Miracle-dz/UniFAR-FTUs huggingface dataset:
QAPPA: Quantization-Aware Power, Performance, and Area Modeling of DNN Accelerators

As the machine learning and systems community strives to achieve higher energy-efficiency through custom DNN accelerators and model compression techniques, there is a need for a design space exploration framework that incorporates quantization-aware processing elements into the accelerator design space while having accurate and fast power, performance, and area models. In this work, we present QAPPA, a highly parameterized quantization-aware power, performance, and area modeling framework for DNN accelerators. Our framework can facilitate the future research on design space exploration of DNN accelerators for various design choices such as bit precision, processing element type, scratchpad sizes of processing elements, global buffer size, device bandwidth, number of total processing elements in the the design, and DNN workloads. Our results show that different bit precisions and processing element types lead to significant differences in terms of performance per area and energy. Specifically, our proposed lightweight processing elements achieve up to 4.9x more performance per area and energy improvement when compared to INT16 based implementation.
Deep neural network classifier for multidimensional functional data

We propose a new approach, called as functional deep neural network (FDNN), for classifying multidimensional functional data. Specifically, a deep neural network is trained based on the principal components of the training data which shall be used to predict the class label of a future data function. Unlike the popular functional discriminant analysis approaches which only work for one‐dimensional functional data, the proposed FDNN approach applies to general non‐Gaussian multidimensional functional data. Moreover, when the log density ratio possesses a locally connected functional modular structure, we show that FDNN achieves minimax optimality. The superiority of our approach is demonstrated through both simulated and real‐world datasets.
Empirical Advocacy of Bio-inspired Models for Robust Image Recognition

Deep convolutional neural networks (DCNNs) have rev-olutionized computer vision and are often advocated as good models of the human visual system. However, there are currently many shortcomings of DCNNs, which pre-clude them as a model of human vision. There are con-tinuous attempts to use features of the human visual system to improve the robustness of neural networks to data perturbations. We provide a detailed analysis of such bio-inspired models and their properties. To this end, we benchmark the robustness of several bio-inspired models against their most comparable baseline DCNN models. We ﬁnd that bio-inspired models tend to be adversarially robust without requiring any special data augmentation. Additionally, we ﬁnd that bio-inspired models beat adversarially trained models in the presence of more real-world common corruptions. Interestingly, we also ﬁnd that bio-inspired models tend to use both low and mid-frequency information, in con-trast to other DCNN models. We ﬁnd that this mix of frequency information makes them robust to both adversarial perturbations and common corruptions.
Non-normal interactions create socio-economic bubbles

Social networks often show sudden bursts of activity. By analysing meme stocks, the authors provide a proof-of concept for a generic endogenous mechanism that explains the emergence of financial bubbles. The theory correlates their size with the structure of social networks, emphasizing how influential individuals tend to impact others in a top-down manner. In social networks, bursts of activity often result from the imitative behavior between interacting agents. The Ising model, along with its variants in the social sciences, serves as a foundational framework to explain these phenomena through its critical properties. We propose an alternative generic mechanism for the emergence of collective exuberance within a broad class of agent-based models. We show that our model does not require the fine-tuning to a critical point, as is commonly done to explain bursts of activity using the Ising model and its variants. Instead, our approach hinges on the intrinsic non-symmetric and hierarchical organization of socio-economic networks. These non-normal networks exhibit transient and unsustainable surges in herd behavior across a wide range of control parameters even in the subcritical regime, thereby eliminating the need for the - arguably artificial - fine-tuning proximity to a critical point. To empirically validate our framework, we examine the behavior of meme stocks and establish a direct linkage between the size of financial bubbles and the degree of non-normality in the network, as quantified by the Kreiss constant. Our proposed mechanism presents an alternative that is more general than prevailing conceptions of instabilities in diverse social systems.
GPoeT-2: A GPT-2 Based Poem Generator

This project aims to produce the next volume of machine-generated poetry, a complex art form that can be structured and unstructured, and carries depth in the meaning between the lines. GPoeT-2 is based on fine-tuning a state of the art natural language model (i.e. GPT-2) to generate limericks, typically humorous structured poems consisting of five lines with a AABBA rhyming scheme. With a two-stage generation system utilizing both forward and reverse language modeling, GPoeT-2 is capable of freely generating limericks in diverse topics while following the rhyming structure without any seed phrase or a posteriori constraints.Based on the automated generation process, we explore a wide variety of evaluation metrics to quantify"good poetry,"including syntactical correctness, lexical diversity, and subject continuity. Finally, we present a collection of 94 categorized limericks that rank highly on the explored"good poetry"metrics to provoke human creativity.
SplitBFT: Improving Byzantine Fault Tolerance Safety Using Trusted Compartments

Byzantine fault-tolerant agreement (BFT) in a partially synchronous system usually requires 3f + 1 nodes to tolerate f faulty replicas. Due to their high throughput and finality property, BFT algorithms build the core of recent permissioned blockchains. As a complex and resource-demanding infrastructure, multiple cloud providers have started offering Blockchain-as-a-Service. This eases the deployment of permissioned blockchains but places the cloud provider in a central controlling position, thereby questioning blockchains' fault tolerance and decentralization properties and their underlying BFT algorithm. This paper presents SplitBFT, a new way to utilize trusted execution technology (TEEs), such as Intel SGX, to harden the safety and confidentiality guarantees of BFT systems, thereby strengthening the trust in could-based deployments of permissioned blockchains. Deviating from standard assumptions, SplitBFT acknowledges that code protected by trusted execution may fail. We address this by splitting and isolating the core logic of BFT protocols into multiple compartments resulting in a more resilient architecture. We apply SplitBFT to the traditional practical byzantine fault tolerance algorithm (PBFT) and evaluate it using SGX. Our results show that SplitBFT adds only a reasonable overhead compared to the non-compartmentalized variant.
DPO: Dynamic-Programming Optimization on Hybrid Constraints

In Bayesian inference, the most probable explanation (MPE) problem requests a variable instantiation with the highest probability given some evidence. Since a Bayesian network can be encoded as a literal-weighted CNF formula $\varphi$, we study Boolean MPE, a more general problem that requests a model $\tau$ of $\varphi$ with the highest weight, where the weight of $\tau$ is the product of weights of literals satisfied by $\tau$. It is known that Boolean MPE can be solved via reduction to (weighted partial) MaxSAT. Recent work proposed DPMC, a dynamic-programming model counter that leverages graph-decomposition techniques to construct project-join trees. A project-join tree is an execution plan that specifies how to conjoin clauses and project out variables. We build on DPMC and introduce DPO, a dynamic-programming optimizer that exactly solves Boolean MPE. By using algebraic decision diagrams (ADDs) to represent pseudo-Boolean (PB) functions, DPO is able to handle disjunctive clauses as well as XOR clauses. (Cardinality constraints and PB constraints may also be compactly represented by ADDs, so one can further extend DPO's support for hybrid inputs.) To test the competitiveness of DPO, we generate random XOR-CNF formulas. On these hybrid benchmarks, DPO significantly outperforms MaxHS, UWrMaxSat, and GaussMaxHS, which are state-of-the-art exact solvers for MaxSAT.
Finite element method for the quasiclassical theory of superconductivity

The Eilenberger-Larkin-Ovchinnikov-Eliashberg quasiclassical theory of superconductivity is a powerful method enabling studies of a wide range of equilibrium and non-equilibrium phenomena in conventional and unconventional superconductors. We introduce here a finite element method, based on a discontinuous Galerkin approach, to self-consistently solve the underlying transport equations for general device geometries, arbitrary mean free path and symmetry of the superconducting order parameter. We present results on i) the influence of scalar impurity scattering on phase crystals in $d$-wave superconducting grains at low temperatures and ii) the current flow and focusing in $d$-wave superconducting weak links, modeling recent experimental realizations of grooved high-temperature superconducting Dayem bridges. The high adaptability of this finite element method for quasiclassical theory paves the way for future investigations of superconducting devices and new physical phenomena in unconventional superconductors.
Sensing Time Effectiveness for Fitness to Drive Evaluation in Neurological Patients

We present a method to automatically calculate sensing time (ST) from the eye tracker data in subjects with neurological impairment using a driving simulator. ST presents the time interval for a person to notice the stimulus from its first occurrence. Precisely, we measured the time since the children started to cross the street until the drivers directed their look to the children. In comparison to the commonly used reaction time, ST does not require additional neuro-muscular responses such as braking and presents unique information on the sensory function. From 108 neurological patients recruited for the study, the analysis of ST was performed in overall 56 patients to assess fit-, unfit-, and conditionally-fit-to-drive patients. The results showed that the proposed method based on the YOLO (You Only Look Once) object detector is efficient for computing STs from the eye tracker data in neurological patients. We obtained discriminative results for fit-to-drive patients by application of Tukey’s Honest Significant Difference post hoc test ( p < 0.01), while no difference was observed between conditionally-fit and unfit-to-drive groups ( p = 0.542). Moreover, we show that time-to-collision (TTC), initial gaze distance (IGD) from pedestrians, and speed at the hazard onset did not influence the result, while the only significant interaction is among fitness, IGD, and TTC on ST. Although the proposed method can be applied to assess fitness to drive, we provide directions for future driving simulation-based evaluation and propose processing workflow to secure reliable ST calculation in other domains such as psychology, neuroscience, marketing, etc.
Quantum Transfer Learning for Wi-Fi Sensing

Beyond data communications, commercial-off-the-shelf Wi-Fi devices can be used to monitor human activities, track device locomotion, and sense the ambient environment. In particular, spatial beam attributes that are inherently available in the 60-GHz IEEE 802.11ad/ay standards have shown to be effective in terms of overhead and channel measurement granularity for these indoor sensing tasks. In this paper, we investigate transfer learning to mitigate domain shift in human monitoring tasks when Wi-Fi settings and environments change over time. As a proof-of-concept study, we consider quantum neural networks (QNN) as well as classical deep neural networks (DNN) for the future quantum-ready society. The effectiveness of both DNN and QNN is validated by an in-house experiment for human pose recognition, achieving greater than 90% accuracy with a limited data size.
Budget Pacing in Repeated Auctions: Regret and Efficiency without Convergence

We study the aggregate welfare and individual regret guarantees of dynamic \emph{pacing algorithms} in the context of repeated auctions with budgets. Such algorithms are commonly used as bidding agents in Internet advertising platforms. We show that when agents simultaneously apply a natural form of gradient-based pacing, the liquid welfare obtained over the course of the learning dynamics is at least half the optimal expected liquid welfare obtainable by any allocation rule. Crucially, this result holds \emph{without requiring convergence of the dynamics}, allowing us to circumvent known complexity-theoretic obstacles of finding equilibria. This result is also robust to the correlation structure between agent valuations and holds for any \emph{core auction}, a broad class of auctions that includes first-price, second-price, and generalized second-price auctions. For individual guarantees, we further show such pacing algorithms enjoy \emph{dynamic regret} bounds for individual value maximization, with respect to the sequence of budget-pacing bids, for any auction satisfying a monotone bang-for-buck property.
Random graph embeddings with general edge potentials

In this paper, we study random embeddings of polymer networks distributed according to any potential energy which can be expressed in terms of distances between pairs of monomers. This includes freely jointed chains, steric effects, Lennard-Jones potentials, bending energies, and other physically realistic models. A conﬁguration of n monomers in R d can be written as a collection of d coordinate vectors, each in R n . Our ﬁrst main result is that entries from different coordinate vectors are uncorrelated, even when they are different coordinates of the same monomer. We predict that this property holds in realistic simulations and in actual polymer conﬁgurations (in the absence of an external ﬁeld). Our second main contribution is a theorem explaining when and how a probability distribution on embeddings of a complicated graph may be pushed forward to a distribution on embeddings of a simpler graph to aid in computations. This construction is based on the idea of chain maps in homology theory. We use it to give a new formula for edge covariances in phantom network theory and to compute some expectations for a freely-jointed network.
It Isn't Sh!tposting, It's My CAT Posting

In this paper, we describe a novel architecture which can generate hilarious captions for a given input image. The architecture is split into two halves, i.e. image captioning and hilarious text conversion. The architecture starts with a pretrained CNN model, VGG16 in this implementa-tion, and applies attention LSTM on it to generate normal caption. These normal captions then are fed forward to our hilarious text conversion transformer which converts this text into something hilarious while maintaining the context of the input image. The architecture can also be split into two halves and only the seq2seq transformer can be used to generate hilarious caption by inputting a sentence.This paper aims to help everyday user to be more lazy and hilarious at the same time by generating captions using CATNet.
Bond Percolation in Small-World Graphs with Power-Law Distribution

\emph{Full-bond percolation} with parameter $p$ is the process in which, given a graph, for every edge independently, we delete the edge with probability $1-p$. Bond percolation is motivated by problems in mathematical physics and it is studied in parallel computing and network science to understand the resilience of distributed systems to random link failure and the spread of information in networks through unreliable links. Full-bond percolation is also equivalent to the \emph{Reed-Frost process}, a network version of \emph{SIR} epidemic spreading, in which the graph represents contacts among people and $p$ corresponds to the probability that a contact between an infected person and a susceptible one causes a transmission of the infection. We consider \emph{one-dimensional power-law small-world graphs} with parameter $\alpha$ obtained as the union of a cycle with additional long-range random edges: each pair of nodes $(u,v)$ at distance $L$ on the cycle is connected by a long-range edge $(u,v)$, with probability proportional to $1/L^\alpha$. Our analysis determines three phases for the percolation subgraph $G_p$ of the small-world graph, depending on the value of $\alpha$. 1) If $\alpha<1$, there is a $p<1$ such that, with high probability, there are $\Omega(n)$ nodes that are reachable in $G_p$ from one another in $O(\log n)$ hops; 2) If $1<\alpha<2$, there is a $p<1$ such that, with high probability, there are $\Omega(n)$ nodes that are reachable in $G_p$ from one another in $\log^{O(1)}(n)$ hops; 3) If $\alpha>2$, for every $p<1$, with high probability all connected components of $G_p$ have size $O(\log n)$. The setting of full-bond percolation in finite graphs studied in this paper, which is the one that corresponds to the network SIR model of epidemic spreading, had not been analyzed before.
3D Single Source Localization Based on Euclidean Distance Matrices

A popular approach for 3D source localization using multiple microphones is the steered-response power method, where the source position is directly estimated by maximizing a function of three continuous position variables. Instead of directly estimating the source position, in this paper we propose an indirect, distance-based method for 3D source localization. Based on properties of Euclidean distance matrices (EDMs), we reformulate the 3D source localization problem as the minimization of a cost function of a single variable, namely the distance between the source and the reference microphone. Using the known microphone geometry and estimated time-differences of arrival (TDOAs) between the microphones, we show how the 3D source position can be computed based on this variable. In addition, instead of using a single TDOA estimate per microphone pair, we propose an extension that enables to select the most appropriate estimate from a set of candidate TDOA estimates, which is especially relevant in reverberant environments with strong early reflections. Experimental results for different source and microphone constellations show that the proposed EDM-based method consistently outperforms the steered-response power method, especially when the source is close to the microphones.
Intuitive & Efficient Human-robot Collaboration via Real-time Approximate Bayesian Inference

The combination of collaborative robots and end-to-end AI, promises flexible automation of human tasks in factories and warehouses. However, such promise seems a few breakthroughs away. In the meantime, humans and cobots will collaborate helping each other. For these collaborations to be effective and safe, robots need to model, predict and exploit human's intents for responsive decision making processes. Approximate Bayesian Computation (ABC) is approach to perform probabilistic predictions upon uncertain quantities. ABC includes priors conveniently, leverages sampling algorithms for inference and is flexible to benefit from complex models, e.g. via simulators. However, ABC is known to be computationally too intensive to run at interactive frame rates required for effective human-robot collaboration tasks. In this paper, we formulate human intent prediction as an ABC problem and describe two key performance innovations which allow computations at interactive rates. Our real-world experiments with a collaborative robot set-up, demonstrate the viability of our proposed approach. Experimental evaluations convey the advantages and value of human intent prediction for packing cooperative tasks. Qualitative results show how anticipating human's intent improves human-robot collaboration without compromising safety. Quantitative task fluency metrics confirm the qualitative claims.
The Interface Usage Skills Test: An Open Source Tool for Quantitative Evaluation in Real Time for Clinicians and Researchers

Assistive machines endow people with limited mobility the opportunity to live more independently. However, operating these machines poses risks to the safety of the human operator as well as the surrounding environment. Thus, proper user training is an essential step towards independent control and use of functionally assistive machines. The human operator can use a variety of control interfaces to issue control signals to the device, depending on the residual mobility and level of injury of the human operator. Proficiency in operating the interface of choice precedes the skill in operating the assistive machine. In this systems paper, we present an open source tool for automatically and objectively quantifying user skill in operating various interface devices.
Evaluation of Transfer Learning for Polish with a Text-to-Text Model

We introduce a new benchmark for assessing the quality of text-to-text models for Polish. The benchmark consists of diverse tasks and datasets: KLEJ benchmark adapted for text-to-text, en-pl translation, summarization, and question answering. In particular, since summarization and question answering lack benchmark datasets for the Polish language, we describe in detail their construction and make them publicly available. Additionally, we present plT5 - a general-purpose text-to-text model for Polish that can be fine-tuned on various Natural Language Processing (NLP) tasks with a single training objective. Unsupervised denoising pre-training is performed efficiently by initializing the model weights with a multi-lingual T5 (mT5) counterpart. We evaluate the performance of plT5, mT5, Polish BART (plBART), and Polish GPT-2 (papuGaPT2). The plT5 scores top on all of these tasks except summarization, where plBART is best. In general (except summarization), the larger the model, the better the results. The encoder-decoder architectures prove to be better than the decoder-only equivalent.
Twist angle controlled collinear Edelstein effect in van der Waals heterostructures

The generation of spatially homogeneous spin polarization by application of electric current is a fundamental manifestation of symmetry-breaking spin--orbit coupling (SOC) in solid-state systems, which underpins a wide range of spintronic applications. Here, we show theoretically that twisted van der Waals heterostructures with proximity-induced SOC are candidates par excellence to realize exotic spin-charge transport phenomena due to their highly tunable momentum-space spin textures. Specifically, we predict that graphene/group-VI dichalcogenide bilayers support room temperature spin--current responses that can be manipulated via twist-angle control. For critical twist angles, the non-equilibrium spin density is pinned parallel to the applied current. This effect is robust against twist-angle disorder, with graphene/$\text{WSe}_{2}$ possessing a critical angle (purely collinear response) of $\theta_{c} \simeq 14^{\circ}$. A simple electrical detection scheme to isolate the collinear Edelstein effect is proposed.