Miracle-dz/UniFAR-FTUs huggingface dataset:
CARE: Extracting Experimental Findings From Clinical Literature

Extracting fine-grained experimental findings from literature can provide massive utility for scientific applications. Prior work has focused on developing annotation schemas and datasets for limited aspects of this problem, leading to simpler information extraction datasets which do not capture the real-world complexity and nuance required for this task. Focusing on biomedicine, this work presents CARE (Clinical Aggregation-oriented Result Extraction) -- a new IE dataset for the task of extracting clinical findings. We develop a new annotation schema capturing fine-grained findings as n-ary relations between entities and attributes, which includes phenomena challenging for current IE systems such as discontinuous entity spans, nested relations, and variable arity n-ary relations. Using this schema, we collect extensive annotations for 700 abstracts from two sources: clinical trials and case reports. We also benchmark the performance of various state-of-the-art IE systems on our dataset, including extractive models and generative LLMs in fully supervised and limited data settings. Our results demonstrate the difficulty of our dataset -- even SOTA models such as GPT4 struggle, particularly on relation extraction. We release our annotation schema and CARE to encourage further research on extracting and aggregating scientific findings from literature.
Topological gap opening without symmetry breaking from dynamical quantum correlations

Topological phase transitions are typically associated with the formation of gapless states. Spontaneous symmetry breaking can lead to a gap opening thereby obliterating the topological nature of the system. Here we highlight a completely different destiny for a topological transition in presence of interaction. Solving a Bernevig-Hughes-Zhang model with local interaction, we show that dynamical quantum fluctuations can lead to the opening of a gap without any symmetry breaking. As we vary the interaction and the bare mass of the model, the continuous gapless topological transition turns into a first-order one, associated with the presence of massive Dirac fermion at the transition point showing a Gross-Neveu critical behaviour near the quantum critical endpoint. We identify the gap opening as a condensed matter analog of the Coleman-Weinberg mechanism of mass generation.
You don't need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instruments

The versatility of Large Language Models (LLMs) on natural language understanding tasks has made them popular for research in social sciences. In particular, to properly understand the properties and innate personas of LLMs, researchers have performed studies that involve using prompts in the form of questions that ask LLMs of particular opinions. In this study, we take a cautionary step back and examine whether the current format of prompting enables LLMs to provide responses in a consistent and robust manner. We first construct a dataset that contains 693 questions encompassing 39 different instruments of persona measurement on 115 persona axes. Additionally, we design a set of prompts containing minor variations and examine LLM's capabilities to generate accurate answers, as well as consistency variations to examine their consistency towards simple perturbations such as switching the option order. Our experiments on 15 different open-source LLMs reveal that even simple perturbations are sufficient to significantly downgrade a model's question-answering ability, and that most LLMs have low negation consistency. Our results suggest that the currently widespread practice of prompting is insufficient to accurately capture model perceptions, and we discuss potential alternatives to improve such issues.
Adversarially Robust Spiking Neural Networks Through Conversion

Spiking neural networks (SNNs) provide an energy-efficient alternative to a variety of artificial neural network (ANN) based AI applications. As the progress in neuromorphic computing with SNNs expands their use in applications, the problem of adversarial robustness of SNNs becomes more pronounced. To the contrary of the widely explored end-to-end adversarial training based solutions, we address the limited progress in scalable robust SNN training methods by proposing an adversarially robust ANN-to-SNN conversion algorithm. Our method provides an efficient approach to embrace various computationally demanding robust learning objectives that have been proposed for ANNs. During a post-conversion robust finetuning phase, our method adversarially optimizes both layer-wise firing thresholds and synaptic connectivity weights of the SNN to maintain transferred robustness gains from the pre-trained ANN. We perform experimental evaluations in numerous adaptive adversarial settings that account for the spike-based operation dynamics of SNNs, and show that our approach yields a scalable state-of-the-art solution for adversarially robust deep SNNs with low-latency.
Soft Matching Distance: A metric on neural representations that captures single-neuron tuning

Common measures of neural representational (dis)similarity are designed to be insensitive to rotations and reflections of the neural activation space. Motivated by the premise that the tuning of individual units may be important, there has been recent interest in developing stricter notions of representational (dis)similarity that require neurons to be individually matched across networks. When two networks have the same size (i.e. same number of neurons), a distance metric can be formulated by optimizing over neuron index permutations to maximize tuning curve alignment. However, it is not clear how to generalize this metric to measure distances between networks with different sizes. Here, we leverage a connection to optimal transport theory to derive a natural generalization based on"soft"permutations. The resulting metric is symmetric, satisfies the triangle inequality, and can be interpreted as a Wasserstein distance between two empirical distributions. Further, our proposed metric avoids counter-intuitive outcomes suffered by alternative approaches, and captures complementary geometric insights into neural representations that are entirely missed by rotation-invariant metrics.
DRIVER RESPONSE TO DELINEATION TREATMENTS ON HORIZONTAL CURVES ON TWO-LANE ROADS

16. Abstract The delineation of horizontal curves on two-lane rural roads is an important component of safety improvements to reduce run-off-road and head-on crashes. This project assessed four types of vertical delineation in conjunction with edgeline markings through a closed-course nighttime driving test, a survey of drivers using video clips of curves, and a field test of vehicle performance at four sites in rural Texas. The treatments evaluated were standard post-mounted delineators with a single reflector at top and one with retroreflective material the full length of the post, standard chevrons, and chevrons with yellow retroreflective material applied the full length of the post. The results show that vertical delineation of any type improves vehicle lane position at the entry and mid-point of horizontal curves. Fully reflective postmounted delineators improved lane position and reduced encroachment more than standard posts. The two styles of chevrons performed equally well and both showed significant speed reduction when compared to pavement markings alone.
Waring Problem For Triangular Matrix Algebra

The Matrix Waring problem is if we can write every matrix as a sum of $k$-th powers. Here, we look at the same problem for triangular matrix algebra $T_n(\mathbb{F}_q)$ consisting of upper triangular matrices over a finite field. We prove that for all integers $k, n \geq 1$, there exists a constant $\mathcal C(k, n)$, such that for all $q>\mathcal C(k,n)$, every matrix in $T_n(\mathbb{F}_q)$ is a sum of three $k$-th powers. Moreover, if $-1$ is $k$-th power in $\mathbb{F}_q$, then for all $q>\mathcal C(k,n)$, every matrix in $T_n(\mathbb{F}_q)$ is a sum of two $k$-th powers. We make use of Lang-Weil estimates about the number of solutions of equations over finite fields to achieve the desired results.
Chromatic functions, interval orders and increasing forests

The chromatic quasisymmetric functions (csf) of Shareshian and Wachs associated to unit interval orders have attracted a lot of interest since their introduction in 2016, both in combinatorics and geometry, because of their relation to the famous Stanley-Stembridge conjecture (1993) and to the topology of Hessenberg varieties, respectively. In the present work we study the csf associated to the larger class of interval orders with no restriction on the length of the intervals. Inspired by an article of Abreu and Nigro, we show that these csf are weighted sums of certain quasisymmetric functions associated to the increasing spanning forests of the associated incomparability graphs. Furthermore, we define quasisymmetric functions that include the unicellular LLT symmetric functions and generalize an identity due to Carlsson and Mellit. Finally we conjecture a formula giving their expansion in the type 1 power sum quasisymmetric functions which should extend a theorem of Athanasiadis.
Quantifying the impact of submesoscale processes on the spring phytoplankton bloom in a turbulent upper ocean using a Lagrangian approach

The spring phytoplankton bloom in the subpolar North Atlantic and the mechanisms controlling its evolution and onset have important consequences for marine ecosystems and carbon cycling. Submesoscale mixed layer eddies (MLEs) play a role in the onset of the bloom by creating localized stratification and alleviating phytoplankton light limitation; however, the importance of MLEs for phytoplankton in a turbulent surface mixed layer has not yet been examined. We explore the effect of MLEs on phytoplankton by simulating their trajectories with Lagrangian particles subject to turbulent vertical displacements in an MLE‐resolving model. By tracking the light exposure of the simulated phytoplankton, we find that MLEs can advance the timing of the spring bloom by 1 to 2 weeks, depending on surface forcing conditions. The onset of the bloom is linked with the onset of positive heat fluxes, whether or not MLEs are present.
Meridional Circulations of the Solar Magnetic Fields of Different Strength

The meridional circulation of the solar magnetic fields in Solar Cycles 21-24 was considered. Data from both ground-based and space observatories were used. Three types of time-latitude distributions of photospheric magnetic fields and their meridional circulations were identified depending on the magnetic field intensity. (i) low-strength magnetic fields. They were distributed evenly across latitude and weakly depended on the magnetic fields of active regions and their cycle variation; (ii) medium-strength magnetic fields. For these fields a wave-like, pole-to-pole, antiphase meridional circulation with a period of approximately 22 years was revealed. The velocities of meridional flows were slower at the minima of solar activity, when they were at high latitudes in the opposite hemispheres, and maximal at the solar maxima, when the positive- and negative-polarity waves crossed the equator. The meridional circulation of these fields reflects the solar global magnetic field dynamics and determines the solar polar field reversal; (iii) high-strength (active region) magnetic fields. They were distributed symmetrically in the Northern and Southern hemispheres. Magnetic fields of both leading and following sunspot polarity migrated from high to low latitudes. The meridional-flow velocities of high-strength magnetic fields were higher at the rising and maxima phases than at the minima. Some of the high-latitude active region magnetic fields were captured by the second type meridional circulation flows and transported along with them to the appropriate pole. But the magnetic fields of active regions are not the main ones in the solar polar field reversal. The results indicate that high-strength magnetic fields were not the main source of weak ones.
Tight lower bound on $|A+\lambda A|$ for algebraic integer $\lambda$

We prove an asymptotically tight lower bound on $|A+\lambda A|$ for $A\subset \mathbb{C}$ and algebraic integer $\lambda$. The proof combines strong version of Freiman's theorem, structural theorem on dense subsets of a hypercubic lattice and a generalisation of the continuous result on tight bound for the measure of $K+\tau K$ for a compact subset $K\subset \mathbb{R}^d$ of unit Lebesgue measure and a fixed linear operator $\tau\colon\mathbb{R}^d\to \mathbb{R}^d$, obtained in our previous work.
A Dichotomy Hierarchy Characterizing Linear Time Subgraph Counting in Bounded Degeneracy Graphs

Subgraph and homomorphism counting are fundamental algorithmic problems. Given a constant-sized pattern graph $H$ and a large input graph $G$, we wish to count the number of $H$-homomorphisms/subgraphs in $G$. Given the massive sizes of real-world graphs and the practical importance of counting problems, we focus on when (near) linear time algorithms are possible. The seminal work of Chiba-Nishizeki (SICOMP 1985) shows that for bounded degeneracy graphs $G$, clique and $4$-cycle counting can be done linear time. Recent works (Bera et al, SODA 2021, JACM 2022) show a dichotomy theorem characterizing the patterns $H$ for which $H$-homomorphism counting is possible in linear time, for bounded degeneracy inputs $G$. At the other end, Ne\v{s}et\v{r}il and Ossona de Mendez used their deep theory of"sparsity"to define bounded expansion graphs. They prove that, for all $H$, $H$-homomorphism counting can be done in linear time for bounded expansion inputs. What lies between? For a specific $H$, can we characterize input classes where $H$-homomorphism counting is possible in linear time? We discover a hierarchy of dichotomy theorems that precisely answer the above questions. We show the existence of an infinite sequence of graph classes $\mathcal{G}_0$ $\supseteq$ $\mathcal{G}_1$ $\supseteq$ . $\supseteq$ $\mathcal{G}_\infty$ where $\mathcal{G}_0$ is the class of bounded degeneracy graphs, and $\mathcal{G}_\infty$ is the class of bounded expansion graphs. Fix any constant sized pattern graph $H$. Let $LICL(H)$ denote the length of the longest induced cycle in $H$. We prove the following. If $LICL(H)<3(r+2)$, then $H$-homomorphisms can be counted in linear time for inputs in $\mathcal{G}_r$. If $LICL(H) \geq 3(r+2)$, then $H$-homomorphism counting on inputs from $\mathcal{G}_r$ takes $\Omega(m^{1+\gamma})$ time. We prove similar dichotomy theorems for subgraph counting.
Glacier Variations since the Early 20th Century in the Gangrigabu Range, Southeast Tibetan Plateau

Recent studies indicate that most alpine glaciers around the world have retreated during the past decades under global warming. In this study, a region with temperate glaciers, mainly influenced by the Indian Monsoon, is selected to examine the present status of glaciers under a warming background. Aerial photos, topographical maps, China-Brazil Earth Resource Satellite (CBERS, with resolution of 19 m and acquired on Jan. 30 2001) and Landsat Thematic Mapper (TM, acquired on Oct. 26 1999) images are used. Aerial photos are mainly applied for identifying the end and lateral moraines formed at the end of the Little Ice Age, which was thought to occur in the mid-1920s. CBERS and Landsat TM images geometrically corrected and co-registered to 1∶{100 000} topographical maps are applied to extract the information of glacier distribution at the time the imageries acquired. It was concluded that the measured 102 glaciers in the region have all in retreat from the 1920s to 1980, with a total areal and ice volumetric decreases of 47^9 km~(2) and 6^95 km~(3). It is estimated that all glaciers in the region have lost 13^8% of their total area and 9^8% of the total ice volume during the same period. Since 1980, glaciers in the region have also experienced an ice mass loss process in general, according to the data of 88 glaciers with a total area of 797^78 km~(2) from topographical maps in 1980 and the CBERS image in 2001. About 60% of these glaciers have been losing their mass and other glaciers have advanced. Analysis indicates that glacier retreat has significantly impacted glacial meltwater runoff, resulting in 50% decrease of runoff from 1915 to 1980. The trend of glacier retreat and climatic warming during the last century, especial during the past two decades, shows a possibility that mass loss of glaciers in the region will be accelerating in the future.
Language Generation from Brain Recordings

Generating human language through non-invasive brain-computer interfaces (BCIs) has the potential to unlock many applications, such as serving disabled patients and improving communication. Currently, however, generating language via BCIs has been previously successful only within a classification setup for selecting pre-generated sentence continuation candidates with the most likely cortical semantic representation. Inspired by recent research that revealed associations between the brain and the large computational language models, we propose a generative language BCI that utilizes the capacity of a large language model (LLM) jointly with a semantic brain decoder to directly generate language from functional magnetic resonance imaging (fMRI) input. The proposed model can generate coherent language sequences aligned with the semantic content of visual or auditory language stimuli perceived, without prior knowledge of any pre-generated candidates. We compare the language generated from the presented model with a random control, pre-generated language selection approach, and a standard LLM, which generates common coherent text solely based on the next word likelihood according to statistical language training data. The proposed model is found to generate language that is more aligned with semantic stimulus in response to which brain input is sampled. Our findings demonstrate the potential and feasibility of employing BCIs in direct language generation.
Contrastive Chain-of-Thought Prompting

Despite the success of chain of thought in enhancing language model reasoning, the underlying process remains less well understood. Although logically sound reasoning appears inherently crucial for chain of thought, prior studies surprisingly reveal minimal impact when using invalid demonstrations instead. Furthermore, the conventional chain of thought does not inform language models on what mistakes to avoid, which potentially leads to more errors. Hence, inspired by how humans can learn from both positive and negative examples, we propose contrastive chain of thought to enhance language model reasoning. Compared to the conventional chain of thought, our approach provides both valid and invalid reasoning demonstrations, to guide the model to reason step-by-step while reducing reasoning mistakes. To improve generalization, we introduce an automatic method to construct contrastive demonstrations. Our experiments on reasoning benchmarks demonstrate that contrastive chain of thought can serve as a general enhancement of chain-of-thought prompting.
On computability of equilibrium states

Equilibrium states are natural dynamical analogs of Gibbs states in thermodynamic formalism. This paper is devoted to the study of their computability in the sense of Computable Analysis. We show that the unique equilibrium state associated to a pair of a computable, topologically exact, distance-expanding, open map $T\colon X\rightarrow X$ and a computable H\"older continuous potential $\varphi\colon X\rightarrow\mathbb{R}$ is always computable. Furthermore, the Hausdorff dimension of the Julia set and the equilibrium state for the geometric potential of a computable hyperbolic rational map are computable. On the other hand, we introduce a mechanism to establish non-uniqueness of equilibrium states. We also present some computable dynamical systems whose equilibrium states are all non-computable.
Impact of a block structure on the Lotka-Volterra model

The Lotka-Volterra (LV) model is a simple, robust, and versatile model used to describe large interacting systems such as food webs or microbiomes. The model consists of $n$ coupled differential equations linking the abundances of $n$ different species. We consider a large random interaction matrix with independent entries and a block variance profile. The $i$th diagonal block represents the intra-community interaction in community $i$, while the off-diagonal blocks represent the inter-community interactions. The variance remains constant within each block, but may vary across blocks. We investigate the important case of two communities of interacting species, study how interactions affect their respective equilibrium. We also describe equilibrium with feasibility (i.e., whether there exists an equilibrium with all species at non-zero abundances) and the existence of an attrition phenomenon (some species may vanish) within each community. Information about the general case of $b$ communities ($b>2$) is provided in the appendix.
Leptogenesis and dark matter in minimal inverse seesaw using $A_4$ modular symmetry

In this paper we have studied neutrino masses and mixings by adding a scalar triplet $\eta$ to the particle content of minimal inverse seesaw. We have realised this extension of minimal inverse seesaw by implementing an isomorphic modular group $\Gamma(3)$ and a non-abelian discrete symmetry group $A_4$. We have also used $Z_3$ symmetry group to restrain certain interaction terms in the lagrangian of the model. We have studied baryon asymmetry of the universe, neutrinoless double-beta decay and dark matter in our work. In order to check the consistency of our model with various experimental constraints, we have therefore calculated effective mass, relic density and baryogenesis via leptogenesis. Interestingly, we have found our model quite compatible with the experimental bounds and is also successful in producing the neutrino masses and mixings in the 3$\sigma$ range.
A branching model for intergenerational telomere length dynamics

We build and study an individual based model of the telomere length's evolution in a population across multiple generations. This model is a continuous time typed branching process, where the type of an individual includes its gamete mean telomere length and its age. We study its Malthusian's behaviour and provide numerical simulations to understand the influence of biologically relevant parameters.