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Article ; Online: "Walking selectivity" in the occipital place area in 8-year-olds, not 5-year-olds.

Jung, Yaelan / Hsu, Debbie / Dilks, Daniel D

Cerebral cortex (New York, N.Y. : 1991)

2024 Volume 34, Issue 3

Abstract: A recent neuroimaging study in adults found that the occipital place area (OPA)-a cortical region involved in "visually guided navigation" (i.e. moving about the immediately visible environment, avoiding boundaries, and obstacles)-represents visual ... ...

Abstract	A recent neuroimaging study in adults found that the occipital place area (OPA)-a cortical region involved in "visually guided navigation" (i.e. moving about the immediately visible environment, avoiding boundaries, and obstacles)-represents visual information about walking, not crawling, suggesting that OPA is late developing, emerging only when children are walking, not beforehand. But when precisely does this "walking selectivity" in OPA emerge-when children first begin to walk in early childhood, or perhaps counterintuitively, much later in childhood, around 8 years of age, when children are adult-like walking? To directly test these two hypotheses, using functional magnetic resonance imaging (fMRI) in two groups of children, 5- and 8-year-olds, we measured the responses in OPA to first-person perspective videos through scenes from a "walking" perspective, as well as three control perspectives ("crawling," "flying," and "scrambled"). We found that the OPA in 8-year-olds-like adults-exhibited walking selectivity (i.e. responding significantly more to the walking videos than to any of the others, and no significant differences across the crawling, flying, and scrambled videos), while the OPA in 5-year-olds exhibited no walking selectively. These findings reveal that OPA undergoes protracted development, with walking selectivity only emerging around 8 years of age.
MeSH term(s)	Child ; Child, Preschool ; Humans ; Brain Mapping/methods ; Magnetic Resonance Imaging/methods ; Neuroimaging ; Photic Stimulation/methods ; Walking
Language	English
Publishing date	2024-03-17
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1077450-6
ISSN	1460-2199 ; 1047-3211
ISSN (online)	1460-2199
ISSN	1047-3211
DOI	10.1093/cercor/bhae101
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Book ; Online: Multi-group Learning for Hierarchical Groups

Deng, Samuel / Hsu, Daniel

2024

Abstract: The multi-group learning model formalizes the learning scenario in which a single predictor must generalize well on multiple, possibly overlapping subgroups of interest. We extend the study of multi-group learning to the natural case where the groups are ...

Abstract	The multi-group learning model formalizes the learning scenario in which a single predictor must generalize well on multiple, possibly overlapping subgroups of interest. We extend the study of multi-group learning to the natural case where the groups are hierarchically structured. We design an algorithm for this setting that outputs an interpretable and deterministic decision tree predictor with near-optimal sample complexity. We then conduct an empirical evaluation of our algorithm and find that it achieves attractive generalization properties on real datasets with hierarchical group structure.
Keywords	Computer Science - Machine Learning
Publishing date	2024-01-31
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Pulmonary hypertension in children with severe OSA: Can CO

Kanney, Michelle / Hsu, Daniel

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine

2022 Volume 18, Issue 6, Page(s) 1485–1486

MeSH term(s)	Carbon Dioxide ; Child ; Humans ; Hypertension ; Hypertension, Pulmonary/complications ; Sleep Apnea, Obstructive/complications
Chemical Substances	Carbon Dioxide (142M471B3J)
Language	English
Publishing date	2022-04-05
Publishing country	United States
Document type	Journal Article ; Comment
ZDB-ID	2397213-0
ISSN	1550-9397 ; 1550-9389
ISSN (online)	1550-9397
ISSN	1550-9389
DOI	10.5664/jcsm.10034
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Zs.A 6590: Show issues

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Article ; Online: Hospital-onset, healthcare-associated Gram-negative bloodstream infections in patients admitted to a busy district general hospital in England: a retrospective cohort study.

Choy, B / Krutikov, M / El-Mugamar, H / Paget, S / Hsu, D / Sivaramakrishnan, A

The Journal of hospital infection

2023 Volume 137, Page(s) 84–85

MeSH term(s)	Humans ; Retrospective Studies ; Hospitals, General ; Cross Infection/epidemiology ; Cross Infection/complications ; Sepsis/complications ; Delivery of Health Care ; Gram-Negative Bacterial Infections/epidemiology ; Gram-Negative Bacterial Infections/complications ; Bacteremia/epidemiology ; Bacteremia/complications ; Gram-Negative Bacteria
Language	English
Publishing date	2023-03-30
Publishing country	England
Document type	Letter
ZDB-ID	779366-2
ISSN	1532-2939 ; 0195-6701
ISSN (online)	1532-2939
ISSN	0195-6701
DOI	10.1016/j.jhin.2023.01.026
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Zs.A 1586: Show issues

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Article ; Online: Do not get stumped: multimodality imaging findings of early and late post-cholecystectomy complications.

Maddu, Kiran / Polireddy, Karunesh / Hsu, Derek / Hoff, Carrie

Emergency radiology

2023 Volume 30, Issue 3, Page(s) 351–362

Abstract: Cholecystectomy is the most performed intra-abdominal surgical procedure in the US, with 1.2 million performed annually, and is predominantly performed laparoscopically. Although largely safe, laparoscopic cholecystectomy results in higher rates of ... ...

Abstract	Cholecystectomy is the most performed intra-abdominal surgical procedure in the US, with 1.2 million performed annually, and is predominantly performed laparoscopically. Although largely safe, laparoscopic cholecystectomy results in higher rates of abdominal symptoms consisting of abdominal pain and dyspepsia, which may persist or recur, collectively known as post-cholecystectomy syndrome. This article aims to (1) provide an overview of post-cholecystectomy syndrome with an emphasis on biliary complications and emergent imaging findings, (2) illustrate the spectrum of imaging findings of early and late post-cholecystectomy complications, (3) enumerate the role of various imaging modalities in evaluating post-cholecystectomy complications and address the role of selective trans-catheter coil embolization in managing bile leaks, and (4) discuss pearls and pitfalls in imaging following cholecystectomy. While common first-line imaging modalities for post-cholecystectomy complications include CT and sonography, ERCP and MRCP can delineate the biliary tree with greater detail. Scintigraphy has a higher sensitivity and specificity than CT or sonography for diagnosing bile leak and may preclude the need for ERCP. Post-operative complications include biliary duct injury or leak, biliary obstruction, remnant gallbladder/cystic duct stones and inflammation, biliary dyskinesia, papillary stenosis, and vascular injury. Subtle cases resulting in lethal outcomes, such as hemorrhage from the gallbladder bed without major vessel injury, have also been described. Cases presented will include biliary complications such as post-cholecystectomy stump cholecystitis, nonbiliary complications such as subcapsular hematoma, and normal post-surgical findings such as oxidized regenerated cellulose. Post-operative biliary complications can cause significant morbidity and mortality, and thus familiarity with the expected post-surgical appearance of the gallbladder fossa and biliary tract, as well as understanding the spectrum of complications and associated multimodality imaging findings, are essential for emergency radiologists and those practicing in the acute care setting to direct appropriate patient management. Furthermore, many of the postoperative complications can be managed by noninvasive percutaneous interventional procedures, from drain placement to cystic artery and cystic duct stump embolization.
MeSH term(s)	Humans ; Postcholecystectomy Syndrome/complications ; Postcholecystectomy Syndrome/surgery ; Cholecystectomy/adverse effects ; Cholecystectomy, Laparoscopic/adverse effects ; Postoperative Complications/diagnostic imaging ; Postoperative Complications/therapy ; Drainage/adverse effects
Language	English
Publishing date	2023-04-12
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	1425144-9
ISSN	1438-1435 ; 1070-3004
ISSN (online)	1438-1435
ISSN	1070-3004
DOI	10.1007/s10140-023-02131-y
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Article ; Online: Atypical haemolytic uremic syndrome with refractory multiorgan involvement and heterozygous CFHR1/CFHR3 gene deletion.

Diep, Jason / Potter, Daniela / Mai, Jun / Hsu, Danny

BMC nephrology

2023 Volume 24, Issue 1, Page(s) 127

Abstract: Background: We present this challenging case report of Atypical Haemolytic Uremic Syndrome (aHUS) presenting with multi-organ involvement in a patient and heterozygous CFHR1/CFHR3 gene variant, which was refractory to initial eculizumab therapy.: Case ...

Abstract	Background: We present this challenging case report of Atypical Haemolytic Uremic Syndrome (aHUS) presenting with multi-organ involvement in a patient and heterozygous CFHR1/CFHR3 gene variant, which was refractory to initial eculizumab therapy. Case presentation: A forty-three year old female presented with aHUS and had heterozygous disease-associated deletions in the complement genes CFHR1/CFHR3. She had progressive kidney failure and severe extra-renal manifestations including cardiomyopathy and haemorrhagic cystitis; as well as pulmonary, gastrointestinal and neurological involvement. The initial kidney biopsy revealed thrombotic microangiopathy (TMA) changes involving all glomeruli. Clinical improvement was initially seen during eculizumab initiation with suppressed CH50 level, but a new rhinovirus/enterovirus upper respiratory tract infection triggered further severe multi-organ disease activity. The extra-renal manifestations stabilised, then ultimately improved after a period of eculizumab dose intensification. However, the impact on dose intensification on this improvement is unclear. Despite the extra-renal clinical improvement, she ultimately progressed to end-stage kidney disease (ESKD), commencing peritoneal dialysis for three years before undergoing a successful uncomplicated cadaveric kidney transplant without prophylactic eculizumab. Two years after transplant, she has excellent transplant graft function without any further disease recurrence. Conclusions: This case highlights the concept of extra-renal manifestations in aHUS initially resistant to eculizumab, which potentially responded to dose intensification. Whilst organ injuries are potentially reversible with timely targeted treatment, it appears that the kidneys are most vulnerable to injury.
MeSH term(s)	Female ; Humans ; Adult ; Gene Deletion ; Atypical Hemolytic Uremic Syndrome/complications ; Atypical Hemolytic Uremic Syndrome/diagnosis ; Atypical Hemolytic Uremic Syndrome/drug therapy ; Kidney ; Kidney Transplantation ; Kidney Failure, Chronic/genetics ; Blood Proteins ; Complement C3b Inactivator Proteins/genetics
Chemical Substances	CFHR3 protein, human ; Blood Proteins ; CFHR1 protein, human ; Complement C3b Inactivator Proteins
Language	English
Publishing date	2023-05-05
Publishing country	England
Document type	Case Reports ; Journal Article
ZDB-ID	2041348-8
ISSN	1471-2369 ; 1471-2369
ISSN (online)	1471-2369
ISSN	1471-2369
DOI	10.1186/s12882-023-03153-x
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Book ; Online: On the sample complexity of estimation in logistic regression

Hsu, Daniel / Mazumdar, Arya

2023

Abstract: The logistic regression model is one of the most popular data generation model in noisy binary classification problems. In this work, we study the sample complexity of estimating the parameters of the logistic regression model up to a given $\ell_2$ ... ...

Abstract	The logistic regression model is one of the most popular data generation model in noisy binary classification problems. In this work, we study the sample complexity of estimating the parameters of the logistic regression model up to a given $\ell_2$ error, in terms of the dimension and the inverse temperature, with standard normal covariates. The inverse temperature controls the signal-to-noise ratio of the data generation process. While both generalization bounds and asymptotic performance of the maximum-likelihood estimator for logistic regression are well-studied, the non-asymptotic sample complexity that shows the dependence on error and the inverse temperature for parameter estimation is absent from previous analyses. We show that the sample complexity curve has two change-points (or critical points) in terms of the inverse temperature, clearly separating the low, moderate, and high temperature regimes.
Keywords	Mathematics - Statistics Theory ; Computer Science - Information Theory ; Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	310
Publishing date	2023-07-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: A New Framework for Query Efficient Active Imitation Learning

Hsu, Daniel

2019

Abstract: We seek to align agent policy with human expert behavior in a reinforcement learning (RL) setting, without any prior knowledge about dynamics, reward function, and unsafe states. There is a human expert knowing the rewards and unsafe states based on his ... ...

Abstract	We seek to align agent policy with human expert behavior in a reinforcement learning (RL) setting, without any prior knowledge about dynamics, reward function, and unsafe states. There is a human expert knowing the rewards and unsafe states based on his preference and objective, but querying that human expert is expensive. To address this challenge, we propose a new framework for imitation learning (IL) algorithm that actively and interactively learns a model of the user's reward function with efficient queries. We build an adversarial generative model of states and a successor feature (SR) model trained over transition experience collected by learning policy. Our method uses these models to select state-action pairs, asking the user to comment on the optimality or safety, and trains a adversarial neural network to predict the rewards. Different from previous papers, which are almost all based on uncertainty sampling, the key idea is to actively and efficiently select state-action pairs from both on-policy and off-policy experience, by discriminating the queried (expert) and unqueried (generated) data and maximizing the efficiency of value function learning. We call this method adversarial reward query with successor representation. We evaluate the proposed method with simulated human on a state-based 2D navigation task, robotic control tasks and the image-based video games, which have high-dimensional observation and complex state dynamics. The results show that the proposed method significantly outperforms uncertainty-based methods on learning reward models, achieving better query efficiency, where the adversarial discriminator can make the agent learn human behavior more efficiently and the SR can select states which have stronger impact on value function. Moreover, the proposed method can also learn to avoid unsafe states when training the reward model.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Statistics - Machine Learning
Subject code	006
Publishing date	2019-12-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: LLMs for Robotic Object Disambiguation

Jiang, Connie / Xu, Yiqing / Hsu, David

2024

Abstract: The advantages of pre-trained large language models (LLMs) are apparent in a variety of language processing tasks. But can a language model's knowledge be further harnessed to effectively disambiguate objects and navigate decision-making challenges ... ...

Abstract	The advantages of pre-trained large language models (LLMs) are apparent in a variety of language processing tasks. But can a language model's knowledge be further harnessed to effectively disambiguate objects and navigate decision-making challenges within the realm of robotics? Our study reveals the LLM's aptitude for solving complex decision making challenges that are often previously modeled by Partially Observable Markov Decision Processes (POMDPs). A pivotal focus of our research is the object disambiguation capability of LLMs. We detail the integration of an LLM into a tabletop environment disambiguation task, a decision making problem where the robot's task is to discern and retrieve a user's desired object from an arbitrarily large and complex cluster of objects. Despite multiple query attempts with zero-shot prompt engineering (details can be found in the Appendix), the LLM struggled to inquire about features not explicitly provided in the scene description. In response, we have developed a few-shot prompt engineering system to improve the LLM's ability to pose disambiguating queries. The result is a model capable of both using given features when they are available and inferring new relevant features when necessary, to successfully generate and navigate down a precise decision tree to the correct object--even when faced with identical options.
Keywords	Computer Science - Robotics ; Computer Science - Computation and Language ; Computer Science - Machine Learning
Subject code	004
Publishing date	2024-01-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Polynomial time auditing of statistical subgroup fairness for Gaussian data

Hsu, Daniel / Huang, Jizhou / Juba, Brendan

2024

Abstract: We study the problem of auditing classifiers with the notion of statistical subgroup fairness. Kearns et al. (2018) has shown that the problem of auditing combinatorial subgroups fairness is as hard as agnostic learning. Essentially all work on remedying ...

Abstract	We study the problem of auditing classifiers with the notion of statistical subgroup fairness. Kearns et al. (2018) has shown that the problem of auditing combinatorial subgroups fairness is as hard as agnostic learning. Essentially all work on remedying statistical measures of discrimination against subgroups assumes access to an oracle for this problem, despite the fact that no efficient algorithms are known for it. If we assume the data distribution is Gaussian, or even merely log-concave, then a recent line of work has discovered efficient agnostic learning algorithms for halfspaces. Unfortunately, the boosting-style reductions given by Kearns et al. required the agnostic learning algorithm to succeed on reweighted distributions that may not be log-concave, even if the original data distribution was. In this work, we give positive and negative results on auditing for the Gaussian distribution: On the positive side, we an alternative approach to leverage these advances in agnostic learning and thereby obtain the first polynomial-time approximation scheme (PTAS) for auditing nontrivial combinatorial subgroup fairness: we show how to audit statistical notions of fairness over homogeneous halfspace subgroups when the features are Gaussian. On the negative side, we find that under cryptographic assumptions, no polynomial-time algorithm can guarantee any nontrivial auditing, even under Gaussian feature distributions, for general halfspace subgroups.
Keywords	Computer Science - Machine Learning ; Computer Science - Computational Complexity ; Computer Science - Computers and Society
Subject code	004
Publishing date	2024-01-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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