Miracle-dz/UniFAR-FTUs huggingface dataset:
SMX: Sequential Monte Carlo Planning for Expert Iteration

Developing agents that can leverage planning abilities during their decision and learning processes is critical to the advancement of Artificial Intelligence. Recent works have demonstrated the effectiveness of combining tree-based search methods and self-play learning mechanisms. Yet, these methods typically face scaling challenges due to the sequential nature of their search. While practical engineering solutions can partly overcome this, they still demand extensive computational resources, which hinders their applicability. In this paper, we introduce SMX, a model-based planning algorithm that utilises scalable Sequential Monte Carlo methods to create an effective self-learning mechanism. Grounded in the theoretical framework of control as inference, SMX benefits from robust theoretical underpinnings. Its sampling-based search approach makes it adaptable to environments with both discrete and continuous action spaces. Furthermore, SMX allows for high parallelisation and can run on hardware accelerators to optimise computing efficiency. SMX demonstrates a statistically significant improvement in performance compared to AlphaZero, as well as demonstrating its performance as an improvement operator for a model-free policy, matching or exceeding top model-free methods across both continuous and discrete environments.
Finite density QCD equation of state: critical point and lattice-based $T'$-expansion

We present a novel construction of the QCD equation of state (EoS) at finite baryon density. Our work combines a recently proposed resummation scheme for lattice QCD results with the universal critical behavior at the QCD critical point. This allows us to obtain a family of equations of state in the range $0 \leq \mu_B \leq 700$ MeV and 25 MeV $\leq T \leq 800$ MeV, which match lattice QCD results near $\mu_B=0$ while featuring a critical point in the 3D Ising model universality class. The position of the critical point can be chosen within the range accessible to beam-energy scan heavy-ion collision experiments. The strength of the singularity and the shape of the critical region are parameterized using a standard parameter set. We impose stability and causality constraints and discuss the available ranges of critical point parameter choices, finding that they extend beyond earlier parametric QCD EoS proposals. We present thermodynamic observables, including baryon density, pressure, entropy density, energy density, baryon susceptibility and speed of sound, that cover a wide range in the QCD phase diagram relevant for experimental exploration.
Assessing the Privacy Risk of Cross-Platform Identity Linkage using Eye Movement Biometrics

The recent emergence of ubiquitous, multi-platform eye tracking has raised user privacy concerns involving a threat that we have termed “cross-platform identity linkage. ” This privacy violation may occur when a person is re-identified across multiple eye tracking-enabled platforms using personally identifying information that is implicitly expressed through their eye movement. We present an empirical investigation quantifying a modern eye movement biometric model’s ability to link subject identities across three different eye tracking devices using eye movement signals from each device. We show that a state-of-the art eye movement biometrics model demonstrates above-chance levels of biometric performance (34.99% equal error rate, 15% rank-1 identification rate) when linking user identities across one pair of devices, but not for the other. Considering these findings, we also discuss the impact that eye tracking signal quality has on the model’s ability to meaningfully associate a subject’s identity between two substantially different eye tracking devices. Our investigation advances a fundamental understanding of the privacy risks for identity linkage across platforms by employing both quantitative and qualitative measures of biometric performance, including a visualization of the model’s ability to distinguish genuine and imposter authentication attempts across platforms.
Learning Cartesian Product Graphs with Laplacian Constraints

Graph Laplacian learning, also known as network topology inference, is a problem of great interest to multiple communities. In Gaussian graphical models (GM), graph learning amounts to endowing covariance selection with the Laplacian structure. In graph signal processing (GSP), it is essential to infer the unobserved graph from the outputs of a filtering system. In this paper, we study the problem of learning Cartesian product graphs under Laplacian constraints. The Cartesian graph product is a natural way for modeling higher-order conditional dependencies and is also the key for generalizing GSP to multi-way tensors. We establish statistical consistency for the penalized maximum likelihood estimation (MLE) of a Cartesian product Laplacian, and propose an efficient algorithm to solve the problem. We also extend our method for efficient joint graph learning and imputation in the presence of structural missing values. Experiments on synthetic and real-world datasets demonstrate that our method is superior to previous GSP and GM methods.
Uncertainty Quantification via Stable Distribution Propagation

We propose a new approach for propagating stable probability distributions through neural networks. Our method is based on local linearization, which we show to be an optimal approximation in terms of total variation distance for the ReLU non-linearity. This allows propagating Gaussian and Cauchy input uncertainties through neural networks to quantify their output uncertainties. To demonstrate the utility of propagating distributions, we apply the proposed method to predicting calibrated confidence intervals and selective prediction on out-of-distribution data. The results demonstrate a broad applicability of propagating distributions and show the advantages of our method over other approaches such as moment matching.
Extension of Theory of Gravitomagnetism and Spinor Quantum Mechanics with Dynamics of Free Electromagnetic Field

The theory of Gravitomagnetism and spinor quantum mechanics describing the interaction between the Dirac spinor field, the electromagnetic field, and a weak gravitational field is extended by including the Lagrangian density of the free electromagnetic field. It is shown that the newly added term in the Lagrangian density is necessary to restore a symmetric energy-momentum tensor in the interaction term of the Lagrangian density within the theory. We prove that when the electromagnetic energy term is missing, the tensor in the interaction Lagrangian density does not represent the energy-momentum tensor of the Dirac field minimally coupled to the electromagnetic field on flat space-time. Moreover, it follows that the Lagrangian density remains unchanged regardless of whether the theory is developed from the principle of general covariance or is defined as a flat space-time theory with the gravitational field treated as a regular tensor field, in analogy to the electromagnetic field. This contradicts previous findings.
NeuRes: Learning Proofs of Propositional Satisfiability

We introduce NeuRes, a neuro-symbolic proof-based SAT solver. Unlike other neural SAT solving methods, NeuRes is capable of proving unsatisfiability as opposed to merely predicting it. By design, NeuRes operates in a certificate-driven fashion by employing propositional resolution to prove unsatisfiability and to accelerate the process of finding satisfying truth assignments in case of unsat and sat formulas, respectively. To realize this, we propose a novel architecture that adapts elements from Graph Neural Networks and Pointer Networks to autoregressively select pairs of nodes from a dynamic graph structure, which is essential to the generation of resolution proofs. Our model is trained and evaluated on a dataset of teacher proofs and truth assignments that we compiled with the same random formula distribution used by NeuroSAT. In our experiments, we show that NeuRes solves more test formulas than NeuroSAT by a rather wide margin on different distributions while being much more data-efficient. Furthermore, we show that NeuRes is capable of largely shortening teacher proofs by notable proportions. We use this feature to devise a bootstrapped training procedure that manages to reduce a dataset of proofs generated by an advanced solver by ~23% after training on it with no extra guidance.
Intelligent Diagnosis of Alzheimer's Disease Based on Machine Learning

This study is based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and aims to explore early detection and disease progression in Alzheimer's disease (AD). We employ innovative data preprocessing strategies, including the use of the random forest algorithm to fill missing data and the handling of outliers and invalid data, thereby fully mining and utilizing these limited data resources. Through Spearman correlation coefficient analysis, we identify some features strongly correlated with AD diagnosis. We build and test three machine learning models using these features: random forest, XGBoost, and support vector machine (SVM). Among them, the XGBoost model performs the best in terms of diagnostic performance, achieving an accuracy of 91%. Overall, this study successfully overcomes the challenge of missing data and provides valuable insights into early detection of Alzheimer's disease, demonstrating its unique research value and practical significance.
Rational Design of Efficient Defect-Based Quantum Emitters

Single-photon emitters are an essential component of quantum networks, and defects or impurities in semiconductors are a promising platform to realize such quantum emitters. Here we present a model that encapsulates the essential physics of coupling to phonons, which governs the behavior of real single-photon emitters, and critically evaluate several approximations that are commonly utilized. Emission in the telecom wavelength range is highly desirable, but our model shows that nonradiative processes are greatly enhanced at these low photon energies, leading to a decrease in efficiency. Our results suggest that reducing the phonon frequency is a fruitful avenue to enhance the efficiency.
Polynomial-Time Algorithms for Weaver's Discrepancy Problem in a Dense Regime

Given $v_1,\ldots, v_m\in\mathbb{C}^d$ with $\|v_i\|^2= \alpha$ for all $i\in[m]$ as input and suppose $\sum_{i=1}^m | \langle u, v_i \rangle |^2 = 1$ for every unit vector $u\in\mathbb{C}^d$, Weaver's discrepancy problem asks for a partition $S_1, S_2$ of $[m]$, such that $\sum_{i\in S_{j}} |\langle u, v_i \rangle|^2 \leq 1 -\theta$ for some universal constant $\theta$, every unit vector $u\in\mathbb{C}^d$ and every $j\in\{1,2\}$. We prove that this problem can be solved deterministically in polynomial time when $m\geq 49 d^2$.
Two dimensional graph model for epitaxial crystal growth with adatoms

We consider a model to describe stable configurations in epitaxial growth of crystals in the two dimensional case, and in the regime of linear elasticity. The novelty is that the model also takes into consideration the adatom density on the surface of the film. These are behind the main mechanisms of crystal growth and formation of islands (or quantum dots). The main result of the paper is the integral representation of the relaxed energy.
PeTailor: Improving Large Language Model by Tailored Chunk Scorer in Biomedical Triple Extraction

The automatic extraction of biomedical entities and their interaction from unstructured data remains a challenging task due to the limited availability of expert-labeled standard datasets. In this paper, we introduce P E T AI - LOR , a retrieval-based language framework that is augmented by tailored chunk scorer. Unlike previous retrieval-augmented language models (LM) that retrieve relevant documents by calculating the similarity between the input sentence and the candidate document set, P E T AILOR segments the sentence into chunks and retrieves the relevant chunk from our pre-computed chunk-based relational key-value memory. Moreover, in order to comprehend the specific requirements of the LM, P E T AI - LOR adapt the tailored chunk scorer to the LM. We also introduce GM-CIHT, an expert annotated biomedical triple extraction dataset with more relation types. This dataset is centered on the non-drug treatment and general biomedical domain. Additionally, we investigate the efficacy of triple extraction models trained on general domains when applied to the biomedical domain. Our experiments reveal that P E T AI - LOR achieves state-of-the-art performance on GM-CIHT. 1
Marine phytoplankton and heterotrophic bacteria rapidly adapt to future pCO2 conditions in experimental co-cultures

The CO2 content of Earth’s atmosphere is rapidly increasing due to human consumption of fossil fuels. Models based on short-term culture experiments predict that major changes will occur in marine phytoplankton communities in the future ocean, but these models rarely consider how the evolutionary potential of phytoplankton or interactions within marine microbial communities may influence these changes. Here we experimentally evolved representatives of four phytoplankton functional types (silicifiers, calcifiers, coastal cyanobacteria, and oligotrophic cyanobacteria) in co-culture with a heterotrophic bacterium, Alteromonas, under either present-day or predicted future pCO2 conditions. Growth rates of cyanobacteria generally increased under both conditions, and the growth defects observed in ancestral Prochlorococcus cultures at elevated pCO2 and in axenic culture were diminished after evolution, possibly due to regulatory mutations in antioxidant genes. Except for Prochlorococcus, mutational profiles suggested phytoplankton experienced primarily purifying selection, but most Alteromonas lineages showed evidence of directional selection, especially when co-cultured with eukaryotic phytoplankton, where evolution appeared to favor a broad metabolic switch from growth on small organic acids to catabolism of more complex carbon substrates. Evolved Alteromonas were also poorer “helpers” for Prochlorococcus, supporting the assertion that the interaction between Prochlorococcus and heterotrophic bacteria is not a true mutualism but rather a competitive interaction stabilized by Black Queen processes. This work provides new insights on how phytoplankton will respond to anthropogenic change and on the evolutionary mechanisms governing the structure and function of marine microbial communities.
Antropologicheskie issledovanya Tsentral’noy Azii vzapadnoy naukeAnthropological works on Central Asia in Western Academia

Also available online (with further discussions by a great variety of scholars on the themes of regional studies/Central Asia): http://anthropologie.kunstkamera. ru/05/ During a speech in February 2013, the USA Secretary of State John Kerry applauded American diplomats working in Kyrzakhstan for their efforts in supporting democratic institutions. Two years later, in January 2015, the New York Times referred to Kyrzbekistan in an article about Tom Caldwell, a mountaineer kidnapped by the Islamic Movement of Uzbekistan. Soon afterwards, the New York Times itself clarified that Kyrzbekistan had been 'misidentified' and the paper apologised because of the error. Blog and twitter users soon picked up the New York Times' mistake and claimed Kyrzbekistan's right to exist: a national anthem was created and posted on Youtube, the country was described as an 'authoritarian democracy' and the first travel guide to Kyrzbekistan was published online. Other commentators, including the writer Leonid Berdshisky, did not, however, find much to laugh about in terms of the Kyrbekistan or Kyrzakhstan mistakes. Berdhisky (2015) described this type of error as a 'manifestation of our strange indifference to, or even contempt for, countries that appear remote, small or unimportant', and as a sad stigma unleashed by well-known world politicians and journalist on the so-called Central Asian 'stans' (Kyrgyzstan, Kazakhstan, Tajikistan, Uzbekistan, and Turkmenistan). Both these errors and jokes are signs of how, despite of the changing landscapes posited by increasing although unequal global mobility of people and capital, Central Asia still conveys in much public discourse a sense of being formed by the 'unfathomable' five republics which were once part of the Soviet Union.
HiCrayon reveals distinct layers of multi-state 3D chromatin organization

The co-visualization of chromatin conformation with 1D ‘omics data is key to the multi-omics driven data analysis of 3D genome organization. Chromatin contact maps are often shown as 2D heatmaps and visually compared to 1D genomic data by simple juxtaposition. While common, this strategy is imprecise, placing the onus on the reader to align features with each other. To remedy this, we developed HiCrayon, an interactive tool that facilitates the integration of 3D chromatin organization maps and 1D datasets. This visualization method integrates data from genomic assays directly into the chromatin contact map by coloring interactions according to 1D signal. HiCrayon is implemented using R shiny and python to create a graphical user interface (GUI) application, available in both web or containerized format to promote accessibility. HiCrayon is implemented in R, and includes a graphical user interface (GUI), as well as a slimmed-down web-based version that lets users quickly produce publication-ready images. We demonstrate the utility of HiCrayon in visualizing the effectiveness of compartment calling and the relationship between ChIP-seq and various features of chromatin organization. We also demonstrate the improved visualization of other 3D genomic phenomena, such as differences between loops associated with CTCF/cohesin vs. those associated with H3K27ac. We then demonstrate HiCrayon’s visualization of organizational changes that occur during differentiation and use HiCrayon to detect compartment patterns that cannot be assigned to either A or B compartments, revealing a distinct 3rd chromatin compartment. Overall, we demonstrate the utility of co-visualizing 2D chromatin conformation with 1D genomic signals within the same matrix to reveal fundamental aspects of genome organization. Local version: https://github.com/JRowleyLab/HiCrayon Web version: https://jrowleylab.com/HiCrayon
Automated multimodal imaging of Caenorhabditis elegans behavior in multi-well plates

Large-scale assays of behavior in model organisms play an important role in genetic screens, drug testing, and the elucidation of gene-behavior relationships. We have developed an automated, high-throughput imaging and analysis method for assaying behaviors of the nematode C. elegans. We use high-resolution optical imaging to longitudinally record the behaviors of 96 animals at a time in multi-well plates, and computer vision software to quantify the animals’ locomotor activity, behavioral states, and egg laying events. To demonstrate the capabilities of our system we used it to examine the role of serotonin in C. elegans behavior. We found that egg-laying events are preceded by a period of reduced locomotion, and that this decline in movement requires serotonin signaling. In addition, we identified novel roles of serotonin receptors SER-1 and SER-7 in regulating the effects of serotonin on egg laying across roaming, dwelling, and quiescent locomotor states. Our system will be useful for performing genetic or chemical screens for modulators of behavior.
Evolutionary and biological mechanisms underpinning chitin degradation in aquatic fungi

Fungal biology underpins major processes in ecosystems. The Chytridiomycota (chytrids) is a group of early-diverging fungi, many of which function in ecosystems as saprotrophs processing high molecular weight biopolymers, however the mechanisms underpinning chytrid saprotrophy are poorly understood. Genome sequences from representatives across the group and the use of model chytrids offers the potential to determine new insights into their evolution. In this study, we focused on the biology underpinning chitin saprotrophy, a major ecosystem function of aquatic chytrids. The genomes of chitinophilic chytrids have expanded inventories of glycoside hydrolase genes responsible for chitin processing, complemented with bacteria-like chitin-binding modules that are absent in other chytrids. In the model chitinophilic saprotroph Rhizoclosmatium globosum JEL800, the expanded repertoire of chitinase genes is diverse and almost half were detected as proteins in the secretome when grown with chitin. Predicted models of the secreted chitinases indicate a range of active site sizes, particularly exochitinases that cleave terminal ends of chitin polymers. We propose that high diversity of secreted chitinases is an adaptive strategy that facilitates chitin degradation in the complex heterologous organic matrix of the arthropod exoskeleton. Free swimming R. globosum JEL800 zoospores are chemotactic to the chitin monomer N-acetylglucosamine and accelerate development when grown with chitin. Our study sheds light on the underpinning biology and evolutionary mechanisms that have supported the saprotrophic niche expansion of some chytrids to utilise lucrative chitin-rich particles in aquatic ecosystems and is a demonstration of the adaptive capability of this successful fungal group. Significance statement The Chytridiomycota (chytrids) diverged early in fungal evolutionary history and maintain biological features of their last common ancestor with other fungi and animals. Unlike more familiar fungi, chytrid spores are often motile and can swim to find suitable substrates to grow on. Many chytrids in aquatic ecosystems grow attached to chitin-rich particles including insect remains. Our study has shown the biological mechanisms that underpin the evolution of chitin-based lifestyle in chytrids. The expansion of existing chitin degradation genes, probably adapted from cell wall remodelling functions, alongside novel gene transfer of complementary enzyme chitin-binding capability facilitated chitin utilisation evolution. Chytrid spores can also swim towards chitin along associated chemical gradients and accelerate their development in response to chitin.
Determination of Absolute Intramolecular Distances in Proteins by Anomalous X-ray Scattering Interferometry

Biomolecular structures are typically determined using frozen or crystalline samples. Measurement of intramolecular distances in solution can provide additional insights into conformational heterogeneity and dynamics of biological macromolecules and their complexes. The established molecular ruler techniques used for this (NMR, FRET, and EPR) are, however, limited in their dynamic range and require model assumptions to determine absolute distance (distributions). Here, we introduce anomalous X-ray scattering interferometry (AXSI) for intramolecular distance measurements in proteins, which are labeled at two sites with small gold nanoparticles of 0.7 nm radius. We apply AXSI to two different cysteine-variants of maltose binding protein in the presence and absence of its ligand maltose and find distances in quantitative agreement with single-molecule FRET experiments. Our study shows that AXSI enables determination of absolute intramolecular distance distributions under virtually arbitrary solution conditions and we anticipate its broad use to characterize protein conformational ensembles and dynamics.