118 research outputs found
Subset Sampling and Its Extensions
This paper studies the \emph{subset sampling} problem. The input is a set
of records together with a function that assigns
each record a probability . A query returns a
random subset of , where each record is
sampled into independently with probability . The goal is to
store in a data structure to answer queries efficiently. If
fits in memory, the problem is interesting when is
dynamic. We develop a dynamic data structure with
expected \emph{query} time,
space and amortized expected \emph{update}, \emph{insert} and
\emph{delete} time, where
. The query time and
space are optimal. If does not fit in memory, the problem is
difficult even if is static. Under this scenario, we present an
I/O-efficient algorithm that answers a \emph{query} in
amortized expected I/Os using space, where is the memory
size, is the block size and is the number of iterative
operations we need to perform on before going below . In
addition, when each record is associated with a real-valued key, we extend the
\emph{subset sampling} problem to the \emph{range subset sampling} problem, in
which we require that the keys of the sampled records fall within a specified
input range . For this extension, we provide a solution under the
dynamic setting, with expected
\emph{query} time, space and amortized
expected \emph{update}, \emph{insert} and \emph{delete} time.Comment: 17 page
Thermodynamics of an Exactly Solvable Model for Superconductivity in a Doped Mott Insulator
Computing superconducting properties starting from an exactly solvable model
for a doped Mott insulator stands as a grand challenge. We have recently shown
that this can be done starting from the Hatsugai-Kohmoto (HK) model which can
be understood generally as the minimal model that breaks the non-local symmetry of a Fermi liquid, thereby constituting a new quartic fixed point
for Mott physics [Phillips et al., Nature Physics 16, 1175 (2020); Huang et
al., Nature Physics (2022)]. In the current work, we compute the
thermodynamics, condensation energy, and electronic properties such as the NMR
relaxation rate and ultrasonic attenuation rate. Key differences arise
with the standard BCS analysis from a Fermi liquid: 1) the free energy exhibits
a local minimum at where the pairing gap turns on discontinuously above a
critical value of the repulsive HK interaction, thereby indicating a
first-order transition, 2) a tri-critical point emerges, thereby demarcating
the boundary between the standard second-order superconducting transition and
the novel first-order regime, 3) Mottness changes the sign of the quartic
coefficient in the Landau-Ginzburg free-energy fuctional relative to that in
BCS, 4) as this obtains in the strongly interacting regime, it is Mott physics
that underlies the generic first-order transition, 5) the condensation energy
exceeds that in BCS theory suggesting that multiple Mott bands might be a way
of enhancing superconducting, 6) the heat-capacity jump is non-universal and
increases with the Mott scale, 7) Mottness destroys the Hebel-Slichter peak in
NMR, and 8) Mottness enhances the fall-off of the ultrasonic attenuation at the
pairing temperature . As several of these properties are observed in the
cuprates, our analysis here points a way forward in computing superconducting
properties of strongly correlated electron matter.Comment: accepted in PR
Dynamic Cycling of t-SNARE Acylation Regulates Platelet Exocytosis
Platelets regulate vascular integrity by secreting a host of molecules that promote hemostasis and its sequelae. Given the importance of platelet exocytosis, it is critical to understand how it is controlled. The t-SNAREs, SNAP-23 and syntaxin-11, lack classical transmembrane domains (TMDs), yet both are associated with platelet membranes and redistributed into cholesterol-dependent lipid rafts when platelets are activated. Using metabolic labeling and hydroxylamine (HA)/HCl treatment, we showed that both contain thioester-linked acyl groups. Mass spectrometry mapping further showed that syntaxin-11 was modified on cysteine 275, 279, 280, 282, 283, and 285, and SNAP-23 was modified on cysteine 79, 80, 83, 85, and 87. Interestingly, metabolic labeling studies showed incorporation of [3H]palmitate into the t-SNAREs increased although the protein levels were unchanged, suggesting that acylation turns over on the two t-SNAREs in resting platelets. Exogenously added fatty acids did compete with [3H]palmitate for t-SNARE labeling. To determine the effects of acylation, we measured aggregation, ADP/ATP release, as well as P-selectin exposure in platelets treated with the acyltransferase inhibitor cerulenin or the thioesterase inhibitor palmostatin B. We found that cerulenin pretreatment inhibited t-SNARE acylation and platelet function in a dose- and time-dependent manner whereas palmostatin B had no detectable effect. Interestingly, pretreatment with palmostatin B blocked the inhibitory effects of cerulenin, suggesting that maintaining the acylation state is important for platelet function. Thus, our work shows that t-SNARE acylation is actively cycling in platelets and suggests that the enzymes regulating protein acylation could be potential targets to control platelet exocytosis in vivo
Steroid saponins and other constituents from the rhizome of Trillium tschonoskii Maxim and their cytotoxic activity
Fourteen compounds were isolated from the rhizome of Trillium tschonoskii Maxim. By spectroscopic analysis, these compounds were established as Gracillin (1), Paris saponins V (2), Paris saponins VI (3), Paris saponins H (4), Paris saponins VII (5), (25R)-17α-hydroxy-5-en-3-O-a-L-arabinofuranosyl-(1→2)-β-Dglucopyranoside (6), (25R)-26-[β-D-glucopyanosyl]-17α,22β-dihydroxy-5-en-3-O-a-L-rhamnopyranosyl- (1→2)-β-D-glucopyranoside (7), Kaempferol-3-O-β-D-rutinoside (8), Quercetin (9), Quercetin-3-O-β-D-galactoside (10), Daucosterol (11), Stigmasterol-3-O-β-D-glucopyranoside (12), 3, 5-Di-O-caffeoyl quinic acid (13), and n-Hexadecanoic acid (14). By GC-MS analysis of the CH2Cl2 extract from Trillium tschonoskii Maxim, twenty compouns were identified, representing 91 % of the area. The cytotoxicity of compounds 1-14 on mouse A549 cells were evaluated.Colegio de Farmacéuticos de la Provincia de Buenos Aire
Exact solution for finite center-of-mass momentum Cooper pairing
Pair density waves (PDWs) are superconducting states formed by ``Cooper
pairs" of electrons containing a non-zero center-of-mass momentum. They are
characterized by a spatially modulated order parameter and may occur in a
variety of emerging quantum materials such as cuprates, transition metal
dichalcogenides (TMDs) and Kagome metals. Despite extensive theoretical and
numerical studies seeking PDWs in a variety of lattices and interacting
settings, there is currently no generic and robust mechanism that favors a
modulated solution of the superconducting order parameter in the presence of
time reversal symmetry. Here, we study the problem of two electrons subject to
an anisotropic (-wave) attractive potential. We solve the two-body
Schrodinger wave equation exactly to determine the pair binding energy as a
function of the center-of-mass momentum. We find that a modulated (finite
momentum) pair is favored over a homogeneous (zero momentum) solution above a
critical interaction. Using this insight from the exact two-body solution, we
construct a BCS-like variational many-body wave function and calculate the free
energy and superconducting gap as a function of the center-of-mass momentum. A
zero temperature analysis of the energy shows that the conclusions of the
two-body problem are robust in the many-body limit. Our results lay the
theoretical and microscopic foundation for the existence of PDWs.Comment: 24 pages, 6 figure
Steroid saponins and other constituents from the rhizome of Trillium tschonoskii Maxim and their cytotoxic activity
Fourteen compounds were isolated from the rhizome of Trillium tschonoskii Maxim. By spectroscopic analysis, these compounds were established as Gracillin (1), Paris saponins V (2), Paris saponins VI (3), Paris saponins H (4), Paris saponins VII (5), (25R)-17α-hydroxy-5-en-3-O-a-L-arabinofuranosyl-(1→2)-β-Dglucopyranoside (6), (25R)-26-[β-D-glucopyanosyl]-17α,22β-dihydroxy-5-en-3-O-a-L-rhamnopyranosyl- (1→2)-β-D-glucopyranoside (7), Kaempferol-3-O-β-D-rutinoside (8), Quercetin (9), Quercetin-3-O-β-D-galactoside (10), Daucosterol (11), Stigmasterol-3-O-β-D-glucopyranoside (12), 3, 5-Di-O-caffeoyl quinic acid (13), and n-Hexadecanoic acid (14). By GC-MS analysis of the CH2Cl2 extract from Trillium tschonoskii Maxim, twenty compouns were identified, representing 91 % of the area. The cytotoxicity of compounds 1-14 on mouse A549 cells were evaluated.Colegio de Farmacéuticos de la Provincia de Buenos Aire
AceGPT, Localizing Large Language Models in Arabic
This paper explores the imperative need and methodology for developing a
localized Large Language Model (LLM) tailored for Arabic, a language with
unique cultural characteristics that are not adequately addressed by current
mainstream models like ChatGPT. Key concerns additionally arise when
considering cultural sensitivity and local values. To this end, the paper
outlines a packaged solution, including further pre-training with Arabic texts,
supervised fine-tuning (SFT) using native Arabic instructions and GPT-4
responses in Arabic, and reinforcement learning with AI feedback (RLAIF) using
a reward model that is sensitive to local culture and values. The objective is
to train culturally aware and value-aligned Arabic LLMs that can serve the
diverse application-specific needs of Arabic-speaking communities.
Extensive evaluations demonstrated that the resulting LLM called `AceGPT' is
the SOTA open Arabic LLM in various benchmarks, including instruction-following
benchmark (i.e., Arabic Vicuna-80 and Arabic AlpacaEval), knowledge benchmark
(i.e., Arabic MMLU and EXAMs), as well as the newly-proposed Arabic cultural \&
value alignment benchmark. Notably, AceGPT outperforms ChatGPT in the popular
Vicuna-80 benchmark when evaluated with GPT-4, despite the benchmark's limited
scale. % Natural Language Understanding (NLU) benchmark (i.e., ALUE)
Codes, data, and models are in https://github.com/FreedomIntelligence/AceGPT.Comment: https://github.com/FreedomIntelligence/AceGP
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