11,220 research outputs found
Thermodynamics of pairing transition in hot nuclei
The pairing correlations in hot nuclei Dy are investigated in terms
of the thermodynamical properties by covariant density functional theory. The
heat capacities are evaluated in the canonical ensemble theory and the
paring correlations are treated by a shell-model-like approach, in which the
particle number is conserved exactly. A S-shaped heat capacity curve, which
agrees qualitatively with the experimental data, has been obtained and analyzed
in details. It is found that the one-pair-broken states play crucial roles in
the appearance of the S shape of the heat capacity curve. Moreover, due to the
effect of the particle-number conservation, the pairing gap varies smoothly
with the temperature, which indicates a gradual transition from the superfluid
to the normal state.Comment: 13 pages, 4 figure
Familial hypomagnesaemia, Hypercalciuria and Nephrocalcinosis associated with a novel mutation of the highly conserved leucine residue 116 of Claudin 16 in a Chinese patient with a delayed diagnosis: A case report
Background: Sixty mutations of claudin 16 coding gene have been reported in familial hypomagnesemia with hypercalciuria and nephrocalcinosis (FHHNC) patients. Recent investigations revealed that a highly conserved glycine-leucine-tryptophan (115G-L-W117) motif in the first extracellular segment (ESC1) of claudin 16 might be essential for stabilization of the appropriately folded ECS1 structure and conservation of normal claudin 16 function. However, neither missense nor nonsense mutation has ever been described in this motif. Our study aimed at identifying mutations in a Chinese patient with FHHNC and exploring the association between genotype and phenotype. Case presentation: A 33-year-old female presented with 4 years history of recurrent acute pyelonephritis without other notable past medical history. Her healthy parents, who aged 56 and 53 respectively, were second cousins, and her only sibling died from renal failure without definite cause at age 25. Renal ultrasound imaging demonstrated atrophic kidneys and bilateral nephrocalcinosis. The laboratory workup revealed impaired renal function (Stage CKD IV), hypocalcemia and mild hypomagnesemia, accompanied with marked renal loss of magnesium and hypercalciuria. During the follow-up, treatment with calcitriol and calcium but not with magnesium was difficult to achieve normal serum calcium levels, whereas her serum magnesium concentration fluctuated within normal ranges. In the end, the patient unavoidably reached ESRD at 36 years old. The clinical features and family history suggested the diagnosis of FHHNC. To make a definite diagnosis, we use whole-exome sequencing to identify the disease-causing mutations and Sanger sequencing to confirm the mutation co-segregation in the family. As a result, a novel homozygous mutation (c.346C > G, p.Leu116Val) in115G-L-W117motif of claudin 16 was identified. Her parents, grandmother and one of her cousins carried heterozygous p.Leu116Val, whereas 200 unrelated controls did not carry this mutation. Conclusions: We described a delayed diagnosis patient with FHHNC in the Chinese population and identified a novel missense mutation in the highly conserved115G-L-W117motif of claudin 16 for the first time. According to the reported data and the information deduced from 3D modeling, we speculate that this mutation probably reserve partial residual function which might be related to the slight phenotype of the patient
Fair and Optimal Classification via Transports to Wasserstein-Barycenter
Fairness in automated decision-making systems has gained increasing attention
as their applications expand to real-world high-stakes domains. To facilitate
the design of fair ML systems, it is essential to understand the potential
trade-offs between fairness and predictive power, and the construction of the
optimal predictor under a given fairness constraint. In this paper, for general
classification problems under the group fairness criterion of demographic
parity (DP), we precisely characterize the trade-off between DP and
classification accuracy, referred to as the minimum cost of fairness. Our
insight comes from the key observation that finding the optimal fair classifier
is equivalent to solving a Wasserstein-barycenter problem under -norm
restricted to the vertices of the probability simplex. Inspired by our
characterization, we provide a construction of an optimal fair classifier
achieving this minimum cost via the composition of the Bayes regressor and
optimal transports from its output distributions to the barycenter. Our
construction naturally leads to an algorithm for post-processing any
pre-trained predictor to satisfy DP fairness, complemented with finite sample
guarantees. Experiments on real-world datasets verify and demonstrate the
effectiveness of our approaches.Comment: Code is at https://github.com/rxian/fair-classificatio
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