Miracle-dz/UniFAR-FTUs huggingface dataset:
Encodability Criteria for Quantum Based Systems (Technical Report)

This technical report contains the proofs to the lemmata and theorems of [15] as well as some additional material. The main contributions of [15] are the analysis of the applicability of several quality criteria for encodings within a quantum based setting and a discussion on new, quantum speciﬁc criteria. Therefore, an encoding from one quantum based process calculi into another is presented and the quality criteria are applied to it. The separation result proves the absence of an encoding the other way around.
Single-grasp deformable object discrimination: the effect of gripper morphology, sensing modalities, and action parameters

In haptic object discrimination, the effect of gripper embodiment, action parameters, and sensory channels has not been systematically studied. We used two anthropomorphic hands and two 2-finger grippers to grasp two sets of deformable objects. On the object classification task, we found: (i) among classifiers, SVM on sensory features and LSTM on raw time series performed best across all grippers; (ii) faster compression speeds degraded performance; (iii) generalization to different grasping configurations was limited; transfer to different compression speeds worked well for the Barrett Hand only. Visualization of the feature spaces using PCA showed that the gripper morphology and the action parameters were the main source of variance, rendering generalization across embodiment or grasp configurations very hard. On the highly challenging dataset consisting of polyurethane foams alone, only the Barrett Hand achieved excellent performance. Tactile sensors can thus provide a key advantage even if recognition is based on stiffness rather than shape. The dataset with 24000 measurements is publicly available.
Edge observables of the Maxwell-Chern-Simons theory

: We analyze the Lagrangian and Hamiltonian formulations of the Maxwell-Chern-Simons theory deﬁned on a manifold with boundary for two diﬀerent sets of boundary equations derived from a variational principle. We pay special attention to the identiﬁcation of the inﬁnite chains of boundary constraints and their resolution. We identify edge observables and their algebra (which corresponds to the well-known U ( 1 ) Kac-Moody algebra). Without performing any gauge ﬁxing, and using the Hodge-Morrey theorem, we solve the Hamilton equations whenever possible. In order to give explicit solutions, we consider the particular case in which the ﬁelds are deﬁned on a 2 -disk. Finally, we study the Fock quantization of the system and discuss the quantum edge observables and states.
Optimizing Generalized Gini Indices for Fairness in Rankings

There is growing interest in designing recommender systems that aim at being fair towards item producers or their least satisfied users. Inspired by the domain of inequality measurement in economics, this paper explores the use of generalized Gini welfare functions (GGFs) as a means to specify the normative criterion that recommender systems should optimize for. GGFs weight individuals depending on their ranks in the population, giving more weight to worse-off individuals to promote equality. Depending on these weights, GGFs minimize the Gini index of item exposure to promote equality between items, or focus on the performance on specific quantiles of least satisfied users. GGFs for ranking are challenging to optimize because they are non-differentiable. We resolve this challenge by leveraging tools from non-smooth optimization and projection operators used in differentiable sorting. We present experiments using real datasets with up to 15k users and items, which show that our approach obtains better trade-offs than the baselines on a variety of recommendation tasks and fairness criteria.
Determination of the Complex-Valued Elastic Moduli of Polymers by Electrical-Impedance Spectroscopy for Ultrasound Applications

A method is presented for the determination of complex-valued compression and shear elastic moduli of polymers for ultrasound applications. The resulting values, which are scarcely reported in the literature, are found with uncertainties typically around 1% (real part) and 6% (imaginary part). The method involves a setup consisting of a cm-radius, mm-thick polymer ring glued concentrically to a disk-shaped piezoelectric transducer. The ultrasound electrical impedance spectrum of the transducer is computed numerically and ﬁtted to measured values as an inverse problem in a wide frequency range, typically from 500 Hz to 5 MHz, both on and oﬀ resonance. The method was validated experimentally by ultrasonic through-transmission around 1.9 MHz. The method is low cost, not limited to speciﬁc geometries and crystal symmetries, and, given the developed software, easy to execute. The method has no obvious frequency limitations before severe attenuation sets in above 100 MHz.
Encoding Domain Knowledge in Multi-view Latent Variable Models: A Bayesian Approach with Structured Sparsity

Many real-world systems are described not only by data from a single source but via multiple data views. In genomic medicine, for instance, patients can be characterized by data from different molecular layers. Latent variable models with structured sparsity are a commonly used tool for disentangling variation within and across data views. However, their interpretability is cumbersome since it requires a direct inspection and interpretation of each factor from domain experts. Here, we propose MuVI, a novel multi-view latent variable model based on a modified horseshoe prior for modeling structured sparsity. This facilitates the incorporation of limited and noisy domain knowledge, thereby allowing for an analysis of multi-view data in an inherently explainable manner. We demonstrate that our model (i) outperforms state-of-the-art approaches for modeling structured sparsity in terms of the reconstruction error and the precision/recall, (ii) robustly integrates noisy domain expertise in the form of feature sets, (iii) promotes the identifiability of factors and (iv) infers interpretable and biologically meaningful axes of variation in a real-world multi-view dataset of cancer patients.
AdaTest: Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection

This paper proposes AdaTest, a novel adaptive test pattern generation framework for efficient and reliable Hardware Trojan (HT) detection. HT is a backdoor attack that tampers with the design of victim integrated circuits (ICs). AdaTest improves the existing HT detection techniques in terms of scalability and accuracy of detecting smaller Trojans in the presence of noise and variations. To achieve high trigger coverage, AdaTest leverages Reinforcement Learning (RL) to produce a diverse set of test inputs. Particularly, we progressively generate test vectors with high ‘reward’ values in an iterative manner. In each iteration, the test set is evaluated and adaptively expanded as needed. Furthermore, AdaTest integrates adaptive sampling to prioritize test samples that provide more information for HT detection, thus reducing the number of samples while improving the samples’ quality for faster exploration. We develop AdaTest with a Software/Hardware co-design principle and provide an optimized on-chip architecture solution. AdaTest’s architecture minimizes the hardware overhead in two ways: (i) Deploying circuit emulation on programmable hardware to accelerate reward evaluation of the test input; (ii) Pipelining each computation stage in AdaTest by automatically constructing auxiliary circuit for test input generation, reward evaluation, and adaptive sampling. We evaluate AdaTest’s performance on various HT benchmarks and compare it with two prior works that use logic testing for HT detection. Experimental results show that AdaTest engenders up to two orders of test generation speedup and two orders of test set size reduction compared to the prior works while achieving the same level or higher Trojan detection rate.
Finding Trolls Under Bridges: Preliminary Work on a Motif Detector

Motifs are distinctive recurring elements found in folklore that have significance as communicative devices in news, literature, press releases, and propaganda. Motifs concisely imply a large constellation of culturally-relevant information, and their broad usage suggests their cognitive importance as touchstones of cultural knowledge, making their detection a worthy step toward culturally-aware natural language processing tasks. Until now, folklorists and others interested in motifs have only extracted motifs from narratives manually. We present a preliminary report on the development of a system for automatically detecting motifs. We briefly describe an annotation effort to produce data for training motif detection, which is on-going. We describe our in-progress architecture in detail, which aims to capture, in part, how people determine whether or not a motif candidate is being used in a motific way. This description includes a test of an off-the-shelf metaphor detector as a feature for motif detection, which achieves a F1 of 0.35 on motifs and a macro-average F1 of 0.21 across four categories which we assign to motif candidates.
Rank One Approximation as a Strategy for Wordle

This paper presents a mathematical method of playing the puzzle game Wordle. In Wordle, the player has six tries to guess a secret word. After each guess the player is told how their guess compares to the secret word. With the available information the player makes their next guess. This paper proposes combining a rank one approximation and latent semantic indexing to a matrix representing the list of all possible solutions. Rank one approximation finds the dominant eigenvector of a matrix of words, and latent semantic indexing reveals which word is closest to the dominant eigenvector. The word whose column vector is closest to the dominant eigenvector is chosen as the next guess. With this method the most representative word of the set of all possible solutions is selected. This paper describes how a word can be converted to a vector and the theory behind a rank one approximation and latent semantic indexing. This paper presents results demonstrating that with an initial guess of"SLATE"the method solves the puzzle in 4.04 guesses on average, with a success rate of 98.7%
Flexible Multiple-Objective Reinforcement Learning for Chip Placement

Recently, successful applications of reinforcement learning to chip placement have emerged. Pretrained models are necessary to improve efficiency and effectiveness. Currently, the weights of objective metrics (e.g., wirelength, congestion, and timing) are fixed during pretraining. However, fixed-weighed models cannot generate the diversity of placements required for engineers to accommodate changing requirements as they arise. This paper proposes flexible multiple-objective reinforcement learning (MORL) to support objective functions with inference-time variable weights using just a single pretrained model. Our macro placement results show that MORL can generate the Pareto frontier of multiple objectives effectively.
GASKAP-HI Pilot Survey Science III: An unbiased view of cold gas in the Small Magellanic Cloud

Abstract We present the first unbiased survey of neutral hydrogen absorption in the Small Magellanic Cloud. The survey utilises pilot neutral hydrogen observations with the Australian Square Kilometre Array Pathfinder telescope as part of the Galactic Australian Square Kilometre Array Pathfinder neutral hydrogen project whose dataset has been processed with the Galactic Australian Square Kilometre Array Pathfinder-HI absorption pipeline, also described here. This dataset provides absorption spectra towards 229 continuum sources, a 275% increase in the number of continuum sources previously published in the Small Magellanic Cloud region, as well as an improvement in the quality of absorption spectra over previous surveys of the Small Magellanic Cloud. Our unbiased view, combined with the closely matched beam size between emission and absorption, reveals a lower cold gas faction (11%) than the 2019 ATCA survey of the Small Magellanic Cloud and is more representative of the Small Magellanic Cloud as a whole. We also find that the optical depth varies greatly between the Small Magellanic Cloud’s bar and wing regions. In the bar we find that the optical depth is generally low (correction factor to the optically thin column density assumption of $\mathcal{R}_{\mathrm{HI}} \sim 1.04$ ) but increases linearly with column density. In the wing however, there is a wide scatter in optical depth despite a tighter range of column densities.
Measuring Optical Extinction toward Young Stellar Objects Using Diffuse Interstellar Bands

Line-of-sight extinction estimates of well-studied young T Tauri and Herbig Ae/Be stars are based on many different measurements and analysis methods. This has resulted in wide scatter among the published A V values for the same star. In this work, we discuss the challenges in measuring extinction to actively accreting and especially outbursting young stellar objects (YSOs). We then explore a method not previously applied to young stars utilizing diffuse interstellar bands (DIBs). In early-type stars, narrow correlations exist between the DIB equivalent widths and the column density of interstellar material, and therefore the line-of-sight extinction. Here, we measure the equivalent widths of the 5780 and 6614 Å DIB features in a sample of actively accreting YSOs, and apply a DIB-reddening calibration to estimate reddening and subsequently extinction. Our calibration is newly derived from a composite of available literature data and fully accounts for the scatter in these measurements. We also compare the DIBs-inferred optical line-of-sight extinction values with previous extinction estimates for our sample stars.
Skyhook: Towards an Arrow-Native Storage System

With the ever-increasing dataset sizes, several file formats such as Parquet, ORC, and Avro have been developed to store data efficiently, save the network, and interconnect bandwidth at the price of additional CPU utilization. However, with the advent of networks supporting 25–100 Gb/s and storage devices delivering 1,000,000 reqs/sec, the CPU has become the bottleneck trying to keep up feeding data in and out of these fast devices. The result is that data access libraries executed on single clients are often CPU-bound and cannot utilize the scale-out benefits of distributed storage systems. One attractive solution to this problem is to offload data-reducing processing and filtering tasks to the storage layer. However, modifying legacy storage systems to support compute offloading is often tedious and requires an extensive understanding of the system internals. Previous approaches re-implemented functionality of data processing frameworks and access libraries for a particular storage system, a duplication of effort that might have to be repeated for different storage systems. This paper introduces a new design paradigm that allows ex-tending programmable object storage systems to embed existing, widely used data processing frameworks and access libraries into the storage layer with no modifications. In this approach, data processing frameworks and access libraries can evolve independently from storage systems while leveraging distributed storage systems' scale-out and availability properties. We present Skyhook, an example implementation of our design paradigm using Ceph, Apache Arrow, and Parquet. We provide a brief performance evaluation of Skyhook and discuss key results.
Social contagion and asset prices: Reddit's self-organised bull runs

Can unstructured text data from social media help explain the drivers of large asset price fluctuations? This paper investigates how social forces affect asset prices, by using machine learning tools to extract beliefs and positions of `hype' traders active on Reddit's WallStreetBets (WSB) forum. Our stylized model shows that peer effects help explain return predictability and reversals, as well as bubble dynamics. We empirically document that sentiments expressed by WSB users about assets' future performances (bullish or bearish) are in part due to sentiments of their peers and past asset returns. The paper directly estimates the effect of WSB activity on asset prices. We document: that retail trader demand follows WSB discussions through using Trade and Quote (TAQ) data, the predictability of prices from retail trader discourse, the amplified market impact of idiosyncratic investor sentiment from viral content online, and the greater exposure of hype investors to bubbles in the markets.
Weakly But Deeply Supervised Occlusion-Reasoned Parametric Road Layouts

We propose an end-to-end network that takes a single perspective RGB image of a complex road scene as input, to produce occlusion-reasoned layouts in perspective space as well as a parametric bird's-eye-view (BEV) space. In contrast to prior works that require dense supervision such as semantic labels in perspective view, our method only requires human annotations for parametric attributes that are cheaper and less ambiguous to obtain. To solve this challenging task, our design is comprised of modules that incorporate inductive biases to learn occlusion-reasoning, geometric transformation and semantic abstraction, where each module may be supervised by appropriately transforming the parametric annotations. We demonstrate how our design choices and proposed deep supervision help achieve meaningful representations and accurate predictions. We validate our approach on two public datasets, KITTI and NuScenes, to achieve state-of-the-art results with considerably less human supervision.
HuBERT-EE: Early Exiting HuBERT for Efficient Speech Recognition

Pre-training with self-supervised models, such as Hidden-unit BERT (HuBERT) and wav2vec 2.0, has brought signiﬁcant improvements in automatic speech recognition (ASR). However, these models usually require an expensive computational cost to achieve outstanding performance, slowing down the inference speed. To improve the model efﬁciency, we propose an early exit scheme for ASR, namely HuBERT-EE, that allows the model to stop the inference dynamically. In HuBERT-EE, multiple early exit branches are added at the intermediate layers, and each branch is used to decide whether a prediction can be exited early. Experimental results on the LibriSpeech dataset show that HuBERT-EE can accelerate the inference of a large-scale HuBERT model while simultaneously balancing the trade-off between the word error rate (WER) performance and the latency.
Investigating Violation Behavior at Intersections using Intelligent Transportation Systems: A Feasibility Analysis on Vehicle/Bicycle-to-Infrastructure Communications as a Potential Countermeasure

The focus of this dissertation is on safety improvement at intersections and presenting how Vehicle/Bicycle-to-Infrastructure Communications can be a potential countermeasure for crashes resulting from drivers’ and cyclists’ violations at intersections. The characteristics (e.g., acceleration capabilities, etc.) of transportation modes affect the violation behavior. Therefore, the first building block is to identify the users’ transportation mode. Consequently, having the mode information, the second building block is to predict whether or not the user is going to violate. This step focuses on two different modes (i.e., driver violation prediction and cyclist violation prediction). Warnings can then be issued for users in potential danger to react or for the infrastructure and vehicles so they can take appropriate actions to avoid or mitigate crashes. A smartphone application was developed to collect sensor data used to conduct the transportation mode recognition task. Driver violation prediction task at signalized intersections was conducted using observational and simulator data. Also, a naturalistic cycling experiment was designed for cyclist violation prediction task. Subsequently, cyclist violation behavior was investigated at both signalized and stop-controlled intersections. To build the prediction models in all the aforementioned tasks, various Artificial Intelligence techniques were adopted. K-fold CrossValidation as well as Out-of-Bag error was used for model selection and validation. Transportation mode recognition models contributed to high classification accuracies (e.g., up to 98%). Thus, data obtained from the smartphone sensors were found to provide important information to distinguish between transportation modes. Driver violation (i.e., red light running) prediction models were resulted in high accuracies (i.e., up to 99.9%). Time to intersection (TTI), distance to intersection (DTI), the required deceleration parameter (RDP), and velocity at the onset of a yellow light were among the most important factors in violation prediction. Based on logistic regression analysis, movement type and presence of other users were found as significant factors affecting the probability of red light violations by cyclists at signalized intersections. Also, presence of other road users and age were the significant factors affecting violations at stop-controlled intersections. In case of stop-controlled intersections, violation prediction models resulted in error rates of 0 to 10 percent depending on how far from the intersection the prediction task is conducted.
Sub-Phases in the Superconducting State of CeIrIn$_5$ Revealed by Low Temperature $c$-axis Heat Transport

Low-temperature (down to ∼ 50 mK) thermal conductivity measurements with the heat ﬂow direction along the interplane tetragonal c axis, κ c , were used to study the superconducting state of heavy fermion CeIrIn 5 . Measurements were performed in the magnetic ﬁelds both parallel to the heat ﬂow direction H (cid:3) c , and transverse to it H (cid:3) a . Interplane heat conductivity in H (cid:3) c conﬁguration shows negligible initial increase with magnetic ﬁeld and a rapid rise on approaching H c 2 from below, similar to the expectations for the superconducting gap without line nodes. This observation is in stark contrast to monotonic increase found in the previous in-plane heat transport measurements. In the conﬁguration with the magnetic ﬁeld breaking the tetragonal symmetry of the lattice H (cid:3) a , κ c reveals nonmonotonic evolution with temperature and magnetic ﬁeld suggesting subphase boundary in the superconducting state. The characteristic temperature T kink ∼ 0.07 K of the subboundary is well within the domain of bulk superconductivity T c ∼ 0.4 K and H c 2 ∼ 1.0 T. These results are consistent with a superconducting gap with an equatorial line node and polar point nodes, a gap symmetry of the D 4 h point group, for which magnetic ﬁeld along the tetragonal plane breaks the degeneracy of the multicomponent order parameter and induces a phase transition with nodal topology change.