419 research outputs found
Simulating large-size quantum spin chains on cloud-based superconducting quantum computers
Quantum computers have the potential to efficiently simulate large-scale
quantum systems for which classical approaches are bound to fail. Even though
several existing quantum devices now feature total qubit numbers of more than
one hundred, their applicability remains plagued by the presence of noise and
errors. Thus, the degree to which large quantum systems can successfully be
simulated on these devices remains unclear. Here, we report on cloud
simulations performed on several of IBM's superconducting quantum computers to
simulate ground states of spin chains having a wide range of system sizes up to
one hundred and two qubits. We find that the ground-state energies extracted
from realizations across different quantum computers and system sizes reach the
expected values to within errors that are small (i.e. on the percent level),
including the inference of the energy density in the thermodynamic limit from
these values. We achieve this accuracy through a combination of
physics-motivated variational Ansatzes, and efficient, scalable
energy-measurement and error-mitigation protocols, including the use of a
reference state in the zero-noise extrapolation. By using a 102-qubit system,
we have been able to successfully apply up to 3186 CNOT gates in a single
circuit when performing gate-error mitigation. Our accurate, error-mitigated
results for random parameters in the Ansatz states suggest that a standalone
hybrid quantum-classical variational approach for large-scale XXZ models is
feasible.Comment: 21 pages, 12 figures, 4 tables; title change; substantial revisio
An Automatic Evaluation Framework for Multi-turn Medical Consultations Capabilities of Large Language Models
Large language models (LLMs) have achieved significant success in interacting
with human. However, recent studies have revealed that these models often
suffer from hallucinations, leading to overly confident but incorrect
judgments. This limits their application in the medical domain, where tasks
require the utmost accuracy. This paper introduces an automated evaluation
framework that assesses the practical capabilities of LLMs as virtual doctors
during multi-turn consultations. Consultation tasks are designed to require
LLMs to be aware of what they do not know, to inquire about missing medical
information from patients, and to ultimately make diagnoses. To evaluate the
performance of LLMs for these tasks, a benchmark is proposed by reformulating
medical multiple-choice questions from the United States Medical Licensing
Examinations (USMLE), and comprehensive evaluation metrics are developed and
evaluated on three constructed test sets. A medical consultation training set
is further constructed to improve the consultation ability of LLMs. The results
of the experiments show that fine-tuning with the training set can alleviate
hallucinations and improve LLMs' performance on the proposed benchmark.
Extensive experiments and ablation studies are conducted to validate the
effectiveness and robustness of the proposed framework.Comment: 10 pages, 9figure
Tianshengyuan-1 (TSY-1) regulates cellular Telomerase activity by methylation of TERT promoter.
Telomere and Telomerase have recently been explored as anti-aging and anti-cancer drug targets with only limited success. Previously we showed that the Chinese herbal medicine Tianshengyuan-1 (TSY-1), an agent used to treat bone marrow deficiency, has a profound effect on stimulating Telomerase activity in hematopoietic cells. Here, the mechanism of TSY-1 on cellular Telomerase activity was further investigated using HL60, a promyelocytic leukemia cell line, normal peripheral blood mononuclear cells, and CD34+ hematopoietic stem cells derived from umbilical cord blood. TSY-1 increases Telomerase activity in normal peripheral blood mononuclear cells and CD34+ hematopoietic stem cells with innately low Telomerase activity but decreases Telomerase activity in HL60 cells with high intrinsic Telomerase activity, both in a dose-response manner. Gene profiling analysis identified Telomerase reverse transcriptase (TERT) as the potential target gene associated with the TSY-1 effect, which was verified by both RT-PCR and western blot analysis. The β-galactosidase reporter staining assay showed that the effect of TSY-1 on Telomerase activity correlates with cell senescence. TSY-1 induced hypomethylation within TERT core promoter in HL60 cells but induced hypermethylation within TERT core promoter in normal peripheral blood mononuclear cells and CD34+ hematopoietic stem cells. TSY-1 appears to affect the Telomerase activity in different cell lines differently and the effect is associated with TERT expression, possibly via the methylation of TERT promoter
Room-temperature antiferromagnetic CrSe monolayer with tunable metal-insulator transition in ferroelectric heterostructures
Recently, there has been a rapidly growing interest in two-dimensional (2D)
transition metal chalcogenide monolayers (MLs) due to their unique magnetic and
electronic properties. By using an evolutionary algorithm and first-principles
calculations, we report the discovery of a previously unexplored, chemically,
energetically, and thermodynamically stable 2D antiferromagnetic (AFM) CrSe ML
with a N\'eel temperature higher than room temperature. Remarkably, we predict
an electric field-controllable metal-insulator transition (MIT) in a van der
Waals (vdW) heterostructure comprised of CrSe ML and ferroelectric Sc2CO2. This
tunable transition in CrSe/Sc2CO2 heterostructure is attributed to the change
in the band alignment between CrSe and Sc2CO2 caused by the ferroelectric
polarization reversal in Sc2CO2. Our findings suggest that 2D AFM CrSe ML has
important potential applications in AFM spintronics, particularly in the gate
voltage conducting channel.Comment: 13 Pages, 4 Figure
ReSup: Reliable Label Noise Suppression for Facial Expression Recognition
Because of the ambiguous and subjective property of the facial expression
recognition (FER) task, the label noise is widely existing in the FER dataset.
For this problem, in the training phase, current FER methods often directly
predict whether the label of the input image is noised or not, aiming to reduce
the contribution of the noised data in training. However, we argue that this
kind of method suffers from the low reliability of such noise data decision
operation. It makes that some mistakenly abounded clean data are not utilized
sufficiently and some mistakenly kept noised data disturbing the model learning
process. In this paper, we propose a more reliable noise-label suppression
method called ReSup (Reliable label noise Suppression for FER). First, instead
of directly predicting noised or not, ReSup makes the noise data decision by
modeling the distribution of noise and clean labels simultaneously according to
the disagreement between the prediction and the target. Specifically, to
achieve optimal distribution modeling, ReSup models the similarity distribution
of all samples. To further enhance the reliability of our noise decision
results, ReSup uses two networks to jointly achieve noise suppression.
Specifically, ReSup utilize the property that two networks are less likely to
make the same mistakes, making two networks swap decisions and tending to trust
decisions with high agreement. Extensive experiments on three popular
benchmarks show that the proposed method significantly outperforms
state-of-the-art noisy label FER methods by 3.01% on FERPlus becnmarks. Code:
https://github.com/purpleleaves007/FERDenois
bpftime: userspace eBPF Runtime for Uprobe, Syscall and Kernel-User Interactions
In kernel-centric operations, the uprobe component of eBPF frequently
encounters performance bottlenecks, largely attributed to the overheads borne
by context switches. Transitioning eBPF operations to user space bypasses these
hindrances, thereby optimizing performance. This also enhances configurability
and obviates the necessity for root access or privileges for kernel eBPF,
subsequently minimizing the kernel attack surface. This paper introduces
bpftime, a novel user-space eBPF runtime, which leverages binary rewriting to
implement uprobe and syscall hook capabilities. Through bpftime, userspace
uprobes achieve a 10x speed enhancement compared to their kernel counterparts
without requiring dual context switches. Additionally, this runtime facilitates
the programmatic hooking of syscalls within a process, both safely and
efficiently. Bpftime can be seamlessly attached to any running process,
limiting the need for either a restart or manual recompilation. Our
implementation also extends to interprocess eBPF Maps within shared memory,
catering to summary aggregation or control plane communication requirements.
Compatibility with existing eBPF toolchains such as clang and libbpf is
maintained, not only simplifying the development of user-space eBPF without
necessitating any modifications but also supporting CO-RE through BTF. Through
bpftime, we not only enhance uprobe performance but also extend the versatility
and user-friendliness of eBPF runtime in user space, paving the way for more
efficient and secure kernel operations
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