recognition memory and spatial memory. The results demonstrate a progressive cognitive decline and morphometric changes in the striatum and hippocampus in a transgenic HD monkey. CONCLUSIONS: This is the first study on a primate model of human HD based on longitudinal clinical measurements. While this study is based a single HD monkey, an ongoing longitudinal study with additional HD monkeys will be important for the confirmation of our findings. A nonhuman primate model of HD could complement other animal models of HD to better understand the pathogenesis of HD and future development of diagnostics and therapeutics through longitudinal assessment
Performance of the 1-ton Prototype Neutrino Detector at CJPL-I
China Jinping Underground Laboratory (CJPL) provides an ideal site for solar,
geo-, and supernova neutrino studies. With a prototype neutrino detector
running since 2017, containing 1-ton liquid scintillator (LS), we tested its
experimental hardware, performed the physics calibration, and measured its
radioactive backgrounds, as an early stage of the Jinping Neutrino Experiment
(JNE). We investigated the radon background and implemented the nitrogen
sealing technology to control it. This paper presents the details of these
studies and will serve as a key reference for the construction and optimization
of the future large detector at JNE
Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study
Summary
Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally.
Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies
have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of
the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income
countries globally, and identified factors associated with mortality.
Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to
hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis,
exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a
minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical
status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary
intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause,
in-hospital mortality for all conditions combined and each condition individually, stratified by country income status.
We did a complete case analysis.
Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital
diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal
malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome
countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male.
Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3).
Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income
countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups).
Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome
countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries;
p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients
combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11],
p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20
[1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention
(ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety
checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed
(ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of
parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65
[0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality.
Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome,
middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will
be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger
than 5 years by 2030
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