Miracle-dz/UniFAR-FTUs huggingface dataset:
EXK-SC: A Semantic Communication Model Based on Information Framework Expansion and Knowledge Collision

Semantic communication is not focused on improving the accuracy of transmitted symbols, but is concerned with expressing the expected meaning that the symbol sequence exactly carries. However, the measurement of semantic messages and their corresponding codebook generation are still open issues. Expansion, which integrates simple things into a complex system and even generates intelligence, is truly consistent with the evolution of the human language system. We apply this idea to the semantic communication system, quantifying semantic transmission by symbol sequences and investigating the semantic information system in a similar way as Shannon’s method for digital communication systems. This work is the first to discuss semantic expansion and knowledge collision in the semantic information framework. Some important theoretical results are presented, including the relationship between semantic expansion and the transmission information rate. We believe such a semantic information framework may provide a new paradigm for semantic communications, and semantic expansion and knowledge collision will be the cornerstone of semantic information theory.
The symplectic leaves for the elliptic Poisson bracket on projective space defined by Feigin-Odesskii and Polishchuk

This paper determines the symplectic leaves for a remarkable Poisson structure on $\mathbb{C}\mathbb{P}^{n-1}$ discovered by Feigin and Odesskii, and, independently, by Polishchuk. The Poisson bracket is determined by a holomorphic line bundle of degree $n \ge 3$ on a compact Riemann surface of genus one or, equivalently, by an elliptic normal curve $E\subseteq\mathbb{C}\mathbb{P}^{n-1}$. The symplectic leaves are described in terms of higher secant varieties to $E$.
Compressing multidimensional weather and climate data into neural networks

Weather and climate simulations produce petabytes of high-resolution data that are later analyzed by researchers in order to understand climate change or severe weather. We propose a new method of compressing this multidimensional weather and climate data: a coordinate-based neural network is trained to overfit the data, and the resulting parameters are taken as a compact representation of the original grid-based data. While compression ratios range from 300x to more than 3,000x, our method outperforms the state-of-the-art compressor SZ3 in terms of weighted RMSE, MAE. It can faithfully preserve important large scale atmosphere structures and does not introduce artifacts. When using the resulting neural network as a 790x compressed dataloader to train the WeatherBench forecasting model, its RMSE increases by less than 2%. The three orders of magnitude compression democratizes access to high-resolution climate data and enables numerous new research directions.
Task-Driven In-Hand Manipulation of Unknown Objects with Tactile Sensing

—Manipulation of objects in-hand without an object model is a foundational skill for many tasks in unstructured environments. In many cases, vision-only approaches may not be feasible; for example, due to occlusion in cluttered spaces. In this paper, we introduce a method to reorient unknown objects by incrementally building a probabilistic estimate of the object shape and pose during task-driven manipulation. Our method leverages Bayesian optimization to strategically trade-off exploration of the global object shape with efﬁcient task completion. We demonstrate our approach on a Tactile-Enabled Roller Grasper, a gripper that rolls objects in hand while continuously collecting tactile data. We evaluate our method in simulation on a set of randomly generated objects and ﬁnd that our method reliably reorients objects while signiﬁcantly reducing the exploration time needed to do so. On the Roller Grasper hardware, we show successful qualitative reconstruction of the object model. In summary, this work (1) presents a system capable of simultaneously learning unknown 3D object shape and pose using tactile sensing; and (2) demonstrates that task-driven exploration results in more efﬁcient object manipulation than the common paradigm of complete object exploration before task-completion.
Probing Transfer in Deep Reinforcement Learning without Task Engineering

We evaluate the use of original game curricula supported by the Atari 2600 console as a heterogeneous transfer benchmark for deep reinforcement learning agents. Game designers created curricula using combinations of several discrete modifications to the basic versions of games such as Space Invaders, Breakout and Freeway, making them progressively more challenging for human players. By formally organising these modifications into several factors of variation, we are able to show that Analyses of Variance (ANOVA) are a potent tool for studying the effects of human-relevant domain changes on the learning and transfer performance of a deep reinforcement learning agent. Since no manual task engineering is needed on our part, leveraging the original multi-factorial design avoids the pitfalls of unintentionally biasing the experimental setup. We find that game design factors have a large and statistically significant impact on an agent's ability to learn, and so do their combinatorial interactions. Furthermore, we show that zero-shot transfer from the basic games to their respective variations is possible, but the variance in performance is also largely explained by interactions between factors. As such, we argue that Atari game curricula offer a challenging benchmark for transfer learning in RL, that can help the community better understand the generalisation capabilities of RL agents along dimensions which meaningfully impact human generalisation performance. As a start, we report that value-function finetuning of regularly trained agents achieves positive transfer in a majority of cases, but significant headroom for algorithmic innovation remains. We conclude with the observation that selective transfer from multiple variants could further improve performance.
Symmetry breaking and weighted Euclidean logarithmic Sobolev inequalities

On the Euclidean space, we establish some Weighted Logarithmic Sobolev (WLS) inequalities. We characterize a symmetry range in which optimal functions are radially symmetric, and a symmetry breaking range. (WLS) inequalities are a limit case for a family of subcritical Caffarelli-Kohn-Nirenberg (CKN) inequalities with similar symmetry properties. A generalized carr\'e du champ method applies not only to the optimal solution of the nonlinear elliptic Euler-Lagrange equation and proves a rigidity result as for (CKN) inequalities, but also to entropy type estimates, with the full strength of the carr\'e du champ method in a parabolic setting. This is a significant improvement on known results for (CKN). Finally, we briefly sketch some consequences of our results for the weighted diffusion flow.
Numerical simulation of microscale acoustic streaming flow

The efficient mixing of fluid samples in miniaturized total analysis systems is essential for numerous applications including biological screening assays, chemical extraction, polymerization, cell analysis and protein folding. Miniaturized microfluidic platforms have recently emerged for microscale fluid mixing with their capabilities of highthroughput sample processing and reduced sample depletion. However, microscale fluid mixing is inherently hampered due to the characteristics of low Reynolds number flows in diminutive channels. Microscale fluid mixing mainly depends on molecular diffusion and thus requires long processing time. In order to address the conundrum, a variety of active microfluidic approaches for swift and efficient mixing have been developed using electro-kinetic flow, laser-induced flow, magnetic stirring, and acoustic streaming flow (ASF). Among these techniques with external force fields, acoustofluidic fluid mixing has been acclaimed due to its on-demand, controllable, non-invasive and biocompatible nature. In this study, we performed numerical simulations of acoustic streaming flows, induced by with a highly localized surface acoustic waves (SAWs), in a microscale fluidic channel based on an acoustic wave attenuation model.
Unconditional Proofs-of-Work and Other Possibilities of Thermodynamic Cryptography

In line with advances in recent years about realizing cryptographic functionalities in an information-theoretically secure way from physical phenomena and laws, we propose here to obtain useful tasks from the sole assumption of limited free energy. Specifically, based on that assumption — resulting in a setting loosely related to Maurer’s bounded-storage model — we derive protocols for unconditional proofs-of-thermodynamical-work, secret sharing of free energy, unforgeable money, and proofs-of-position. While our schemes can be considered classical and not quantum per se, they are resistant against both classes of adversaries.
Quasiclassical frustration

We study the dissipative properties of a harmonic oscillator subject to two independent heat baths, one of which couples to its position and the other one to its momentum. This model describes a large spin impurity in a ferromagnet. We ﬁnd that some eﬀects of the two heat baths partially cancel each other. Most notably, oscillations may remain underdamped for arbitrarily strong coupling. This eﬀect is a direct consequence of the mutually conjugate character of position and momentum. For a single dissipative bath coupled linearly to both position and momentum, no underdamped regime is possible for strong coupling. The dynamics of purity loss for one and two wave packets is also investigated.
Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts

Crises such as the COVID-19 pandemic continuously threaten our world and emotionally affect billions of people worldwide in distinct ways. Understanding the triggers leading to people’s emotions is of crucial importance. Social media posts can be a good source of such analysis, yet these texts tend to be charged with multiple emotions, with triggers scattering across multiple sentences. This paper takes a novel angle, namely, emotion detection and trigger summarization, aiming to both detect perceived emotions in text, and summarize events and their appraisals that trigger each emotion. To support this goal, we introduce CovidET (Emotions and their Triggers during Covid-19), a dataset of ~1,900 English Reddit posts related to COVID-19, which contains manual annotations of perceived emotions and abstractive summaries of their triggers described in the post. We develop strong baselines to jointly detect emotions and summarize emotion triggers. Our analyses show that CovidET presents new challenges in emotion-specific summarization, as well as multi-emotion detection in long social media posts.
On the Ilmonen-Haukkanen-Merikoski Conjecture

Let $K_n$ be the set of all $n\times n$ lower triangular (0,1)-matrices with each diagonal element equal to $1$, $L_n = \{ YY^T: Y\in K_n\}$ and let \begin{equation*} c_n = \min_{Z\in L_n} \left\lbrace \mu_n^{(1)}(Z):\mu_n^{(1)} (Z) \text{ is the smallest eigenvalue of } Z \right\rbrace . \end{equation*} The Ilmonen-Haukkanen-Merikoski conjecture (the IHM conjecture) states that $c_n$ is equal to the smallest eigenvalue of $Y_0Y_0^T$, where \begin{equation*} (Y_0)_{ij}=\left\lbrace \ \begin{array}{cl} 0 & \text{if } \ ij. \end{array} \right. \end{equation*} In this paper we present a proof of this conjecture. In our proof we use an inequality for spectral radii of nonnegative matrices.
Generating Information-Seeking Conversations from Unlabeled Documents

Synthesizing datasets for conversational question answering (CQA) from unlabeled documents remains challenging due to its interactive nature.Moreover, while modeling information needs is an essential key, only few studies have discussed it.In this paper, we introduce a novel framework, **SimSeek**, (**Sim**ulating information-**Seek**ing conversation from unlabeled documents), and compare its two variants.In our baseline, **SimSeek-sym**, a questioner generates follow-up questions upon the predetermined answer by an answerer.On the contrary, **SimSeek-asym** first generates the question and then finds its corresponding answer under the conversational context.Our experiments show that they can synthesize effective training resources for CQA and conversational search tasks.As a result, conversations from **SimSeek-asym** not only make more improvements in our experiments but also are favorably reviewed in a human evaluation.We finally release a large-scale resource of synthetic conversations, **Wiki-SimSeek**, containing 2 million CQA pairs built upon Wikipedia documents.With the dataset, our CQA model achieves the state-of-the-art performance on a recent CQA benchmark, QuAC.The code and dataset are available at https://github.com/naver-ai/simseek
Castelnuovo bound and higher genus Gromov-Witten invariants of quintic 3-folds

We prove a conjectural vanishing result for Gopakumar–Vafa invariants of quintic 3-folds, referred to as Castelnuovo bound in the literature. Furthermore, we calculate Gopakumar–Vafa invariants at Castelnuovo bound g = d 2 +5 d +10 10 . As physicists showed, these two properties allow us to compute all Gromov–Witten invariants of quintic 3-folds up to genus 53 [20], provided that the conifold gap condition holds. We also give a bound for the genus of any one-dimensional closed subscheme in a smooth hypersurface of degree ≤ 5, which may be of independent interest.
Error-Covariance Analysis of Monocular Pose Estimation Using Total Least Squares

This study presents a theoretical structure for the monocular pose estimation problem using the total least squares. The unit-vector line-of-sight observations of the features are extracted from the monocular camera images. First, the optimization framework is formulated for the pose estimation problem with observation vectors extracted from unit vectors from the camera center-of-projection, pointing towards the image features. The attitude and position solutions obtained via the derived optimization framework are proven to reach the Cram\'er-Rao lower bound under the small angle approximation of the attitude errors. Specifically, The Fisher Information Matrix and the Cram\'er-Rao bounds are evaluated and compared to the analytical derivations of the error-covariance expressions to rigorously prove the optimality of the estimates. The sensor data for the measurement model is provided through a series of vector observations, and two fully populated noise-covariance matrices are assumed for the body and reference observation data. The inverse of the former matrices appear in terms of a series of weight matrices in the cost function. The proposed solution is simulated in a Monte-Carlo framework with 10,000 samples to validate the error-covariance analysis.
Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings

Zero-resource cross-lingual transfer approaches aim to apply supervised models from a source language to unlabelled target languages. In this paper we perform an in-depth study of the two main techniques employed so far for cross-lingual zero-resource sequence labelling, based either on data or model transfer. Although previous research has proposed translation and annotation projection (data-based cross-lingual transfer) as an effective technique for cross-lingual sequence labelling, in this paper we experimentally demonstrate that high capacity multilingual language models applied in a zero-shot (model-based cross-lingual transfer) setting consistently outperform data-based cross-lingual transfer approaches. A detailed analysis of our results suggests that this might be due to important differences in language use. More specifically, machine translation often generates a textual signal which is different to what the models are exposed to when using gold standard data, which affects both the fine-tuning and evaluation processes. Our results also indicate that data-based cross-lingual transfer approaches remain a competitive option when high-capacity multilingual language models are not available.
Scalable Program Clone Search through Spectral Analysis

We consider the problem of program clone search, i.e. given a target program and a repository of known programs (all in executable format), the goal is to find the program in the repository most similar to the target program -- with potential applications in terms of reverse engineering, program clustering, malware lineage and software theft detection. Recent years have witnessed a blooming in code similarity techniques, yet most of them focus on function-level similarity and function clone search, while we are interested in program-level similarity and program clone search. Actually, our study shows that prior similarity approaches are either too slow to handle large program repositories, or not precise enough, or yet not robust against slight variations introduced by compilers, source code versions or light obfuscations. We propose a novel spectral analysis method for program-level similarity and program clone search called Programs Spectral Similarity (PSS). In a nutshell, PSS one-time spectral feature extraction is tailored for large repositories, making it a perfect fit for program clone search. We have compared the different approaches with extensive benchmarks, showing that PSS reaches a sweet spot in terms of precision, speed and robustness.
B3RTDP: A Belief Branch and Bound Real-Time Dynamic Programming Approach to Solving POMDPs

Partially Observable Markov Decision Processes (POMDPs) offer a promising world representation for autonomous agents, as they can model both transitional and perceptual uncertainties. Calculating the optimal solution to POMDP problems can be computationally expensive as they require reasoning over the (possibly inﬁnite) space of beliefs. Several approaches have been proposed to overcome this difﬁculty, such as discretizing the belief space, point-based belief sam- pling, and Monte Carlo tree search. The Real-Time Dynamic Programming approach of the RTDP-Bel algorithm approximates the value function by storing it in a hashtable with discretized belief keys. We propose an extension to the RTDP-Bel algorithm which we call Belief Branch and Bound RTDP (B 3 RTDP). Our algorithm uses a bounded value function representation and takes advantage of this in two novel ways: a search-bounding technique based on action selection convergence probabilities, and a method for leveraging early action convergence called the Convergence Frontier . Lastly, we empirically demonstrate that B 3 RTDP can achieve greater returns in less time than the state-of-the-art SARSOP solver on known POMDP problems.
On the number of genealogical ancestors tracing to the source groups of an admixed population

Abstract Members of genetically admixed populations possess ancestry from multiple source groups, and studies of human genetic admixture frequently estimate ancestry components corresponding to fractions of individual genomes that trace to specific ancestral populations. However, the same numerical ancestry fraction can represent a wide array of admixture scenarios within an individual’s genealogy. Using a mechanistic model of admixture, we consider admixture genealogically: how many ancestors from the source populations does the admixture represent? We consider African-Americans, for whom continent-level estimates produce a 75–85% value for African ancestry on average and 15–25% for European ancestry. Genetic studies together with key features of African-American demographic history suggest ranges for parameters of a simple three-epoch model. Considering parameter sets compatible with estimates of current ancestry levels, we infer that if all genealogical lines of a random African-American born during 1960–1965 are traced back until they reach members of source populations, the mean over parameter sets of the expected number of genealogical lines terminating with African individuals is 314 (interquartile range 240–376), and the mean of the expected number terminating in Europeans is 51 (interquartile range 32–69). Across discrete generations, the peak number of African genealogical ancestors occurs in birth cohorts from the early 1700s, and the probability exceeds 50% that at least one European ancestor was born more recently than 1835. Our genealogical perspective can contribute to further understanding the admixture processes that underlie admixed populations. For African-Americans, the results provide insight both on how many of the ancestors of a typical African-American might have been forcibly displaced in the Transatlantic Slave Trade and on how many separate European admixture events might exist in a typical African-American genealogy.
Towards a Higher-Order Mathematical Operational Semantics

Compositionality proofs in higher-order languages are notoriously involved, and general semantic frameworks guaranteeing compositionality are hard to come by. In particular, Turi and Plotkin’s bialgebraic abstract GSOS framework, which has been successfully applied to obtain off-the-shelf compositionality results for first-order languages, so far does not apply to higher-order languages. In the present work, we develop a theory of abstract GSOS specifications for higher-order languages, in effect transferring the core principles of Turi and Plotkin’s framework to a higher-order setting. In our theory, the operational semantics of higher-order languages is represented by certain dinatural transformations that we term pointed higher-order GSOS laws. We give a general compositionality result that applies to all systems specified in this way and discuss how compositionality of the SKI calculus and the λ-calculus w.r. t. a strong variant of Abramsky’s applicative bisimilarity are obtained as instances.
On multiplicative Zagreb indices of graphs

Todeschini et al. have recently suggested to consider multiplicative variants of additive graph invariants, which applied to the Zagreb indices would lead to the multiplicative Zagreb indices of a graph G, denoted by ( ) 1  G and ( ) 2  G , under the name first and second multiplicative Zagreb index, respectively. These are define as     ( ) 2 1 ( ) ( ) v V G G G d v and ( ) ( ) ( ) ( ) 2 G d v d v G uv E G  G    , where dG(v) is the degree of the vertex v. In this paper we compute these indices for link and splice of graphs. In continuation, with use these graph operations, we compute the first and the second multiplicative Zagreb indices for a class of dendrimers.
Survey of Ices toward Massive Young Stellar Objects. I. OCS, CO, OCN−, and CH3OH

An important tracer of the origin and evolution of cometary ices is the comparison with ices found in dense clouds and toward young stellar objects (YSOs). We present a survey of ices in the 2–5 μm spectra of 23 massive YSOs, taken with the NASA InfraRed Telescope Facility SpeX spectrometer. The 4.90 μm absorption band of OCS ice is detected in 20 sight lines, more than 5 times the previously known detections. The absorption profile shows little variation and is consistent with OCS embedded in CH3OH-rich ices, and proton-irradiated H2S or SO2-containing ices. The OCS column densities correlate well with those of CH3OH and OCN−, but not with H2O and apolar CO ice. This association of OCS with CH3OH and OCN− firmly establishes their formation location deep inside dense clouds or protostellar envelopes. The median composition of this ice phase toward massive YSOs, as a percentage of H2O, is CO:CH3OH:OCN−:OCS = 24:20:1.53:0.15. CS, due to its low abundance, is likely not the main precursor to OCS. Sulfurization of CO is likely needed, although the source of this sulfur is not well constrained. Compared to massive YSOs, low-mass YSOs and dense clouds have similar or somewhat lower CO and CH3OH ice abundances, but less OCN− and more apolar CO, while OCS awaits detection. Comets tend to be underabundant in carbon-bearing species, but this does not appear to be the case for OCS, perhaps signalling OCS production in protoplanetary disks.