Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Algorithms for Evolution of QWOP Gaits

QWOP is a browser-based, 2-dimensional flash game in which the player controls an Olympic sprinter competing in a simulated 100-meter race. The goal of the game is to advance the runner to the end of the 100-meter race as quickly as possible using the Q, W, O, and P keys, which control the muscles in the sprinters legs. Despite the game simple controls and straightforward goal, it is renowned for its difficulty and unintuitive gameplay. In this paper, we attempt to automatically discover effective QWOP gaits. We describe a programmatic interface developed to play the game, and we introduce several variants of a genetic algorithm tailored to solve this problem. We present experimental results on the effectiveness of various representations, initialization strategies, evolution paradigms, and parameter control mechanisms.
NLTS Hamiltonians and Strongly-Explicit SoS Lower Bounds from Low-Rate Quantum LDPC Codes

Recent constructions of the first asymptotically good quantum LDPC (qLDPC) codes led to two breakthroughs in complexity theory: the NLTS (No Low-Energy Trivial States) theorem (Anshu, Breuckmann, and Nirkhe, STOC'23), and explicit lower bounds against a linear number of levels of the Sum-of-Squares (SoS) hierarchy (Hopkins and Lin, FOCS'22). In this work, we obtain improvements to both of these results using qLDPC codes of low rate: - Whereas Anshu et al. only obtained NLTS Hamiltonians from qLDPC codes of linear dimension, we show the stronger result that qLDPC codes of arbitrarily small positive dimension yield NLTS Hamiltonians. - The SoS lower bounds of Hopkins and Lin are only weakly explicit because they require running Gaussian elimination to find a nontrivial codeword, which takes polynomial time. We resolve this shortcoming by introducing a new method of planting a strongly explicit nontrivial codeword in linear-distance qLDPC codes, which in turn yields strongly explicit SoS lower bounds. Our"planted"qLDPC codes may be of independent interest, as they provide a new way of ensuring a qLDPC code has positive dimension without resorting to parity check counting, and therefore provide more flexibility in the code construction.
Synthesis estimators for positivity violations with a continuous covariate

Studies intended to estimate the effect of a treatment, like randomized trials, often consist of a biased sample of the desired target population. To correct for this bias, estimates can be transported to the desired target population. Methods for transporting between populations are often premised on a positivity assumption, such that all relevant covariate patterns in one population are also present in the other. However, eligibility criteria, particularly in the case of trials, can result in violations of positivity. To address nonpositivity, a synthesis of statistical and mathematical models can be considered. This approach integrates multiple data sources (e.g. trials, observational, pharmacokinetic studies) to estimate treatment effects, leveraging mathematical models to handle positivity violations. This approach was previously demonstrated for positivity violations by a single binary covariate. Here, we extend the synthesis approach for positivity violations with a continuous covariate. For estimation, two novel augmented inverse probability weighting estimators are proposed. Both estimators are contrasted with other common approaches for addressing nonpositivity. Empirical performance is compared via Monte Carlo simulation. Finally, the competing approaches are illustrated with an example in the context of two-drug versus one-drug antiretroviral therapy on CD4 T cell counts among women with HIV.
The impact of effective participation in stopping misinformation: an approach based on branching processes

The emergence of research focused to understand the spreading and impact of disinformation is increasing year over year. Most times, the purpose of those who start the spreading of information intentionally false and designed to cause harm is in catalyzing its fast transformation into misinformation, which is the false content shared by people who do not realize it is false or misleading. Our interest is in discussing the role of people who decide to adopt an active role in stopping the propagation of an information when they realize that it is false. For this, we formulate two simple probabilistic models to compare misinformation spreading in the possible scenarios for which there is a passive or an active environment of aware individuals. With aware individuals we mean those individuals who realize that a given information is false or misleading. In the passive environment we assume that if one of an aware individual is exposed to the misinformation then he/she will not spread it. In the active environment we assume that if one of an aware individual is exposed to the misinformation then he/she will not spread it but also he/she will stop the propagation to other individuals from the individual who contacted him/her. We appeal to the theory of branching processes to analyse propagation in both scenarios and we discuss the role and the impact of effective participation in stopping misinformation. We show that the propagation reduces drastically provided we assume an active environment, and we obtain theoretical and computational results to measure such a reduction, which in turns depends on the proportion of aware individuals and the number of potential contacts of each individual which is assumed to be random.
Analyzing Deviations of Dyadic Lines in Fast Hough Transform

Fast Hough transform is a widely used algorithm in pattern recognition. The algorithm relies on approximating lines using a specific discrete line model called dyadic lines. The worst-case deviation of a dyadic line from the ideal line it used to construct grows as $O(log(n))$, where $n$ is the linear size of the image. But few lines actually reach the worst-case bound. The present paper addresses a statistical analysis of the deviation of a dyadic line from its ideal counterpart. Specifically, our findings show that the mean deviation is zero, and the variance grows as $O(log(n))$. As $n$ increases, the distribution of these (suitably normalized) deviations converges towards a normal distribution with zero mean and a small variance. This limiting result makes an essential use of ergodic theory.
The subdivision of hypergraphs

Hypergraphs, as a generalization of simplicial complexes, have long been a subject of interest in their geometric interpretation. The subdivision of simplicial complexes can, to some extent, provide insights into the geometry of simplicial complexes. In this paper, we introduce the concept of the subdivision of hypergraphs. Notably, the subdivision of hypergraphs can be reduced to the subdivision of simplicial complexes. Moreover, we prove that the subdivision of hypergraphs has the topological invariance with respect to the embedded homology.
LOKE: Linked Open Knowledge Extraction for Automated Knowledge Graph Construction

While the potential of Open Information Extraction (Open IE) for Knowledge Graph Construction (KGC) may seem promising, we find that the alignment of Open IE extraction results with existing knowledge graphs to be inadequate. The advent of Large Language Models (LLMs), especially the commercially available OpenAI models, have reset expectations for what is possible with deep learning models and have created a new field called prompt engineering. We investigate the use of GPT models and prompt engineering for knowledge graph construction with the Wikidata knowledge graph to address a similar problem to Open IE, which we call Open Knowledge Extraction (OKE) using an approach we call the Linked Open Knowledge Extractor (LOKE, pronounced like"Loki"). We consider the entity linking task essential to construction of real world knowledge graphs. We merge the CaRB benchmark scoring approach with data from the TekGen dataset for the LOKE task. We then show that a well engineered prompt, paired with a naive entity linking approach (which we call LOKE-GPT), outperforms AllenAI's OpenIE 4 implementation on the OKE task, although it over-generates triples compared to the reference set due to overall triple scarcity in the TekGen set. Through an analysis of entity linkability in the CaRB dataset, as well as outputs from OpenIE 4 and LOKE-GPT, we see that LOKE-GPT and the"silver"TekGen triples show that the task is significantly different in content from OIE, if not structure. Through this analysis and a qualitative analysis of sentence extractions via all methods, we found that LOKE-GPT extractions are of high utility for the KGC task and suitable for use in semi-automated extraction settings.
A characterization of efficiently compilable constraint languages

A central task in knowledge compilation is to compile a CNF-SAT instance into a succinct representation format that allows efficient operations such as testing satisfiability, counting, or enumerating all solutions. Useful representation formats studied in this area range from ordered binary decision diagrams (OBDDs) to circuits in decomposable negation normal form (DNNFs). While it is known that there exist CNF formulas that require exponential size representations, the situation is less well studied for other types of constraints than Boolean disjunctive clauses. The constraint satisfaction problem (CSP) is a powerful framework that generalizes CNF-SAT by allowing arbitrary sets of constraints over any finite domain. The main goal of our work is to understand for which type of constraints (also called the constraint language) it is possible to efficiently compute representations of polynomial size. We answer this question completely and prove two tight characterizations of efficiently compilable constraint languages, depending on whether target format is structured. We first identify the combinatorial property of ``strong blockwise decomposability'' and show that if a constraint language has this property, we can compute DNNF representations of linear size. For all other constraint languages we construct families of CSP-instances that provably require DNNFs of exponential size. For a subclass of ``strong uniformly blockwise decomposable'' constraint languages we obtain a similar dichotomy for structured DNNFs. In fact, strong (uniform) blockwise decomposability even allows efficient compilation into multi-valued analogs of OBDDs and FBDDs, respectively. Thus, we get complete characterizations for all knowledge compilation classes between O(B)DDs and DNNFs.
Which Modality should I use - Text, Motif, or Image? : Understanding Graphs with Large Language Models

Our research integrates graph data with Large Language Models (LLMs), which, despite their advancements in various fields using large text corpora, face limitations in encoding entire graphs due to context size constraints. This paper introduces a new approach to encoding a graph with diverse modalities, such as text, image, and motif, coupled with prompts to approximate a graph's global connectivity, thereby enhancing LLMs' efficiency in processing complex graph structures. The study also presents GraphTMI, a novel benchmark for evaluating LLMs in graph structure analysis, focusing on homophily, motif presence, and graph difficulty. Key findings indicate that the image modality, especially with vision-language models like GPT-4V, is superior to text in balancing token limits and preserving essential information and outperforms prior graph neural net (GNN) encoders. Furthermore, the research assesses how various factors affect the performance of each encoding modality and outlines the existing challenges and potential future developments for LLMs in graph understanding and reasoning tasks. All data will be publicly available upon acceptance.
Dual Non-local Cnot gate

Distant quantum control via quantum gates represents an essential step toward realizing distributed quantum networks. An efficient theoretical protocol for the dual non-local implementation of controlled-not (CNOT) gates between two separated partners is presented in this regard. The suggested protocol requires 1~ebit with local operations and classical communication channels. The efficiency of the teleportation scheme is quantified through an infidelity measure. The numerical results show that the infidelity of performing the CNOT gate between legitimate partners depends on the initial qubit settings. It is also shown that the protocol is performed efficiently if the CNOT control qubit and the auxiliary qubit are prepared in the same direction. Furthermore, we provide a noise analysis for the suggested scheme. We find that by maintaining the noise strengths under the threshold $\frac{1}{4}$, one can achieve the dual non-local CNOT gate optimally.
Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces

Despite the prevalence of tabular datasets, few-shot learning remains under-explored within this domain. Existing few-shot methods are not directly applicable to tabular datasets due to varying column relationships, meanings, and permutational invariance. To address these challenges, we propose FLAT-a novel approach to tabular few-shot learning, encompassing knowledge sharing between datasets with heterogeneous feature spaces. Utilizing an encoder inspired by Dataset2Vec, FLAT learns low-dimensional embeddings of datasets and their individual columns, which facilitate knowledge transfer and generalization to previously unseen datasets. A decoder network parametrizes the predictive target network, implemented as a Graph Attention Network, to accommodate the heterogeneous nature of tabular datasets. Experiments on a diverse collection of 118 UCI datasets demonstrate FLAT's successful generalization to new tabular datasets and a considerable improvement over the baselines.
Evolving Domain Adaptation of Pretrained Language Models for Text Classification

Adapting pre-trained language models (PLMs) for time-series text classification amidst evolving domain shifts (EDS) is critical for maintaining accuracy in applications like stance detection. This study benchmarks the effectiveness of evolving domain adaptation (EDA) strategies, notably self-training, domain-adversarial training, and domain-adaptive pretraining, with a focus on an incremental self-training method. Our analysis across various datasets reveals that this incremental method excels at adapting PLMs to EDS, outperforming traditional domain adaptation techniques. These findings highlight the importance of continually updating PLMs to ensure their effectiveness in real-world applications, paving the way for future research into PLM robustness against the natural temporal evolution of language.
The Dirac-Higgs complex and branes on the stack of Higgs bundles

Kapustin and Witten introduced (BBB)-branes on the moduli space of Higgs bundles by considering hyperholomorphic bundles supported on hyperk\"ahler subvarieties. Following a suggestion of Gaiotto for the categorification of these objects, we propose a definition of (BBB)-branes on the corresponding moduli stack in terms of a pair given by a perfect complex over the Dolbeault moduli stack, and a second complex over the Deligne moduli stack, whose restriction is the analytification of the first. After addressing the construction of the Deligne moduli stack, we construct examples of such (BBB)-branes using a family of integral functors with the universal bundles as kernels. This construction includes the Dirac-Higgs complex as a particular case. Finally, we discuss some properties of these integral functors, in particular their relation with the Wilson operators appearing in the Geometric Langlands Correspondence.
Emergent symmetry in a low-dimensional superconductor on the edge of Mottness

Upon cooling, condensed-matter systems typically transition into states of lower symmetry. The converse—i.e., the emergence of higher symmetry at lower temperatures—is extremely rare. In this work, we show how an unusually isotropic magnetoresistance in the highly anisotropic, one-dimensional conductor Li0.9Mo6O17 and its temperature dependence can be interpreted as a renormalization group (RG) flow toward a so-called separatrix. This approach is equivalent to an emergent symmetry in the system. The existence of two distinct ground states, Mott insulator and superconductor, can then be traced back to two opposing RG trajectories. By establishing a direct link between quantum field theory and an experimentally measurable quantity, we uncover a path through which emergent symmetry might be identified in other candidate materials. Description Editor’s summary The concept of emergent symmetry applies to systems in which a more symmetric state emerges at low temperatures from an initial high-temperature state. Demonstrating this emergence in an experiment is very challenging. In search of experimental signatures, Chudzinski et al. studied the one-dimensional material, Li0.9Mo6O17, which lies close to the Mott transition but can also host superconductivity. The researchers measured a surprisingly isotropic magnetoresistance, indicating that rather than being a Mott insulator or a superconductor, the system has fallen into a state with higher, emergent symmetry. —Jelena Stajic Magnetoresistance measurements in Li0.9Mo6O17 reveal the emergence of a state of higher symmetry at low temperatures.
Efficient genome editing using modified Cas9 proteins in zebrafish

The zebrafish (Danio rerio) is an important model organism for basic as well as applied bio-medical research. One main advantage is its genetic tractability, which was greatly enhanced by the introduction of the CRISPR/Cas method a decade ago. The generation of loss-of-function alleles via the production of small insertions or deletions in the coding sequences of genes with CRISPR/Cas systems is now routinely achieved with high efficiency. The method is based on the error prone repair of precisely targeted DNA double strand breaks by non-homologous end joining (NHEJ) in the cell nucleus. However, editing the genome with base pair precision, by homology-directed repair (HDR), is by far less efficient and therefore often requires large-scale screening of potential carriers by labour intensive genotyping. Here we show that the Cas9 protein variant SpRY with relaxed PAM requirement can be used for gene targeting in zebrafish, thus expanding the versatility of the method. In addition, we demonstrate that the incorporation of an artificial nuclear localisation signal (aNLS) into the Cas9 protein variants not only enhances the efficiency of gene knock-out but also the frequency of HDR thereby facilitating the efficient modification of single base pairs in the genome. Our protocols provide a guide for a cost-effective generation of versatile and potent Cas9 protein variants and efficient gene editing in zebrafish.
Multi-timescale reinforcement learning in the brain

To thrive in complex environments, animals and artificial agents must learn to act adaptively to maximize fitness and rewards. Such adaptive behavior can be learned through reinforcement learning1, a class of algorithms that has been successful at training artificial agents2–6 and at characterizing the firing of dopamine neurons in the midbrain7–9. In classical reinforcement learning, agents discount future rewards exponentially according to a single time scale, controlled by the discount factor. Here, we explore the presence of multiple timescales in biological reinforcement learning. We first show that reinforcement agents learning at a multitude of timescales possess distinct computational benefits. Next, we report that dopamine neurons in mice performing two behavioral tasks encode reward prediction error with a diversity of discount time constants. Our model explains the heterogeneity of temporal discounting in both cue-evoked transient responses and slower timescale fluctuations known as dopamine ramps. Crucially, the measured discount factor of individual neurons is correlated across the two tasks suggesting that it is a cell-specific property. Together, our results provide a new paradigm to understand functional heterogeneity in dopamine neurons, a mechanistic basis for the empirical observation that humans and animals use non-exponential discounts in many situations 10–14, and open new avenues for the design of more efficient reinforcement learning algorithms.
Nutritional value of extracted soybean, full fat soya and maize gluten meal proteins in comparison to a fish meal based reference diet for fingerling tilapia (Oreochromis mossambicus)

A two-month investigation evaluated potential selected plant protein sources compared to fish meal protein in scientifically formulated diets for fingerling tilapia (Oreochromis mossambicus). Six nutritionally balanced diets: one reference (control) diet (fish-meal-based diet), three selected plant proteins (solvent extracted soybean, full fat soybean, and maize gluten) were substituted in the LT (Low Temperature dried) fish-meal-based control diet protein at 50% respectively, and the remaining diets were replaced with 75% inclusion of solvent extracted soybean and maize gluten meals. Results showed that SESB50, FFSB50 and MG50 fed tilapia did not appreciably differ in their growthperformance (final mean weights of 12.07, 10.75 and 9.18% respectively) with a final mean weight of 14.95g being significantly superior for the fish meal control (CO) group. Substitution of fish meal diet with solvent-extracted soybean and maize gluten at levels of 75% gave the poorest growth performance and Feed Efficiency (FE) (7.01g final weight, 51% FE). However, the MG50 diet also showed some inferior feed utilisation performance. However, tilapia fed on a full fat soybean diet (FFSB50) did not vary significantly from the control (CON) diet in terms of Apparent Net Protein Utilisation (ANPU) at 34.20 and 37.53% respectively. Whilst this preliminary study concluded that limitations on the use of the selected plant proteins in diets for tilapia are apparent, several approaches that could result in the future improvement of the nutritional value of soybean and maize gluten products for use in fish diets are also stated. Highlights A scientifically formulated reference diet using a Low Temperature dried (LT) fish meal produced superior growth performance for fingerling tilapia compared to selected oilseed (soybean) and grain (maize gluten) derived protein concentrates. Soybean and full fat soybean meals were effective in contributing to 50% inclusion in experimental diets, but 75% of soybean and maize gluten meal was not able to support similar growth of tilapia over eight weeks. Feed utilisation parameters such as Protein Efficiency Ratio (PER), Apparent Net Protein Utilisation (ANPU), Net Energy Utilisation (NEU) and Feed Efficiency (FE) gave supporting evidence and conclusions for optimal threshold inclusions of the test plant ingredients for tilapia.