Miracle-dz/UniFAR-FTUs huggingface dataset:
Complexity Framework For Forbidden Subgraphs

For any particular class of graphs, algorithms for computational problems restricted to the class often rely on structural properties that depend on the specific problem at hand. This begs the question if a large set of such results can be explained by some common problem conditions. We propose such conditions for $HH$-subgraph-free graphs. For a set of graphs $HH$, a graph $G$ is $HH$-subgraph-free if $G$ does not contain any of graph from $H$ as a subgraph. Our conditions are easy to state. A graph problem must be efficiently solvable on graphs of bounded treewidth, computationally hard on subcubic graphs, and computational hardness must be preserved under edge subdivision of subcubic graphs. Our meta-classification says that if a graph problem satisfies all three conditions, then for every finite set $HH$, it is ``efficiently solvable'' on $HH$-subgraph-free graphs if $HH$ contains a disjoint union of one or more paths and subdivided claws, and is ``computationally hard'' otherwise. We illustrate the broad applicability of our meta-classification by obtaining a dichotomy between polynomial-time solvability and NP-completeness for many well-known partitioning, covering and packing problems, network design problems and width parameter problems. For other problems, we obtain a dichotomy between almost-linear-time solvability and having no subquadratic-time algorithm (conditioned on some hardness hypotheses). The proposed framework thus gives a simple pathway to determine the complexity of graph problems on $HH$-subgraph-free graphs. This is confirmed even more by the fact that along the way, we uncover and resolve several open questions from the literature.
Word-Level Representation From Bytes For Language Modeling

Modern language models mostly take sub-words as input, a design that balances the trade-off between vocabulary size, number of parameters, and performance. However, sub-word tokenization still has disadvantages like not being robust to noise and difficult to generalize to new languages. Also, the current trend of scaling up models reveals that larger models require larger embeddings but that makes parallelization hard. Previous work on image classification proves splitting raw input into a sequence of chucks is a strong, model-agnostic inductive bias. Based on this observation, we rethink the existing character-aware method that takes character-level inputs but makes word-level sequence modeling and prediction. We overhaul this method by introducing a cross-attention network that builds word-level representation directly from bytes, and a sub-word level prediction based on word-level hidden states to avoid the time and space requirement of word-level prediction. With these two improvements combined, we have a token free model with slim input embeddings for downstream tasks. We name our method Byte2Word and perform evaluations on language modeling and text classification. Experiments show that Byte2Word is on par with the strong sub-word baseline BERT but only takes up 10\% of embedding size. We further test our method on synthetic noise and cross-lingual transfer and find it competitive to baseline methods on both settings.
Effects of Sim2Real Image Translation on Lane Keeping Assist System in CARLA Simulator

Autonomous vehicle simulation has the advantage of testing algorithms in various environment variables and scenarios without wasting time and resources, however, there is a visual gap with the real-world. In this paper, we trained DCLGAN to realistically convert the image of the CARLA simulator and evaluated the effect of the Sim2Real conversion focusing on the LKAS (Lane Keeping Assist System) algorithm. In order to avoid the case where the lane is translated distortedly by DCLGAN, we found the optimal training hyperparameter using FSIM (feature-similarity). After training, we built a system that connected the DCLGAN model with CARLA and AV in real-time. Then, we collected data (e.g. images, GPS) and analyzed them using the following four methods. First, image reality was measured with FID, which we verified quantitatively reflects the lane characteristics. CARLA images that passed through DCLGAN had smaller FID values than the original images. Second, lane segmentation accuracy through ENet-SAD was improved by DCLGAN. Third, in the curved route, the case of using DCLGAN drove closer to the center of the lane and had a high success rate. Lastly, in the straight route, DCLGAN improved lane restoring ability after deviating from the center of the lane as much as in reality.
Effect of Topology on the Collapse Transition and the Instantaneous Shape of a Model Heteropolymer

The effect of topology on the collapse transition and instantaneous shape of an energy polydisperse polymer (a model heteropolymer) is studied by means of computer simulations. In particular, we consider three different chain topology, namely, linear (L), ring (R) and trefoil knot (T). The heteropolymer is modeled by assigning each monomer an interaction parameter, εi, drawn randomly from a Gaussian distribution. Through chain size scaling the transition temperature, θ, is located and compared among the chains of different topogies. The influence of topology is reflected in the value of θ and observed that θ(L) > θ(R) > θ(T) in a similar fashion to that of the homopolymer counterpart. Also studied chain size distributions, and the shape changes across the transition temperature characterised through shape parameters based on the eigenvalues of the gyration tensor. It is observed that, for the model heteropolymer, in addition to chain topology the θ-temperature also depends on energy polydispersity.
You Don't Know When I Will Arrive: Unpredictable Controller Synthesis for Temporal Logic Tasks

In this paper, we investigate the problem of synthesizing controllers for temporal logic specifications under security constraint. We assume that there exists a passive intruder (eavesdropper) that can partially observe the behavior of the system. For the purpose of security, we require that the system's behaviors are unpredictable in the sense that the intruder cannot determine for sure that the system will exactly accomplish the task in $K$ steps ahead. This problem is particularly challenging since future information is involved in the synthesis process. We propose a novel information structure that predicts the effect of control in the future. A sound and complete algorithm is developed to synthesize a controller which ensures both task completion and security guarantee. The proposed approach is illustrated by a case study of robot task planning.
Tailored presolve techniques in branch‐and‐bound method for fast mixed‐integer optimal control applications

Mixed‐integer model predictive control (MI‐MPC) can be a powerful tool for controlling hybrid systems. In case of a linear‐quadratic objective in combination with linear or piecewise‐linear system dynamics and inequality constraints, MI‐MPC needs to solve a mixed‐integer quadratic program (MIQP) at each sampling time step. This paper presents a collection of exact block‐sparse presolve techniques to efficiently remove decision variables, and to remove or tighten inequality constraints, tailored to mixed‐integer optimal control problems. In addition, we describe a novel approach based on a heuristic presolve algorithm to compute a feasible but possibly suboptimal MIQP solution. We present benchmarking results for a C code implementation of the proposed BB‐ASIPM solver, including a branch‐and‐bound (B&B) method with the proposed tailored presolve techniques and an active‐set based interior point method (ASIPM), compared against multiple state‐of‐the‐art MIQP solvers on a case study of motion planning with obstacle avoidance constraints. Finally, we demonstrate the feasibility and computational performance of the BB‐ASIPM solver in embedded system on a dSPACE Scalexio real‐time rapid prototyping unit for a second case study of stabilization for an underactuated cart‐pole with soft contacts.
Coverage of credible intervals in Bayesian multivariate isotonic regression

We consider the nonparametric multivariate isotonic regression problem, where the regression function is assumed to be nondecreasing with respect to each predictor. Our goal is to construct a Bayesian credible interval for the function value at a given interior point with assured limiting frequentist coverage. We put a prior on unrestricted step-functions, but make inference using the induced posterior measure by an"immersion map"from the space of unrestricted functions to that of multivariate monotone functions. This allows maintaining the natural conjugacy for posterior sampling. A natural immersion map to use is a projection via a distance, but in the present context, a block isotonization map is found to be more useful. The approach of using the induced"immersion posterior"measure instead of the original posterior to make inference provides a useful extension of the Bayesian paradigm, particularly helpful when the model space is restricted by some complex relations. We establish a key weak convergence result for the posterior distribution of the function at a point in terms of some functional of a multi-indexed Gaussian process that leads to an expression for the limiting coverage of the Bayesian credible interval. Analogous to a recent result for univariate monotone functions, we find that the limiting coverage is slightly higher than the credibility, the opposite of a phenomenon observed in smoothing problems. Interestingly, the relation between credibility and limiting coverage does not involve any unknown parameter. Hence by a recalibration procedure, we can get a predetermined asymptotic coverage by choosing a suitable credibility level smaller than the targeted coverage, and thus also shorten the credible intervals.
Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Framework

Many industrial applications rely on real-time optimization to improve key performance indicators. In the case of unknown process characteristics, real-time optimization becomes challenging, particularly for the satisfaction of safety constraints. In this paper, we demonstrate the application of an adaptive and explorative real-time optimization framework to an industrial refrigeration process, where we learn the process characteristics through changes in process control targets and through exploration to satisfy safety constraints. We quantify the uncertainty in unknown compressor characteristics of the refrigeration plant by using Gaussian processes and incorporate this uncertainty into the objective function of the real-time optimization problem as a weighted cost term. We adaptively control the weight of this term to drive exploration. The results of our simulation experiments indicate the proposed approach can help to increase the energy efficiency of the considered refrigeration process, closely approximating the performance of a solution that has complete information about the compressor performance characteristics.
Developmental Plasticity-inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks

Developmental plasticity plays a prominent role in shaping the brain's structure during ongoing learning in response to dynamically changing environments. However, the existing network compression methods for deep artificial neural networks (ANNs) and spiking neural networks (SNNs) draw little inspiration from the brain's developmental plasticity mechanisms, thus limiting their ability to learn efficiently, rapidly, and accurately. This paper proposed a developmental plasticity-inspired adaptive pruning (DPAP) method, with inspiration from the adaptive developmental pruning of dendritic spines, synapses, and neurons according to the"use it or lose it, gradually decay"principle. The proposed DPAP model considers multiple biologically realistic mechanisms (such as dendritic spine dynamic plasticity, activity-dependent neural spiking trace, and local synaptic plasticity), with the addition of an adaptive pruning strategy, so that the network structure can be dynamically optimized during learning without any pre-training and retraining. We demonstrated that the proposed DPAP method applied to deep ANNs and SNNs could learn efficient network architectures. Extensive comparative experiments show consistent and remarkable performance and speed boost with the extremely compressed networks on a diverse set of benchmark tasks, especially neuromorphic datasets for SNNs. This work explores how developmental plasticity enables the complex deep networks to gradually evolve into brain-like efficient and compact structures, eventually achieving state-of-the-art (SOTA) performance for biologically realistic SNNs.
Modeling Aggregate Interaction Effects in Many Variable Observational Studies

A method to examine aggregate interaction effects in high dimensional categorical data from observational studies is presented. Koch and colleagues (Koch et al. 1977) suggested using characteristics of subpopulations to model outcomes in such a situation. This technique is extended into a fuzzy partitioning of the observational space. This partitioning of the outcome space is based on representing the probabilities of response as a convex combination of archetype variables. The methodology resembles methods used to analyze samples of compounds in analytic chemistry. A grade of membership (GoM) approach is used to characterize the convex and bounded data. Subpopulations can then be defined using these grade of membership scores.
Characterizing a Neutron-Induced Fault Model for Deep Neural Networks

The reliability evaluation of deep neural networks (DNNs) executed on graphic processing units (GPUs) is a challenging problem, since the hardware architecture is highly complex, and the software frameworks are composed of many layers of abstraction. While software-level fault injection is a common and fast way to evaluate the reliability of complex applications, it may produce unrealistic results, since it has limited access to the hardware resources, and the adopted fault models may be too naive (i.e., single and double bit flips). Contrarily, physical fault injection with neutron beam provides realistic error rates but lacks fault propagation visibility. This article proposes a characterization of the DNN fault model combining both neutron beam experiments and fault injection at the software level. We exposed GPUs running general matrix multiplication (GEMM) and DNNs to beam neutrons to measure their error rate. On DNNs, we observe that the percentage of critical errors can be up to 61% and show that the error correction code (ECC) is ineffective in reducing critical errors. We then performed a complementary software-level fault injection, using fault models derived from register-transfer level (RTL) simulations. Our results show that by injecting complex fault models, the version 3 of you only look once (YOLOv3) misdetection rate is validated to be very close to the rate measured with beam experiments, which is $8.66\times $ higher than the one measured with fault injection using only single-bit flips.
Reason from Context with Self-supervised Learning

Self-supervised learning (SSL) learns to capture discriminative visual features useful for knowledge transfers. To better accommodate the object-centric nature of current downstream tasks such as object recognition and detection, various methods have been proposed to suppress contextual biases or disentangle objects from contexts. Nevertheless, these methods may prove inadequate in situations where object identity needs to be reasoned from associated context, such as recognizing or inferring tiny or obscured objects. As an initial effort in the SSL literature, we investigate whether and how contextual associations can be enhanced for visual reasoning within SSL regimes, by (a) proposing a new Self-supervised method with external memories for Context Reasoning (SeCo), and (b) introducing two new downstream tasks, lift-the-flap and object priming, addressing the problems of"what"and"where"in context reasoning. In both tasks, SeCo outperformed all state-of-the-art (SOTA) SSL methods by a significant margin. Our network analysis revealed that the proposed external memory in SeCo learns to store prior contextual knowledge, facilitating target identity inference in the lift-the-flap task. Moreover, we conducted psychophysics experiments and introduced a Human benchmark in Object Priming dataset (HOP). Our results demonstrate that SeCo exhibits human-like behaviors.
Powderworld: A Platform for Understanding Generalization via Rich Task Distributions

One of the grand challenges of reinforcement learning is the ability to generalize to new tasks. However, general agents require a set of rich, diverse tasks to train on. Designing a `foundation environment' for such tasks is tricky -- the ideal environment would support a range of emergent phenomena, an expressive task space, and fast runtime. To take a step towards addressing this research bottleneck, this work presents Powderworld, a lightweight yet expressive simulation environment running directly on the GPU. Within Powderworld, two motivating challenges distributions are presented, one for world-modelling and one for reinforcement learning. Each contains hand-designed test tasks to examine generalization. Experiments indicate that increasing the environment's complexity improves generalization for world models and certain reinforcement learning agents, yet may inhibit learning in high-variance environments. Powderworld aims to support the study of generalization by providing a source of diverse tasks arising from the same core rules.
IDEALI: intuitively localising connected devices in order to support autonomy

The ability to localise a smart device is very useful to visually or cognitively impaired people. Localisation-capable technologies are becoming more readily available as off-the-shelf components. In this paper, we highlight the need for such a service in the field of health and autonomy, especially for disabled people. We introduce a model for Semantic Position Description (SPD) (e.g."The pill organiser in on the kitchen table") as well as various algorithms that transform raw distance estimations to SPD related to proximity, alignment and room identification. Two of these algorithms are evaluated using the LocURa4IoT testbed. The results are compared to the output of a pre-experiment involving ten human participants in the Maison Intelligente de Blagnac. The two studies indicate that both approaches converge up to 90% of the time.
A new graph topology. connections with the compact open topology

Let E be a closed connected subset of and let C be the set of all the closed non-empty subsets of E. Given Ω E С, let GΩ denote the set of all the graphs of continuous functions in . Let . We endow G with a new topology called Τ-topology. It is strictly coarser than Hausdorff metric topology and strictly finer than the topology of uniform convergence of distance functionals on bounded sets of The topological space (G,τ) is homeomorphic to the quotient space with a suitable equivalence relation R. The relationships between τ‐topology and the topologies introduced in GΩ by other Authors are explored.
Can lies be faked? Comparing low-stakes and high-stakes deception video datasets from a Machine Learning perspective

Despite the great impact of lies in human societies and a meager 54% human accuracy for Deception Detection (DD), Machine Learning systems that perform automated DD are still not viable for proper application in real-life settings due to data scarcity. Few publicly available DD datasets exist and the creation of new datasets is hindered by the conceptual distinction between low-stakes and high-stakes lies. Theoretically, the two kinds of lies are so distinct that a dataset of one kind could not be used for applications for the other kind. Even though it is easier to acquire data on low-stakes deception since it can be simulated (faked) in controlled settings, these lies do not hold the same significance or depth as genuine high-stakes lies, which are much harder to obtain and hold the practical interest of automated DD systems. To investigate whether this distinction holds true from a practical perspective, we design several experiments comparing a high-stakes DD dataset and a low-stakes DD dataset evaluating their results on a Deep Learning classifier working exclusively from video data. In our experiments, a network trained in low-stakes lies had better accuracy classifying high-stakes deception than low-stakes, although using low-stakes lies as an augmentation strategy for the high-stakes dataset decreased its accuracy.
Frequency space derivation of linear and non-linear memory gravitational wave signals from eccentric binary orbits

The memory effect in gravitational wave (GW) signals is the phenomenon, wherein the relative position of two inertial GW detectors undergoes a permanent displacement owing to the passage of GWs through them. Measurement of the memory signal is an important target for future observations as it establishes a connection between observations with field-theoretic results like the soft-graviton theorems. Theoretically, the memory signal is predicted at the leading order quadrupole formula for sources like binaries in hyperbolic orbits. This can be in the realm of observations by Advanced-LIGO, Einstein-Telescope, or LISA for stellar mass black-holes scattered by the super-massive black-hole at the galactic center. Apart from the direct memory component there is a non-linear memory signal in the secondary GW emitted from the primary GW chirp-signals emitted by coalescing binaries. In this paper, we compute the gravitational wave signals and their energy spectrum using the field-theoretic method by computing the scattering amplitudes for eccentric elliptical and hyperbolic binary orbits. The field theoretic calculation gives us the gravitational waveforms of linear and non-linear memory signals directly in the frequency space. The frequency domain templates are useful for extracting signals from the data. We compare our results with other calculations of linear and non-linear memory signals in literature and point out novel features we find in our calculations like the presence of $\log(\omega)$ terms in the linear memory from hyperbolic orbits.
Sarcasm Detection Framework Using Emotion and Sentiment Features

Sarcasm detection is an essential task that can help identify the actual sentiment in user-generated data, such as discussion forums or tweets. Sarcasm is a sophisticated form of linguistic expression because its surface meaning usually contradicts its inner, deeper meaning. Such incongruity is the essential component of sarcasm, however, it makes sarcasm detection quite a challenging task. In this paper, we propose a model, which incorporates emotion and sentiment features to capture the incongruity intrinsic to sarcasm. We use CNN and pre-trained Transformer to capture context features. Our approach outperformed previous state-of-the-art re-sults on four datasets from social networking platforms and online media