Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Linear Density Dependence in a Stochastic Wild Turkey Harvest Model

Current eastern wild turkey (Meleagris gallopavo silvestris) harvest models assume density-independent population dynamics despite indications that populations are subject to a form of density dependence. I suggest that both density-dependent and independent factors operate simultaneously on wild turkey populations, where the relative strength of each is governed by population density. I attempt to estimate the form of the density dependence relationship in wild turkey population growth using the theta-Ricker model. Density-independent relationships are explored between production and rainfall and temperature correlates for possible inclusion in the harvest model. Density-dependent and independent effects are then combined in the model to compare multiple harvest strategies. To estimate a functional relationship between population growth and density, I fit the theta-Ricker model to harvest index time-series from 11 state wildlife agencies. To model density-independent effects on population growth, I explored the ability of rainfall, temperature, and mast during the nesting and brooding season to predict observed production indices for 7 states. I then built a harvest model incorporating estimates to determine their influence on the mean and variability of the fall and spring harvest. ii Estimated density-dependent growth rates produced a left-skewed yield curve maximized at ~40% of carrying capacity, with large residuals. Density-independent models of production varied widely and were characterized by high model uncertainty. Results indicate a non-linear density dependence effect strongest at low population densities. High residuals from the model fit indicate that extrinsic factors will overshadow density-dependent factors at most population densities. However, environmental models were weak, requiring more data with higher precision. This indicates that density-independence can be correctly and more easily modeled as random error. The constructed model uses both density dependence and density-independent stochastic error as a tool to explore harvest strategies for biologists. The inclusion of weak density dependence changes expected harvest rates little from density-independent models. However, it does lower the probability of overharvest at low densities. Alternatives to proportional harvesting are explored to reduce the uncertainty in annual harvests.
Outcomes of prognostication in people living with advanced cancer: a qualitative study to inform a Core Outcome Set

Objective: Studies of prognostication in advanced cancer use a wide range of outcomes and outcome measures, making it difficult to compare these studies and their findings. Core Outcome Sets facilitate comparability and standardisation between studies and would benefit future prognostic research. This qualitative study is the second step in developing such a Core Outcome Set, with the aim to explore the perceptions and experiences of patients with advanced cancer, informal caregivers, and clinicians regarding the potential outcomes of prognostication. Methods: A qualitative study using semi-structured interviews and framework analysis, informed by the Core Outcome Measures for Effectiveness Trials (COMET) taxonomy. We conducted semi-structured interviews with patients living with advanced cancer (n=8), informal caregivers (n=10), and clinicians (n=10) recruited from palliative care services across three sites in London, United Kingdom. Interviews were conducted in-person, via telephone, or video conferencing. Results: We identified 33 outcomes, 16 of which were not previously reported in the literature. We grouped outcomes into 10 domains, using a modified COMET taxonomy: 1) mortality/survival; 2) general physiological/clinical outcomes; 3) psychiatric outcomes; 4) spiritual/religious/existential functioning/wellbeing; 5) emotional functioning/wellbeing; 6) social functioning; 7) delivery of care; 8) perceived health status; 9) personal circumstances; 10) societal/carer burden. Conclusions: This study offers valuable insights into outcomes significant to key stakeholders, underscoring the need for a patient-centred approach in research and clinical practice in prognostication in advanced cancer. These outcomes will play a key role in the development of a Core Outcome Set to assess the impact of prognostication in advanced cancer.
Is the Price Right? Reconceptualizing Price and Income Elasticity to Anticipate Price Perception Issues

Price perception by consumers represents a challenge to the ability of a business to correctly and profitably price and sell their products or services in a given market and any new target market. Complicating the perception of prices is the dynamics of price and income elasticity, both of which are key for estimating demand. This article proposes a novel and elegant identity that conceptualizes elasticity as a means of quantifying the potential for price perception problems in a market. Elasticity studies from 1990 to 2023 (n=30) were sampled to evaluate the relationship between price and income elasticity for various consumer commodities using the identity. The results suggest that, given known price and income elasticity values, a business can anticipate pricing perception problems in advance and address the potential for damaging distortion of their value proposition. Further, the business can use this insight to correctly choose a strategic pricing approach.
Optimizing for ROC Curves on Class-Imbalanced Data by Training over a Family of Loss Functions

Although binary classification is a well-studied problem in computer vision, training reliable classifiers under severe class imbalance remains a challenging problem. Recent work has proposed techniques that mitigate the effects of training under imbalance by modifying the loss functions or optimization methods. While this work has led to significant improvements in the overall accuracy in the multi-class case, we observe that slight changes in hyperparameter values of these methods can result in highly variable performance in terms of Receiver Operating Characteristic (ROC) curves on binary problems with severe imbalance. To reduce the sensitivity to hyperparameter choices and train more general models, we propose training over a family of loss functions, instead of a single loss function. We develop a method for applying Loss Conditional Training (LCT) to an imbalanced classification problem. Extensive experiment results, on both CIFAR and Kaggle competition datasets, show that our method improves model performance and is more robust to hyperparameter choices. Code will be made available at: https://github.com/klieberman/roc_lct.
Zeroth-order Low-rank Hessian Estimation via Matrix Recovery

A zeroth-order Hessian estimator aims to recover the Hessian matrix of an objective function at any given point, using minimal finite-difference computations. This paper studies zeroth-order Hessian estimation for low-rank Hessians, from a matrix recovery perspective. Our challenge lies in the fact that traditional matrix recovery techniques are not directly suitable for our scenario. They either demand incoherence assumptions (or its variants), or require an impractical number of finite-difference computations in our setting. To overcome these hurdles, we employ zeroth-order Hessian estimations aligned with proper matrix measurements, and prove new recovery guarantees for these estimators. More specifically, we prove that for a Hessian matrix $H \in \mathbb{R}^{n \times n}$ of rank $r$, $ \mathcal{O}(nr^2 \log^2 n ) $ proper zeroth-order finite-difference computations ensures a highly probable exact recovery of $H$. Compared to existing methods, our method can greatly reduce the number of finite-difference computations, and does not require any incoherence assumptions.
FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs

Our society is facing rampant misinformation harming public health and trust. To address the societal challenge, we introduce FACT-GPT, a system leveraging Large Language Models (LLMs) to automate the claim matching stage of fact-checking. FACT-GPT, trained on a synthetic dataset, identifies social media content that aligns with, contradicts, or is irrelevant to previously debunked claims. Our evaluation shows that our specialized LLMs can match the accuracy of larger models in identifying related claims, closely mirroring human judgment. This research provides an automated solution for efficient claim matching, demonstrates the potential of LLMs in supporting fact-checkers, and offers valuable resources for further research in the field.
A characterization of ramification groups via Taylor morphism

In this paper, we introduce an analogue of the Taylor map within the category of commutative unitary rings. The establishment of characterizations for formal smoothness and results on derivation permits us to compute the kernel of this Taylor map within a wide-ranging context. From there, we characterize the conventional higher ramification groups in terms of the inertia groups of a certain jet algebra. Given the functoriality of this approach, it will in principle be possible to follow the same construction in the context of algebraic geometry, thus defining the higher ramification groups associated to certain group schemes.
Learning Uncertainty-Aware Temporally-Extended Actions

In reinforcement learning, temporal abstraction in the action space, exemplified by action repetition, is a technique to facilitate policy learning through extended actions. However, a primary limitation in previous studies of action repetition is its potential to degrade performance, particularly when sub-optimal actions are repeated. This issue often negates the advantages of action repetition. To address this, we propose a novel algorithm named Uncertainty-aware Temporal Extension (UTE). UTE employs ensemble methods to accurately measure uncertainty during action extension. This feature allows policies to strategically choose between emphasizing exploration or adopting an uncertainty-averse approach, tailored to their specific needs. We demonstrate the effectiveness of UTE through experiments in Gridworld and Atari 2600 environments. Our findings show that UTE outperforms existing action repetition algorithms, effectively mitigating their inherent limitations and significantly enhancing policy learning efficiency.
Can quantum statistics help distinguish Dirac from Majorana neutrinos?

Finding out if neutrinos are Dirac or Majorana particles is known to be extremely difficult due to the smallness of neutrino mass and the fact that in the limit $m_\nu=0$ both Dirac and Majorana neutrinos become Weyl particles, i.e. are indistinguishable. There have been suggestions in the literature that in the case of processes with production of a neutrino-antineutrino pair (if neutrinos are Dirac particles) or two neutrinos (if they are of Majorana nature) quantum statistics may be of help. This is because for Majorana neutrinos quantum indistinguishability of identical particles requires the amplitude of the process to be antisymmetrized with respect to the interchange of the final-state neutrinos, whereas no such antisymmetrization must be done for Dirac neutrinos. It has been claimed that the resulting differences between the cross sections for Dirac and Majorana neutrinos persist even for arbitrarily small but not exactly vanishing neutrino mass. We demonstrate that, at least in the framework of the Standard Model, this is not the case. We also give a general proof that within the Standard Model quantum statistics does not help tell Dirac and Majorana neutrinos apart in the limit of negligibly small $m_\nu/E$.
InstaGen: Enhancing Object Detection by Training on Synthetic Dataset

In this paper, we introduce a novel paradigm to enhance the ability of object detector, e.g., expanding categories or improving detection performance, by training on synthetic dataset generated from diffusion models. Specifically, we integrate an instance-level grounding head into a pre-trained, generative diffusion model, to augment it with the ability of localising arbitrary instances in the generated images. The grounding head is trained to align the text embedding of category names with the regional visual feature of the diffusion model, using supervision from an off-the-shelf object detector, and a novel self-training scheme on (novel) categories not covered by the detector. This enhanced version of diffusion model, termed as InstaGen, can serve as a data synthesizer for object detection. We conduct thorough experiments to show that, object detector can be enhanced while training on the synthetic dataset from InstaGen, demonstrating superior performance over existing state-of-the-art methods in open-vocabulary (+4.5 AP) and data-sparse (+1.2 to 5.2 AP) scenarios.
Discovering Temporally-Aware Reinforcement Learning Algorithms

Recent advancements in meta-learning have enabled the automatic discovery of novel reinforcement learning algorithms parameterized by surrogate objective functions. To improve upon manually designed algorithms, the parameterization of this learned objective function must be expressive enough to represent novel principles of learning (instead of merely recovering already established ones) while still generalizing to a wide range of settings outside of its meta-training distribution. However, existing methods focus on discovering objective functions that, like many widely used objective functions in reinforcement learning, do not take into account the total number of steps allowed for training, or"training horizon". In contrast, humans use a plethora of different learning objectives across the course of acquiring a new ability. For instance, students may alter their studying techniques based on the proximity to exam deadlines and their self-assessed capabilities. This paper contends that ignoring the optimization time horizon significantly restricts the expressive potential of discovered learning algorithms. We propose a simple augmentation to two existing objective discovery approaches that allows the discovered algorithm to dynamically update its objective function throughout the agent's training procedure, resulting in expressive schedules and increased generalization across different training horizons. In the process, we find that commonly used meta-gradient approaches fail to discover such adaptive objective functions while evolution strategies discover highly dynamic learning rules. We demonstrate the effectiveness of our approach on a wide range of tasks and analyze the resulting learned algorithms, which we find effectively balance exploration and exploitation by modifying the structure of their learning rules throughout the agent's lifetime.
Binding Dynamics in Rotating Features

In human cognition, the binding problem describes the open question of how the brain flexibly integrates diverse information into cohesive object representations. Analogously, in machine learning, there is a pursuit for models capable of strong generalization and reasoning by learning object-centric representations in an unsupervised manner. Drawing from neuroscientific theories, Rotating Features learn such representations by introducing vector-valued features that encapsulate object characteristics in their magnitudes and object affiliation in their orientations. The"$\chi$-binding"mechanism, embedded in every layer of the architecture, has been shown to be crucial, but remains poorly understood. In this paper, we propose an alternative"cosine binding"mechanism, which explicitly computes the alignment between features and adjusts weights accordingly, and we show that it achieves equivalent performance. This allows us to draw direct connections to self-attention and biological neural processes, and to shed light on the fundamental dynamics for object-centric representations to emerge in Rotating Features.
Natural Language User Profiles for Transparent and Scrutable Recommendations

Current state-of-the-art recommender systems predominantly rely on either implicit or explicit feedback from users to suggest new items. While effective in recommending novel options, these conventional systems often use uninterpretable embeddings. This lack of transparency not only limits user understanding of why certain items are suggested but also reduces the user's ability to easily scrutinize and edit their preferences. For example, if a user has a change in interests, they would need to make significant changes to their interaction history to adjust the model's recommendations. To address these limitations, we introduce a novel method that utilizes user reviews to craft personalized, natural language profiles describing users' preferences. Through these descriptive profiles, our system provides transparent recommendations in natural language. Our evaluations show that this novel approach maintains a performance level on par with established recommender systems, but with the added benefits of transparency and user control. By enabling users to scrutinize why certain items are recommended, they can more easily verify, adjust, and have greater autonomy over their recommendations.
Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming

Mixed Integer Linear Programming (MILP) is a pillar of mathematical optimization that offers a powerful modeling language for a wide range of applications. During the past decades, enormous algorithmic progress has been made in solving MILPs, and many commercial and academic software packages exist. Nevertheless, the availability of data, both from problem instances and from solvers, and the desire to solve new problems and larger (real-life) instances, trigger the need for continuing algorithmic development. MILP solvers use branch and bound as their main component. In recent years, there has been an explosive development in the use of machine learning algorithms for enhancing all main tasks involved in the branch-and-bound algorithm, such as primal heuristics, branching, cutting planes, node selection and solver configuration decisions. This paper presents a survey of such approaches, addressing the vision of integration of machine learning and mathematical optimization as complementary technologies, and how this integration can benefit MILP solving. In particular, we give detailed attention to machine learning algorithms that automatically optimize some metric of branch-and-bound efficiency. We also address how to represent MILPs in the context of applying learning algorithms, MILP benchmarks and software.
Morphological Characteristics of the Thymus and Spleen and the Subpopulation Composition of Lymphocytes in Peripheral Blood during Systemic Inflammatory Response in Male Rats with Different Resistance to Hypoxia

On the model of the systemic inflammatory response (SIRS), induced by lipopolysaccharide (LPS), the morphological and functional changes in the thymus and spleen and the subpopulation composition of peripheral blood lymphocytes of rats differing in resistance to hypoxia were studied. It was demonstrated that the level of endotoxin in blood serum after 3 hours of LPS administration in susceptible-to-hypoxia rats was 64 times higher than in the control group, while in tolerant-to-hypoxia animals it was only 8 times higher in 6 hours. After 24 hours of LPS injection, only in susceptible-to-hypoxia rats did the level of C-reactive protein in blood serum increase. There is a difference in the dynamics of morphological changes of lymphoid organs after LPS injection in tolerant- and susceptible-to-hypoxia animals. After 3 hours of LPS administration, the tolerant-to-hypoxia rats showed no changes in the thymus, spleen, and subpopulation composition of lymphocytes in peripheral blood. After 6 hours there was only a decrease in B-lymphocytes and increase in cytotoxic T-lymphocytes and NK cells. After 1 day of LPS injection, the tolerant-to-hypoxia rats had devastation in PALS of the spleen. After 3 hours of LPS injection the susceptible-to-hypoxia animals had reactive changes in the lymphoid organs: decrease of the thymus cortex, narrowing of the marginal zones of spleen lymphoid follicles, widening of their germinal centers, and a decrease in the absolute number of cytotoxic T-lymphocytes, NK cells, and B-lymphocytes. After 24 hours of LPS injection the tolerant-to-hypoxia animals had a greater absolute number of T-lymphocytes and NK cells in comparison with the susceptible rats. Thus, in animals with different resistance to hypoxia the LPS-induced SIRS is characterized by different dynamics of morphological and functional changes of the thymus and spleen. The obtained data will serve as a basis for the development of new individual approaches to the prevention and treatment of infectious and inflammatory diseases.
A Mechanical Origin of Cooperative Transport

Cooperative transport is a striking phenomenon where multiple agents join forces to transit a payload too heavy for the individual. While social animals such as ants are routinely observed to coordinate transport at scale, reproducing the effect in artificial swarms remains challenging, as it requires synchronization in a noisy many-body system. Here we show that cooperative transport spontaneously emerges in swarms of stochastic self-propelled agents, without requiring any form of sensing, feedback, or control. We find that a minute modification to the mechanical design of the individual agent dramatically changes its alignment response to an external force. We then show experimentally that with the proper design, a swarm of active particles spontaneously cooperates in the directional transport of larger objects. Surprisingly, transport increases with increasing payload size. A mechanical, coarse-grained description reveals that force-alignment is intrinsic and captured by a signed, charge-like parameter with units of curvature. Numerical simulations of swarms of active particles with a negative active charge corroborate the experimental findings. We analytically derive a geometrical criterion for cooperative transport which results from a bifurcation in a non-linear dynamical system. Our findings generalize existing models of active particles, offer new design rules for distributed robotic systems, and shed light on cooperation in natural swarms.
Fixed width treelike neural networks capacity analysis - generic activations

We consider the capacity of \emph{treelike committee machines} (TCM) neural networks. Relying on Random Duality Theory (RDT), \cite{Stojnictcmspnncaprdt23} recently introduced a generic framework for their capacity analysis. An upgrade based on the so-called \emph{partially lifted} RDT (pl RDT) was then presented in \cite{Stojnictcmspnncapliftedrdt23}. Both lines of work focused on the networks with the most typical, \emph{sign}, activations. Here, on the other hand, we focus on networks with other, more general, types of activations and show that the frameworks of \cite{Stojnictcmspnncaprdt23,Stojnictcmspnncapliftedrdt23} are sufficiently powerful to enable handling of such scenarios as well. In addition to the standard \emph{linear} activations, we uncover that particularly convenient results can be obtained for two very commonly used activations, namely, the \emph{quadratic} and \emph{rectified linear unit (ReLU)} ones. In more concrete terms, for each of these activations, we obtain both the RDT and pl RDT based memory capacities upper bound characterization for \emph{any} given (even) number of the hidden layer neurons, $d$. In the process, we also uncover the following two, rather remarkable, facts: 1) contrary to the common wisdom, both sets of results show that the bounding capacity decreases for large $d$ (the width of the hidden layer) while converging to a constant value; and 2) the maximum bounding capacity is achieved for the networks with precisely \textbf{\emph{two}} hidden layer neurons! Moreover, the large $d$ converging values are observed to be in excellent agrement with the statistical physics replica theory based predictions.