Miracle-dz/UniFAR-FTUs huggingface dataset:
Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning

Machine reading comprehension has aroused wide concerns, since it explores the potential of model for text understanding. To further equip the machine with the reasoning capability, the challenging task of logical reasoning is proposed. Previous works on logical reasoning have proposed some strategies to extract the logical units from different aspects. However, there still remains a challenge to model the long distance dependency among the logical units. Also, it is demanding to uncover the logical structures of the text and further fuse the discrete logic to the continuous text embedding. To tackle the above issues, we propose an end-to-end model Logiformer which utilizes a two-branch graph transformer network for logical reasoning of text. Firstly, we introduce different extraction strategies to split the text into two sets of logical units, and construct the logical graph and the syntax graph respectively. The logical graph models the causal relations for the logical branch while the syntax graph captures the co-occurrence relations for the syntax branch. Secondly, to model the long distance dependency, the node sequence from each graph is fed into the fully connected graph transformer structures. The two adjacent matrices are viewed as the attention biases for the graph transformer layers, which map the discrete logical structures to the continuous text embedding space. Thirdly, a dynamic gate mechanism and a question-aware self-attention module are introduced before the answer prediction to update the features. The reasoning process provides the interpretability by employing the logical units, which are consistent with human cognition. The experimental results show the superiority of our model, which outperforms the state-of-the-art single model on two logical reasoning benchmarks.
A Feasible Sequential Linear Programming Algorithm with Application to Time-Optimal Path Planning Problems

In this paper, we propose a Feasible Sequential Linear Programming (FSLP) algorithm applied to time-optimal control problems (TOCP) obtained through direct multiple shooting discretization. This method is motivated by TOCP with nonlinear constraints which arise in motion planning of mechatronic systems. The algorithm applies a trust-region globalization strategy ensuring global convergence. For fully determined problems our algorithm provides locally quadratic convergence. Moreover, the algorithm keeps all iterates feasible enabling early termination at suboptimal, feasible solutions. This additional feasibility is achieved by an efficient iterative strategy using evaluations of constraints, i.e., zero-order information. Convergence of the feasibility iterations can be enforced by reduction of the trust-region radius. These feasibility iterations maintain feasibility for general Nonlinear Programs (NLP). Therefore, the algorithm is applicable to general NLPs. We demonstrate our algorithm’s efficiency and the feasibility update strategy on a TOCP of an overhead crane motion planning simulation case.
Large Neighborhood Search based on Neural Construction Heuristics

: We propose a Large Neighborhood Search (LNS) approach utilizing a learned construction heuristic based on neural networks as repair operator to solve the vehicle routing problem with time windows (VRPTW). Our method uses graph neural networks to encode the problem and auto-regressively decodes a solution and is trained with reinforcement learning on the construction task without requiring any labels for supervision. The neural repair operator is combined with a local search routine, heuristic destruction operators and a selection procedure applied to a small population to arrive at a sophisticated solution approach. The key idea is to use the learned model to re-construct the partially destructed solution and to introduce randomness via the destruction heuristics (or the stochastic policy itself) to eﬀectively explore a large neighborhood.
Superredundancy: A tool for Boolean formula minimization complexity analysis

A superredundant clause is a clause that is redundant in the resolution closure of a formula. The converse concept of superirredundancy ensures membership of the clause in all minimal CNF formulae that are equivalent to the given one. This allows for building formulae where some clauses are ﬁxed when minimizing size. An example are proofs of complexity hardness of the problems of minimal formula size. Others are proofs of size when forgetting variables or revising a formula. Most clauses can be made superirredundant by splitting them over a new variable.
Sharp bounds on the height of K-semistable toric Fano varieties, I

Inspired by Fujita's algebro-geometric result that complex projective space has maximal degree among all K-semistable Fano varieties, we conjecture that the height of a K-semistable metrized arithmetic Fano variety X of relative dimension n is maximal when X is the projective space over the integers, endowed with the Fubini-Study metric. Our main result establishes the conjecture for the canonical integral model of a toric Fano variety when n less than or equal to 6 (the extension to higher dimensions is conditioned on a conjectural"gap hypothesis"for the degree). Translated into toric Kahler geometry this result yields a sharp lower bound on a toric invariant introduced by Donaldson, defined as the minimum of the toric Mabuchi functional. We furthermore reformulate our conjecture as an optimal lower bound on Odaka's modular height. Along the way we show how to control the height of the canonical toric model X, with respect to the Kahler-Einstein metric, by the algebro-geometric degree of X in any dimension n. In a sequel to this paper our height conjecture is established for any projective diagonal Fano hypersurface, by exploiting a more general logarithmic setup.
GRAND for Rayleigh Fading Channels

Guessing Random Additive Noise Decoding (GRAND) is a code-agnostic decoding technique for short-length and high-rate channel codes. GRAND attempts to guess the channel-induced noise by generating Test Error Patterns (TEPs), and the sequence of TEP generation is the primary distinction between GRAND variants. In this work, we extend the application of GRAND to multipath frequency non-selective Rayleigh fading communication channels, and we refer to this GRAND variant as Fading-GRAND. The proposed Fading-GRAND adapts its TEP generation to the fading conditions of the underlying communication channel, outperforming traditional channel code decoders in scenarios with L spatial diversity branches as well as scenarios with no diversity. Numerical simulation results show that the Fading-GRAND outperforms the traditional Berlekamp-Massey (B-M) decoder for decoding BCH code (127, 106) and BCH code (127, 113) by $0.5\sim 6.5\mathrm{dB}$ at a target FER of $10^{-7}$. Similarly, Fading-GRAND outperforms GRANDAB, the hard-input variation of GRAND, by $0.2\sim 8\mathbf{d B}$ at a target FER of $10^{-7}$ with CRC (128, 104) code and RLC (128,104). Furthermore the average complexity of Fading-GRAND, at $\frac{E_{b}}{N_{0}}$ corresponding to target FER of $10^{-7}$, is $\frac{1}{2}\times\sim\frac{1}{46}\times$ the complexity of GRANDAB.
A Unified Approach to Semantic Information and Communication Based on Probabilistic Logic

Traditionally, studies on technical communication (TC) are based on stochastic modeling and manipulation. This is not sufficient for semantic communication (SC) where semantic elements are logically connected, rather than stochastically correlated. To fill this void, by leveraging a logical programming language called probabilistic logic (ProbLog), we propose a unified approach to semantic information and communication through the interplay between TC and SC. Building on the well-established existing TC layer, we introduce, in this paper, a SC layer that utilizes knowledge bases of communicating parties for the exchange of semantic information. These knowledge bases are logically described, manipulated, and exploited using ProbLog. To allow efficient interactions between SC and TC layers, various measures are proposed in this paper using the entropy of a clause in a knowledge base. These measurements can account for various technical problems in SC, such as message selection to improve the receiver’s knowledge base. Extending this, we present few selected examples of how the SC and TC layers interact with each other, s while taking into account constraints of physical channels and efficiently utilizing channel resources.
Ensuring Resilience Against Stealthy Attacks on Cyber-Physical Systems

This article provides a tool for analyzing mechanisms that aim to achieve resilience against stealthy, or undetectable, attacks on cyber-physical systems (CPSs). We consider attackers who are able to corrupt all of the inputs and outputs of the system. To counter such attackers, a response scheme must be implemented that keeps the attacker from corrupting the inputs and outputs of the system for certain periods of time. To aid in the design of such a response scheme, our tool provides sufficient lengths for these periods of time in order to ensure safety with a particular probability. We provide a conservative upper bound on how long the system can remain under stealthy attack before the safety constraints are violated. Furthermore, we show how a detector limits the set of biases an attacker can exert on the system while still remaining stealthy, aiding a system operator in the design of the detector. Our contributions are demonstrated with an illustrative example.
Unfolding, higher spins, metaplectic groups and resolution of classical singularities

We review and extend some recent results concerning the analysis of spacetime singularities in four-dimensional higher spin gravity, summarizing how the coupling of the gravitational ﬁeld to massless higher spins may provide resolution mechanisms. We elucidate such mechanisms at the level of curvature singularities and degenerate metrics in exact as well as linearized solutions to Vasiliev’s equations. As a preamble, we review the underlying higher-spin algebra and its metaplectic group extensions, after which we detail various gauge functions encoding the 𝐴𝑑𝑆 4 vacuum and the non-rotating Bañados–Gomberoﬀ–Martinez(BGM) metric, the four-dimensional lift of the spinless BTZ black hole, in diﬀerent coordinate patches related by transition functions. We then revisit how, within the unfolded formalism, it is natural extend the BGM black hole through its causal singularity. Finally, we compare the metric-like and unfolded descriptions of scalar ﬂuctuations over the (extended) BGM background, showing how the latter description maps singularities to well-deﬁned metaplectic group elements providing regular values for the Weyl zero-form master ﬁeld, which thus admits continuation over the full extended BGM spacetime.
Quantum Magnetometer with Dual‐Coupling Optomechanics

An experimentally feasible magnetometer based on a dual‐coupling optomechanical system is proposed, where the radiation‐pressure coupling transduces the magnetic signal to the optical phase, and the quadratic optomechanical interaction induces a periodic squeezing effect. The latter not only amplifies the signal to be measured, but also accelerates the signal transducing rate characterized by an experimentally observable phase accumulation efficiency. In the vicinity of opto‐mechanical decoupled time, the ultimate bound to the estimability of magnetic signal is proportional to exp(−6r)$\exp (-6r)$ , and then the optimized accuracy of estimation can be enhanced nearly three orders with a controllable squeezing parameter r<1$r<1$ . Moreover, this proposal is robust against the mechanical thermal noise, and the sensitivity of a specific measurement can reach to the order of 10−17T(Hz)−1${10}^{-17}\mathrm{T}{(\sqrt{\mathrm{Hz}})}^{-1}$ in the presence of dissipations and without ground state cooling of mechanical oscillator. The proposal fundamentally broadens the fields of quantum metrology and cavity optomechanics, with the potential application for on‐chip magnetic detection with high precision.
RADNet: A Deep Neural Network Model for Robust Perception in Moving Autonomous Systems

—Interactive autonomous applications require ro- bustness of the perception engine to artifacts in unconstrained videos. In this paper, we examine the effect of camera motion on the task of action detection. We develop a novel ranking method to rank videos based on the degree of global camera motion. For the high ranking camera videos we show that the accuracy of action detection is decreased. We propose an action detection pipeline that is robust to the camera motion effect and verify it empirically. Speciﬁcally, we do actor feature alignment across frames and couple global scene features with local actor-speciﬁc features. We do feature alignment using a novel formulation of the Spatio-temporal Sampling Network (STSN) but with multi-scale offset prediction and reﬁnement using a pyramid structure. We also propose a novel input dependent weighted averaging strategy for fusing local and global features. We show the applicability of our network on our dataset of moving camera videos with high camera motion (MOVE dataset) with a 4.1% increase in frame mAP and 17% increase in video mAP.
Graph Learning From Multivariate Dependent Time Series Via A Multi-Attribute Formulation

We consider the problem of inferring the conditional independence graph (CIG) of a high-dimensional stationary multivariate Gaussian time series. In a time series graph, each component of the vector series is represented by distinct node, and associations between components are represented by edges between the corresponding nodes. We formulate the problem as one of multi-attribute graph estimation for random vectors where a vector is associated with each node of the graph. At each node, the associated random vector consists of a time series component and its delayed copies. We present an alternating direction method of multipliers (ADMM) solution to minimize a sparse-group lasso penalized negative pseudo log-likelihood objective function to estimate the precision matrix of the random vector associated with the entire multi-attribute graph. The time series CIG is then inferred from the estimated precision matrix. A theoretical analysis is provided. Numerical results illustrate the proposed approach which outperforms existing frequency-domain approaches in correctly detecting the graph edges.
A Note on"A survey of preference estimation with unobserved choice set heterogeneity"by Gregory S. Crawford, Rachel Griffith, and Alessandro Iaria

Crawford’s et al. (2021) article on estimation of discrete choice models with unobserved or latent consideration sets, presents a unified framework to address the problem in practice by using “sufficient sets”, defined as a combination of past observed choices. The proposed approach is sustained in a re-interpretation of a consistency result by McFadden (1978) for the problem of sampling of alternatives, but the usage of that result in Crawford et al. (2021) is imprecise in an important matter. It is stated that consistency would be attained if any subset of the true consideration set is used for estimation, but McFadden (1978) shows that, in general, one needs to do a sampling correction that depends on the protocol used to draw the choice set. This note derives the sampling correction that is required when the choice set for estimation is built from past choices. Then, it formalizes the conditions under which such correction would fulfill the uniform condition property and can therefore be ignored when building practical estimators, such as the ones analyzed by Crawford et al. (2021). Crawford et al. (2021) study the problem of choice sequences but, to simplify the notation and exposition of the issue, this note will refer to the modeling of a single choice in a traditional discrete choice model. The note begins defining the choice model, later revising McFadden’s (1978) work on sampling of alternatives, and finally showing how it can be adapted to tackle the problem of unobserved or latent consideration sets and how it relates to Crawford’s et al. (2021) results.
Learning to Get Up

Getting up from an arbitrary fallen state is a basic human skill. Existing methods for learning this skill often generate highly dynamic and erratic get-up motions, which do not resemble human get-up strategies, or are based on tracking recorded human get-up motions. In this paper, we present a staged approach using reinforcement learning, without recourse to motion capture data. The method first takes advantage of a strong character model, which facilitates the discovery of solution modes. A second stage then learns to adapt the control policy to work with progressively weaker versions of the character. Finally, a third stage learns control policies that can reproduce the weaker get-up motions at much slower speeds. We show that across multiple runs, the method can discover a diverse variety of get-up strategies, and execute them at a variety of speeds. The results usually produce policies that use a final stand-up strategy that is common to the recovery motions seen from all initial states. However, we also find policies for which different strategies are seen for prone and supine initial fallen states. The learned get-up control strategies often have significant static stability, i.e., they can be paused at a variety of points during the get-up motion. We further test our method on novel constrained scenarios, such as having a leg and an arm in a cast.
Katana: Dual Slicing Based Context for Learning Bug Fixes

Contextual information plays a vital role for software developers when understanding and fixing a bug. Consequently, deep learning based program repair techniques leverage context for bug fixes. However, existing techniques treat context in an arbitrary manner, by extracting code in close proximity of the buggy statement within the enclosing file, class, or method, without any analysis to find actual relations with the bug. To reduce noise, they use a predefined maximum limit on the number of tokens to be used as context. We present a program slicing based approach, in which instead of arbitrarily including code as context, we analyze statements that have a control or data dependency on the buggy statement. We propose a novel concept called dual slicing, which leverages the context of both buggy and fixed versions of the code to capture relevant repair ingredients. We present our technique and tool called Katana, the first to apply slicing-based context for a program repair task. The results show that Katana effectively preserves sufficient information for a model to choose contextual information while reducing noise. We compare against four recent state-of-the-art context-aware program repair techniques. Our results show that Katana fixes between 1.5 and 3.7 times more bugs than existing techniques.
Lightweight Image Enhancement Network for Mobile Devices Using Self-Feature Extraction and Dense Modulation

Recently, as the use of media services including Social Networking Service (SNS) has been increased, customers who transmit and share their images and videos through the online platforms by using mobile phones also have increased. Since these images are processed with downsampling and compression through the online platform server, their visual quality is noticeably degraded when stored from the server. In addition, the cases tend to increase that users capture their oldphotographs by using mobile phone cameras and store them. It causes the quality worsening, since most old-photographs have some degradation characteristics such as faded color and irregular noise patterns, etc. Therefore, there are increasing needs for image enhancement solutions, which reconstruct a high-quality image from a low-quality image. Such solutions are operated on the mobile device requirements, and can be applied for image quality enhancement including face renovation and old-photograph restoration. As depicted in Fig. 1, image enhancement solutions perform not only super-resolution with compression artifacts removal for low-resolution images, but detail improvement including color enhancement while maintaining the resolution. Many studies have introduced deep convolutional network based image super-resolution methods [1-3]. It is obvious that CNN has become a disruptive technology and noticeably changed the landscape of image processing applications [4], [5]. They exhibit significant improvements for semantic fidelity and visual reality of reconstructed images. However, these solutions have limitations to perform in lightweight models for SoC requirements while keeping identities of their networks. In spite of increasing demands of image enhancement methods which enable to operate in ondevice environments, conventional lightweight networks from existing methods show low performance with undesired artifacts, such as aliasing effects. Lightweight Image Enhancement Network for Mobile Devices Using Self-Feature Extraction and Dense Modulation
Learning user-defined sub-goals using memory editing in reinforcement learning

The aim of reinforcement learning (RL) is to allow the agent to achieve the ﬁnal goal. Most RL studies have focused on improving the eﬃciency of learning to achieve the ﬁnal goal faster. However, the RL model is very diﬃcult to modify an intermediate route in the process of reaching the ﬁnal goal. That is, the agent cannot be under control to achieve other sub-goals in the existing studies. If the agent can go through the sub-goals on the way to the destination, the RL can be applied and stud-ied in various ﬁelds. In this study, I propose a methodology to achieve the user-deﬁned sub-goals as well as the ﬁnal goal using memory editing. The memory editing is performed to generate various sub-goals and give an additional reward to the agent. In addition, the sub-goals are separately learned from the ﬁnal goal. I set two simple environments and various scenarios in the test environments. As a result, the agent almost successfully passed the sub-goals as well as the ﬁnal goal under control. Moreover, the agent was able to be induced to visit the novel state indirectly in the environments. I expect that this methodology can be used in the ﬁelds that need to control the agent in a variety of scenarios.
Scheduling for Multi-Phase Parallelizable Jobs

With multiple identical unit speed servers, the online problem of scheduling jobs that migrate between two phases, limitedly parallelizable or completely sequential, and choosing their respective speeds to minimize the total flow time is considered. In the limited parallelizable regime, allocating k servers to a job, the speed extracted is $k^{1/\alpha},\ \alpha$ > 1, a sub-linear, concave speedup function, while in the sequential phase, a job can be processed by at most one server with a maximum speed of unity. A LCFS based algorithm is proposed for scheduling jobs which always assigns equal speed to the jobs that are in the same phase (limitedly parallelizable/sequential), and is shown to have a constant (dependent only on $\alpha$ > 1) competitive ratio. For the special case when all jobs are available beforehand, improved competitive ratio is obtained.