Miracle-dz/UniFAR-FTUs huggingface dataset:
Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction

Value functions are crucial for model-free Reinforcement Learning (RL) to obtain a policy implicitly or guide the policy updates. Value estimation heavily depends on the stochasticity of environmental dynamics and the quality of reward signals. In this paper, we propose a two-step understanding of value estimation from the perspective of future prediction, through decomposing the value function into a reward-independent future dynamics part and a policy-independent trajectory return part. We then derive a practical deep RL algorithm from the above decomposition, consisting of a convolutional trajectory representation model, a conditional variational dynamics model to predict the expected representation of future trajectory and a convex trajectory return model that maps a trajectory representation to its return. Our algorithm is evaluated in MuJoCo continuous control tasks and shows superior results under both common settings and delayed reward settings.
The Effect of Physicians’ Treatment Recommendations on Their Epistemic Authority: The Medical Expertise Bias

This study examines the hypothesis that patients perceive physicians who recommend more active and major treatment as having greater epistemic authority. The hypothesis is based on the assumption that patients expect that their physicians should advocate for an active treatment rather than abstention from treatment. The sample included 631 participants. Data were collected using a between-subjects design and scenarios that described a person who suffers from a medical problem and visits a physician (surgeon, orthopedist, or dentist). The physician gives a passive or active recommendation regarding treatment. Different levels of passive recommendation (against or wait on treatment) and active recommendation (minor, moderate, or major procedures) were used. The experience of the physician was also manipulated. The dependent measure was the patient's rating of the physician's epistemic authority. Physicians who prescribed an active mode of treatment were perceived as having a higher epistemic authority than physicians who gave a passive recommendation. We named this phenomenon the medical expertise bias, as people might be biased when judging the level of expertise of their physicians such that those physicians who recommend an active treatment are considered to have greater medical epistemic authority in general.
Pre-Infection Stages of Austropuccinia psidii in the Epidermis of Eucalyptus Hybrid Leaves with Different Resistance Levels

Rust is a major Eucalyptus spp. disease, which is especially damaging for early-stage plants. The aim of this study was to verify the pre-infection process of Austropuccinia psidii (A. psidii) in the leaves of three phenological stages of Eucalyptus clones with different resistance levels. Plants from the hybrids of Eucalyptus urophylla × Eucalyptus grandis (E. grandis) with variable levels of resistance to this disease were used. The pathogen was inoculated in vitro on abaxial leaf discs of first, third, and fifth leaf stages and maintained under conditions suitable for disease development. Subsequently, samples from these discs were collected 24 and 120 h after inoculation and processed using scanning electron microscopy analysis. No symptoms were seen in any leaf stage of the resistant clone. Additionally, a low incidence of A. psidii germination (1.3–2%) and appressoria (0–0.5%) in three leaf stages was observed. However, the first leaf stage of the susceptible clone presented germination of large numbers of urediniospores (65%) with appressoria (55%) and degradation of the cuticle and wax. From the third stage, the percentage of germinated urediniospores (<15%) and appressoria (<2%) formation of this clone decreased. Protrusions on the leaf surface, associated with the pathogen, were observed on the first and third leaf stages of the resistant clone and on the fifth stage of the susceptible clone, suggesting a possible defensive plant reaction.
Detecting Individual Level `Always Survivor' Causal Effects Under `Truncation by Death' and Censoring Through Time

The analysis of causal effects when the outcome of interest is possibly truncated by death has a long history in statistics and causal inference. The survivor average causal effect is commonly identified with more assumptions than those guaranteed by the design of a randomized clinical trial or using sensitivity analysis. This paper demonstrates that individual level causal effects in the `always survivor' principal stratum can be identified with no stronger identification assumptions than randomization. We illustrate the practical utility of our methods using data from a clinical trial on patients with prostate cancer. Our methodology is the first and, as of yet, only proposed procedure that enables detecting causal effects in the presence of truncation by death using only the assumptions that are guaranteed by design of the clinical trial.
Systematic Identification of Pathways That Couple Cell Growth and Division in Yeast

Size homeostasis in budding yeast requires that cells grow to a critical size before commitment to division in the late prereplicative growth phase of the cell cycle, an event termed Start. We determined cell size distributions for the complete set of ∼6000Saccharomyces cerevisiae gene deletion strains and identified ∼500 abnormally small (whi) or large (lge) mutants. Genetic analysis revealed a complex network of newly found factors that govern critical cell size at Start, the most potent of which were Sfp1, Sch9, Cdh1, Prs3, and Whi5. Ribosome biogenesis is intimately linked to cell size through Sfp1, a transcription factor that controls the expression of at least 60 genes implicated in ribosome assembly. Cell growth and division appear to be coupled by multiple conserved mechanisms.
All Neural Networks are Created Equal

One of the unresolved questions in deep learning is the nature of the solutions that are being discovered. We investigate the collection of solutions reached by the same network architecture, with different random initialization of weights and random mini-batches. These solutions are shown to be rather similar - more often than not, each train and test example is either classified correctly by all the networks, or by none at all. Surprisingly, all the network instances seem to share the same learning dynamics, whereby initially the same train and test examples are correctly recognized by the learned model, followed by other examples which are learned in roughly the same order. When extending the investigation to heterogeneous collections of neural network architectures, once again examples are seen to be learned in the same order irrespective of architecture, although the more powerful architecture may continue to learn and thus achieve higher accuracy. This pattern of results remains true even when the composition of classes in the test set is unrelated to the train set, for example, when using out of sample natural images or even artificial images. To show the robustness of these phenomena we provide an extensive summary of our empirical study, which includes hundreds of graphs describing tens of thousands of networks with varying NN architectures, hyper-parameters and domains. We also discuss cases where this pattern of similarity breaks down, which show that the reported similarity is not an artifact of optimization by gradient descent. Rather, the observed pattern of similarity is characteristic of learning complex problems with big networks. Finally, we show that this pattern of similarity seems to be strongly correlated with effective generalization.
Improving Access to Eye Care among Persons at High-Risk of Glaucoma in Philadelphia — Design and Methodology: The Philadelphia Glaucoma Detection and Treatment Project

ABSTRACT Purpose: The Wills Eye Glaucoma Research Center initiated a 2-year demonstration project to develop and implement a community-based intervention to improve detection and management of glaucoma in Philadelphia. Methods: The glaucoma detection examination consisted of: ocular, medical, and family history; visual acuity testing; corneal pachymetry; biomicroscopy of the anterior segment; intraocular pressure (IOP) measurement; gonioscopy; funduscopy; automated visual field testing; and fundus-color photography. Treatment included laser surgery and/or IOP-lowering medication. A cost analysis was conducted to understand resource requirements. Outcome measures included; prevalence of glaucoma-related pathology and other eye diseases among high-risk populations; the impact of educational workshops on level of knowledge about glaucoma (assessed by pre- and post-test evaluation); and patient satisfaction of the glaucoma detection examinations in the community (assessed by satisfaction survey). Treatment outcome measures were change in IOP at 4–6 weeks and 4–6 months following selective laser trabeculoplasty treatment, deepening of the anterior chamber angle following laser-peripheral iridotomy treatment, and rate of adherence to recommended follow-up examinations. Cost outcomes included total program costs, cost per case of glaucoma detected, and cost per case of ocular disease detected. Results: This project enrolled 1649 participants (African Americans aged 50+ years, adults 60+ years and individuals with a family history of glaucoma). A total of 1074 individuals attended a glaucoma educational workshop and 1508 scheduled glaucoma detection examination appointments in the community setting. Conclusions: The Philadelphia Glaucoma Detection and Treatment Project aimed to improve access and use of eye care and to provide a model for a targeted community-based glaucoma program.
A wavelet-domain non-parametric statistical approach for image denoising

The challenge of conventional parametric model-based wavelet image denoising approaches is that the efficiency of these methods greatly depends on the accuracy of the prior distribution used for modeling the wavelet coefficients. To tackle the above challenge, a non-parametric statistical model is proposed in this paper to formulate the marginal distribution of wavelet coefficients. The proposed non-parametric model differs from conventional parametric models in that the proposed model is automatically adapted to the observed image data, rather than imposing an assumption about the distribution of the data. Furthermore, the proposed non-parametric model is incorporated into a Bayesian inference framework to derive a maximum a posterior (MAP) estimation-based image denoising approach. Experiments are conducted to not only demonstrate that the proposed non-parametric statistical model is more suitable than conventional models to formulate the marginal distribution of wavelet coefficients, but also show that the proposed image denoising approach outperforms the conventional approaches.
Fast computation of loudness using a deep neural network

The present paper introduces a deep neural network (DNN) for predicting the instantaneous loudness of a sound from its time waveform. The DNN was trained using the output of a more complex model, called the Cambridge loudness model. While a modern PC can perform a few hundred loudness computations per second using the Cambridge loudness model, it can perform more than 100,000 per second using the DNN, allowing real-time calculation of loudness. The root-mean-square deviation between the predictions of instantaneous loudness level using the two models was less than 0.5 phon for unseen types of sound. We think that the general approach of simulating a complex perceptual model by a much faster DNN can be applied to other perceptual models to make them run in real time.
Expression of Fractals Through Neural Network Functions

To help understand the underlying mechanisms of neural networks (NNs), several groups have studied the number of linear regions <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> of piecewise linear (PwL) functions, generated by deep neural networks (DNN). In particular, they showed that <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> can grow exponentially with the number of network parameters <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>, a property often used to explain the advantages of deep over shallow NNs. Nonetheless, a dimension argument shows that DNNs cannot generate all PwL functions with <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> linear regions when <inline-formula> <tex-math notation="LaTeX">$\ell > p$ </tex-math></inline-formula>. It is thus natural to seek to characterize specific families of functions with <inline-formula> <tex-math notation="LaTeX">$\ell > p$ </tex-math></inline-formula> linear regions that can be constructed by DNNs. Iterated Function Systems (IFS) recursively construct a sequence of PwL functions <inline-formula> <tex-math notation="LaTeX">$F_{k}$ </tex-math></inline-formula> with a number of linear regions which is exponential in <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>. We show that <inline-formula> <tex-math notation="LaTeX">$F_{k}$ </tex-math></inline-formula> can be generated by a NN using only <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(k)$ </tex-math></inline-formula> parameters. IFS are used extensively to generate natural-looking landscape textures in artificial images as well as for compression of natural images. The surprisingly good performance of this compression suggests that human visual system may lock in on self-similarities. The combination of this phenomenon with the capacity of DNNs to efficiently approximate IFS may contribute to the success of DNNs, particularly striking for image processing tasks.
Correlations between rapid maxillary expansion (RME) and the auditory apparatus.

OBJECTIVE To evaluate the effects of rapid maxillary expansion (RME) on conductive hearing loss and maxillary constriction. MATERIALS AND METHODS A total of 25 subjects (15 girls and 10 boys, aged between 6 years 8 months to 8 years 2 months) with conductive hearing loss and maxillary constriction were studied. Audiogram, tympanogram, and video-otoscopy were used to investigate the anatomical and physiological modifications of the bony and muscular structure of the maxilla and the auditory apparatus. The records were taken before maxillary expansion (T0), after expansion (7-14 days; T1), and after the retention period (8 months after expansion; T2). RESULTS After expansion, the audiometric records indicated an improvement in hearing levels for higher frequencies but not for lower ones. After the retention period, there was a functional improvement in all patients for all frequencies. The recovery of the tympanic membrane's elasticity occurred only after retention, as shown by the standard model tympanogram, which was still flat after expansion. CONCLUSIONS The auditory function in patients with conductive hearing loss may be corrected through correction of the palatal anatomy, which influences the muscular function of the tubal ostia and allows a normal activity of the tympanic membrane and the auditory apparatus. Positive effects on conductive hearing loss are possible additional benefits of RME treatment, but this does not indicate that patients with conductive hearing loss without an accompanying maxillary constriction should consider this as a treatment approach.
Generalizable Adversarial Attacks Using Generative Models

Adversarial attacks on deep neural networks traditionally rely on a constrained optimization paradigm, where an optimization procedure is used to obtain a single adversarial perturbation for a given input example. In this work we frame the problem as learning a distribution of adversarial perturbations, enabling us to generate diverse adversarial distributions given an unperturbed input. We show that this framework is domain-agnostic in that the same framework can be employed to attack different input domains with minimal modification. Across three diverse domains---images, text, and graphs---our approach generates whitebox attacks with success rates that are competitive with or superior to existing approaches, with a new state-of-the-art achieved in the graph domain. Finally, we demonstrate that our framework can efficiently generate a diverse set of attacks for a single given input, and is even capable of attacking \textit{unseen} test instances in a zero-shot manner, exhibiting attack generalization.
Converse Theorems for Integral Quadratic Constraints

A collection of converse theorems for integral quadratic constraints (IQCs) is established for linear time-invariant systems. It is demonstrated that when a system interconnected in feedback with an arbitrary system satisfying an IQC is stable, then the given system must necessarily satisfy the complementary IQC. These theorems are specialized to derive multiple versions of converse passivity results. They cover standard notions of strict passivity as well as passivity indices that characterize the tradeoffs between passivity surplus and deficit. Converse frequency-weighted small-gain and passivity theorems are also established.
Perioperative morbidity associated with bariatric surgery: an academic center experience.

HYPOTHESIS As the demand for bariatric surgery increases, it becomes increasingly important to define predictors of morbidity and mortality. We hypothesize that specific clinical variables predict postoperative morbidity after bariatric surgery. DESIGN, SETTING, AND PATIENTS This is a retrospective review of 452 patients undergoing inpatient bariatric surgery at an academic tertiary care institution. INTERVENTIONS Patients underwent open or laparoscopic gastric bypass or biliopancreatic diversion with duodenal switch at Oregon Health & Science University, Portland, from 2000 to 2003. Patient data were prospectively entered into a database. MAIN OUTCOME MEASURES Postoperative morbidity and mortality were analyzed among all patients, and logistic regression was used to identify clinical predictors of morbidity. RESULTS Major and minor morbidity rates were 10% and 13%, respectively; mortality was 0.9%. Age was associated with postoperative complications (odds ratio = 1.056 for each additional year). Duodenal switch was also associated with higher morbidity than gastric bypass (odds ratio = 2.149). Body mass index, sex, diabetes, surgical approach, and surgeon experience did not predict complications. CONCLUSIONS Increased age is a predictor of complications after bariatric surgery. Duodenal switch is also associated with a higher morbidity rate than gastric bypass. Surgeons should caution older patients (>/=60 years) of a higher risk of postoperative complications, and a higher risk associated with duodenal switch. Large multicenter studies will be necessary to accurately define other clinical predictors of morbidity and mortality after bariatric surgery.
On the lattice Hadwiger number of superballs and some other bodies

We show that the lattice Hadwiger (=kissing) number of superballs is exponential in the dimension. The same is true for some more general convex bodies. AMS 2010 Classification: 11H31, 05B40, 52C17, 11H71, 14G50;
Composing Ensembles of Policies with Deep Reinforcement Learning

Composition of elementary skills into complex behaviors to solve challenging problems is one of the key elements toward building intelligent machines. To date, there has been plenty of work on learning new policies or skills but almost no focus on composing them to perform complex decision-making. In this paper, we propose a policy ensemble composition framework that takes the robot's primitive policies and learns to compose them concurrently or sequentially through reinforcement learning. We evaluate our method in problems where traditional approaches either fail or exhibit high sample complexity to find a solution. We show that our method not only solves the problems that require both task and motion planning but also exhibits high data efficiency, which is currently one of the main limitations of reinforcement learning.
Plug-in Factorization for Latent Representation Disentanglement

In this work, we propose a Factorized Disentangler-Entangler Network (FDEN) that learns to decompose a latent representation into two mutually independent factors, namely, identity and style. Given a latent representation, the proposed framework draws a set of interpretable factors aligned to identity of an observed data and learns to maximize the independency between these factors. Our work introduces an idea for a plug-in method to disentangle latent representations of already learned deep models with no affect to the model. In doing so, it brings the possibilities of extending state-of-the-art models to solve different tasks and also maintain the performance of its original task. Thus, FDEN is naturally applicable to jointly perform multiple tasks such as few-shot learning and image-to-image translation in a single framework. We show the effectiveness of our work in disentangling a latent representation in two parts. First, to evaluate the alignment of factor to an identity, we perform few-shot learning using only the aligned factor. Then, to evaluate the effectiveness of decomposition of latent representation and to show that plugin method does not affect the deep model in its performance, we perform image-to-image style transfer by mixing factors of different images. These evaluations show, qualitatively and quantitatively, that our proposed framework can indeed disentangle a latent representation.
Combining mixture models with linear mixing updates: multilayer image segmentation and synthesis

Finite mixture models for clustering can often be improved by adding a regularization that is specific to the topology of the data. For instance, mixtures are common in unsupervised image segmentation, and typically rely on averaging the posterior mixing probabilities of spatially adjacent data points (i.e. smoothing). However, this approach has had limited success with natural images. Here we make three contributions. First, we show that a Dirichlet prior with an appropriate choice of parameters allows -- using the Expectation-Maximization approach -- to define any linear update rule for the mixing probabilities, including many smoothing regularizations as special cases. Second, we demonstrate how to use this flexible design of the update rule to propagate segmentation information across layers of a deep network, and to train mixtures jointly across layers. Third, we compare the standard Gaussian mixture and the Student-t mixture, which is known to better capture the statistics of low-level visual features. We show that our models achieve competitive performance in natural image segmentation, with the Student-t mixtures reaching state-of-the art on boundaries scores. We also demonstrate how to exploit the resulting multilayer probabilistic generative model to synthesize naturalistic images beyond uniform textures.