648 research outputs found
Localized Lasso for High-Dimensional Regression
We introduce the localized Lasso, which is suited for learning models that
are both interpretable and have a high predictive power in problems with high
dimensionality and small sample size . More specifically, we consider a
function defined by local sparse models, one at each data point. We introduce
sample-wise network regularization to borrow strength across the models, and
sample-wise exclusive group sparsity (a.k.a., norm) to introduce
diversity into the choice of feature sets in the local models. The local models
are interpretable in terms of similarity of their sparsity patterns. The cost
function is convex, and thus has a globally optimal solution. Moreover, we
propose a simple yet efficient iterative least-squares based optimization
procedure for the localized Lasso, which does not need a tuning parameter, and
is guaranteed to converge to a globally optimal solution. The solution is
empirically shown to outperform alternatives for both simulated and genomic
personalized medicine data
Mitigating Voter Attribute Bias for Fair Opinion Aggregation
The aggregation of multiple opinions plays a crucial role in decision-making,
such as in hiring and loan review, and in labeling data for supervised
learning. Although majority voting and existing opinion aggregation models are
effective for simple tasks, they are inappropriate for tasks without
objectively true labels in which disagreements may occur. In particular, when
voter attributes such as gender or race introduce bias into opinions, the
aggregation results may vary depending on the composition of voter attributes.
A balanced group of voters is desirable for fair aggregation results but may be
difficult to prepare. In this study, we consider methods to achieve fair
opinion aggregation based on voter attributes and evaluate the fairness of the
aggregated results. To this end, we consider an approach that combines opinion
aggregation models such as majority voting and the Dawid and Skene model (D&S
model) with fairness options such as sample weighting. To evaluate the fairness
of opinion aggregation, probabilistic soft labels are preferred over discrete
class labels. First, we address the problem of soft label estimation without
considering voter attributes and identify some issues with the D&S model. To
address these limitations, we propose a new Soft D&S model with improved
accuracy in estimating soft labels. Moreover, we evaluated the fairness of an
opinion aggregation model, including Soft D&S, in combination with different
fairness options using synthetic and semi-synthetic data. The experimental
results suggest that the combination of Soft D&S and data splitting as a
fairness option is effective for dense data, whereas weighted majority voting
is effective for sparse data. These findings should prove particularly valuable
in supporting decision-making by human and machine-learning models with
balanced opinion aggregation
Competing protein-protein interactions regulate binding of Hsp27 to its client protein tau.
Small heat shock proteins (sHSPs) are a class of oligomeric molecular chaperones that limit protein aggregation. However, it is often not clear where sHSPs bind on their client proteins or how these protein-protein interactions (PPIs) are regulated. Here, we map the PPIs between human Hsp27 and the microtubule-associated protein tau (MAPT/tau). We find that Hsp27 selectively recognizes two aggregation-prone regions of tau, using the conserved β4-β8 cleft of its alpha-crystallin domain. The β4-β8 region is also the site of Hsp27-Hsp27 interactions, suggesting that competitive PPIs may be an important regulatory paradigm. Indeed, we find that each of the individual PPIs are relatively weak and that competition for shared sites seems to control both client binding and Hsp27 oligomerization. These findings highlight the importance of multiple, competitive PPIs in the function of Hsp27 and suggest that the β4-β8 groove acts as a tunable sensor for clients
Estimating Treatment Effects Under Heterogeneous Interference
Treatment effect estimation can assist in effective decision-making in
e-commerce, medicine, and education. One popular application of this estimation
lies in the prediction of the impact of a treatment (e.g., a promotion) on an
outcome (e.g., sales) of a particular unit (e.g., an item), known as the
individual treatment effect (ITE). In many online applications, the outcome of
a unit can be affected by the treatments of other units, as units are often
associated, which is referred to as interference. For example, on an online
shopping website, sales of an item will be influenced by an advertisement of
its co-purchased item. Prior studies have attempted to model interference to
estimate the ITE accurately, but they often assume a homogeneous interference,
i.e., relationships between units only have a single view. However, in
real-world applications, interference may be heterogeneous, with multi-view
relationships. For instance, the sale of an item is usually affected by the
treatment of its co-purchased and co-viewed items. We hypothesize that ITE
estimation will be inaccurate if this heterogeneous interference is not
properly modeled. Therefore, we propose a novel approach to model heterogeneous
interference by developing a new architecture to aggregate information from
diverse neighbors. Our proposed method contains graph neural networks that
aggregate same-view information, a mechanism that aggregates information from
different views, and attention mechanisms. In our experiments on multiple
datasets with heterogeneous interference, the proposed method significantly
outperforms existing methods for ITE estimation, confirming the importance of
modeling heterogeneous interference
Surgically treated Zenker’s diverticulum: Diverticulectomy and cricopharyngeal myotomy
 Zenker's diverticulum is a very rare disease among gastorointestinal diverticulum. We report a case of Zenker's diverticulum successfully treated with diverticulectomy and cricophalyngial myotomy. A 71-year-old male complained of aspirating water for two years. He was diagnosed as Zenker's diverticulum. Due to his severe symptoms, the operation was performed in an open-neck approach. The left recurrent laryngeal nerve was identified and preserved. An incision was made in the diverticulum wall, and the internal diameter of normal cervical esophagus was measured. The diverticulum was then excised with an automatic suture device in the minor axis direction of the esophagus. A cricopharyngeal myotomy was conducted, because this muscle was fibrotic and stiffened. The patient's symptoms disappeared after the operation. Diverticulectomy and cricopharyngeal myotomy through an open-neck approach is a safe and reliable method that follows, direct access to the diverticulum and recurrent laryngeal nerve
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