Miracle-dz/UniFAR-FTUs huggingface dataset:
Automating In-Network Machine Learning

Using programmable network devices to aid in-network machine learning has been the focus of significant research. However, most of the research was of a limited scope, providing a proof of concept or describing a closed-source algorithm. To date, no general solution has been provided for mapping machine learning algorithms to programmable network devices. In this paper, we present Planter, an open-source, modular framework for mapping trained machine learning models to programmable devices. Planter supports a wide range of machine learning models, multiple targets and can be easily extended. The evaluation of Planter compares different mapping approaches, and demonstrates the feasibility, performance, and resource efficiency for applications such as anomaly detection, financial transactions, and quality of experience. The results show that Planter-based in-network machine learning algorithms can run at line rate, have a negligible effect on latency, coexist with standard switching functionality, and have no or minor accuracy trade-offs.
Topological entanglement stabilization in superconducting quantum circuits

Topological properties of quantum systems are one of the most intriguing emerging phenomena in condensed matter physics. A crucial property of topological systems is the symmetry-protected robustness towards local noise. Experiments have demonstrated topological phases of matter in various quantum systems. However, using the robustness of such modes to stabilize quantum correlations is still a highly sought-after milestone. In this work, we put forward a concept of using topological modes to stabilize fully entangled quantum states, and we demonstrate the stability of the entanglement with respect to parameter fluctuations. Specifically, we see that entanglement remains stable against parameter fluctuations in the topologically non-trivial regime, while entanglement in the trivial regime is highly susceptible. We supplement our scheme with an experimentally realistic and detailed proposal based on coupled superconducting resonators and qubits. Our proposal sets a novel approach for generating long-lived quantum modes with robustness towards disorder in the circuit parameters via a bottom-up experimental approach relying on easy-to-engineer building blocks.
Designing for Caregiving: Integrating Robotic Assistance in Senior Living Communities

Robots hold significant promise to assist with providing care to an aging population and to help overcome increasing caregiver demands. Although a large body of research has explored robotic assistance for individuals with disabilities and age-related challenges, this past work focuses primarily on building robotic capabilities for assistance and has not yet fully considered how these capabilities could be used by professional caregivers. To better understand the workflows and practices of caregivers who support aging populations and to determine how robotic assistance can be integrated into their work, we conducted a field study using ethnographic and co-design methods in a senior living community. From our results, we created a set of design opportunities for robotic assistance, which we organized into three different parts: supporting caregiver workflows, adapting to resident abilities, and providing feedback to all stakeholders of the interaction.
Dialog Inpainting: Turning Documents into Dialogs

Many important questions (e.g."How to eat healthier? ") require conversation to establish context and explore in depth. However, conversational question answering (ConvQA) systems have long been stymied by scarce training data that is expensive to collect. To address this problem, we propose a new technique for synthetically generating diverse and high-quality dialog data: dialog inpainting. Our approach takes the text of any document and transforms it into a two-person dialog between the writer and an imagined reader: we treat sentences from the article as utterances spoken by the writer, and then use a dialog inpainter to predict what the imagined reader asked or said in between each of the writer's utterances. By applying this approach to passages from Wikipedia and the web, we produce WikiDialog and WebDialog, two datasets totalling 19 million diverse information-seeking dialogs -- 1,000x larger than the largest existing ConvQA dataset. Furthermore, human raters judge the answer adequacy and conversationality of WikiDialog to be as good or better than existing manually-collected datasets. Using our inpainted data to pre-train ConvQA retrieval systems, we significantly advance state-of-the-art across three benchmarks (QReCC, OR-QuAC, TREC CAsT) yielding up to 40% relative gains on standard evaluation metrics.
A Tutorial on Structural Optimization

Structural optimization is a useful and interesting tool. Unfortunately, it can be hard for new researchers to get started on the topic because existing tutorials assume the reader has substantial domain knowledge. They obscure the fact that structural optimization is really quite simple, elegant, and easy to implement. With that in mind, let's write our own structural optimization code, from scratch, in 180 lines.
Influence of Magnetic Fields on the Gas Rotation in the Galaxy NGC 6946

Magnetic fields can play an important role in the energy balance and formation of gas structures in galaxies. However, their dynamical effect on the rotation curve of galaxies is immensely unexplored. We investigate the dynamical effect of the known magnetic arms of NGC 6946 on its circular gas rotation traced in H i, considering two dark-matter mass-density models, ISO, and the universal NFW profile. We used a three-dimensional model for the magnetic field structure to fit the modeled rotation curve to the observed data via a χ 2 minimization method. The shape of the H i gas rotation curve is reproduced better including the effect of the magnetic field, especially in the outer part, where the dynamical effect of the magnetic field could become important. The typical amplitude of the regular magnetic field contribution in the rotation curve is about 6–14 km s−1 in the outer gaseous disk of the galaxy NGC 6946. The contribution ratio of the regular magnetic field to the observed circular velocity and to dark matter increases with the galactocentric radius. Its ratio to the observed rotational velocity is about 5% and, to dark matter, is about 10% in the outer regions of the galaxy NGC 6946. Therefore, the large-scale magnetic fields cannot be completely ignored in the large-scale dynamics of spiral galaxies, especially in the outer parts of galaxies.
A New Soft-switching Technique for CCM Synchronous Buck Converter

Abstract A new soft-switching technique for synchronous buck converter is proposed. It utilizes an auxiliary winding of the filter inductor and an auxiliary capacitor to provide zero-voltage-switching (ZVS) of the power switches and maintain a continuous filter inductor current. The reverse recovery problem of the anti-parallel body diode of the synchronous switch is solved. Moreover, it provides a simple structure and shows a higher efficiency compared to the conventional synchronous buck converter. Theoretical analysis of the proposed buck converter and the experimental results obtained on 230 W prototype are discussed
Mathematical Characterization of Private and Public Immune Repertoire Sequences

Diverse T and B cell repertoires play an important role in mounting effective immune responses against a wide range of pathogens and malignant cells. The number of unique T and B cell clones is characterized by T and B cell receptors (TCRs and BCRs), respectively. Although receptor sequences are generated probabilistically by recombination processes, clinical studies found a high degree of sharing of TCRs and BCRs among different individuals. In this work, we formulate a mathematical and statistical framework to quantify receptor distributions. We define information-theoretic metrics for comparing the frequency of sampled sequences observed across different individuals. Using synthetic and empirical TCR amino acid sequence data, we perform simulations to compare theoretical predictions of this clonal commonality across individuals with corresponding observations. Thus, we quantify the concept of “publicness” or “privateness” of T cell and B cell clones. Our methods can also be used to study the effect of different sampling protocols on the expected commonality of clones and on the confidence levels of this overlap. We also quantify the information loss associated with grouping together certain receptor sequences, as is done in spectratyping.
An approach to translating Haskell programs to Agda and reasoning about them

We are using the Agda programming language and proof assistant to formally verify the correctness of a Byzantine Fault Tolerant consensus implementation based on HotStuff / LibraBFT . The Agda implementation is a translation of our Haskell implementation based on LibraBFT . This paper focuses on one aspect of this work. We have developed a library that enables the translated Agda implementation to closely mirror the Haskell code on which it is based. This makes it easier and more eﬃcient to review the translation for accu-racy, and to maintain the translated Agda code when the Haskell code changes, thereby reducing the risk of translation errors. We also explain how we capture the semantics of the syntactic features provided by our library, thus enabling formal reasoning about programs that use them; details of how we reason about the resulting Agda implementation will be presented in a future paper. The library that we present is independent of our particular veriﬁcation project, and is available, open-source, for others to use and extend.
Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation.

Stemming from the limited availability of datasets and textual resources for low-resource languages such as isiZulu, there is a significant need to be able to harness knowledge from pre-trained models to improve low resource machine translation. Moreover, a lack of techniques to handle the complexities of morphologically rich languages has compounded the unequal development of translation models, with many widely spoken African languages being left behind. This study explores the potential benefits of transfer learning in an English-isiZulu translation framework. The results indicate the value of transfer learning from closely related languages to enhance the performance of low-resource translation models, thus providing a key strategy for low-resource translation going forward. We gathered results from 8 different language corpora, including one multi-lingual corpus, and saw that isiXhosa-isiZulu outperformed all languages, with a BLEU score of 8.56 on the test set which was better from the multi-lingual corpora pre-trained model by 2.73. We also derived a new coefficient, Nasir’s Geographical Distance Coefficient (NGDC) which provides an easy selection of languages for the pre-trained models. NGDC also indicated that isiXhosa should be selected as the language for the pre-trained model.
Automatic Complexity Analysis of Integer Programs via Triangular Weakly Non-Linear Loops

There exist several results on deciding termination and computing runtime bounds for triangular weakly non-linear loops (twn-loops). We show how to use results on such subclasses of programs where complexity bounds are computable within incomplete approaches for complexity analysis of full integer programs. To this end, we present a novel modular approach which computes local runtime bounds for subprograms which can be transformed into twn-loops. These local runtime bounds are then lifted to global runtime bounds for the whole program. The power of our approach is shown by our implementation in the tool KoAT which analyzes complexity of programs where all other state-of-the-art tools fail.
GSN: A Graph Neural Network Inspired by Spring Networks

The design of Graph Neural Networks (GNNs) that operate on both homophilous and heterophilous graphs has received research attention in recent years. Existing heterophilous GNNs, particularly those designed in the spatial domain, lack a convincing theoretical or physical motivation. Inspired by an old-fashioned spring network model, we propose the Graph Spring Network (GSN), a universal GNN model that works for homophilous and heterophilous graphs. We show that the GSN framework can interpret many GNN models from the perspective of potential energy minimization of a spring network with respect to various metrics, which entrusts strong physical motivations to these models. We also conduct experiments to demonstrate the performance of our GSN model on real-world datasets.
Phy-Q: A Testbed for Physical Reasoning

Humans are well-versed in reasoning about the behaviors of physical objects and choosing actions accordingly to accomplish tasks, while it remains a major challenge for AI. To facilitate research addressing this problem, we propose a new testbed that requires an agent to reason about physical scenarios and take an action appropriately. Inspired by the physical knowledge acquired in infancy and the capabilities required for robots to operate in real-world environments, we identify 15 essential physical scenarios. For each scenario, we create a wide variety of distinct task templates, and we ensure all the task templates within the same scenario can be solved by using one speciﬁc strategic physical rule. By having such a design, we evaluate two distinct levels of generalization, namely the local generalization and the broad generalization . We conduct an extensive evaluation with human players, learning agents with varying input types and architectures, and heuristic agents with different strategies. Inspired by how human IQ is calculated, we deﬁne the physical reasoning quotient (Phy-Q score) that reﬂects the physical reasoning intelligence of an agent. Our evaluation shows that 1) all agents are far below human performance, and 2) learning agents, even with good local generalization ability, struggle to learn the underlying physical reasoning rules and fail to generalize broadly. We encourage the development of intelligent agents that can reach the human level Phy-Q score. Website: https: //github.com/phy-q/benchmark
Erasing Labor with Labor: Dark Patterns and Lockstep Behaviors on Google Play

Google Play’s policy forbids the use of incentivized installs, ratings, and reviews to manipulate the placement of apps. However, there still exist apps that incentivize installs for other apps on the platform. To understand how install-incentivizing apps affect users, we examine their ecosystem through a socio-technical lens and perform a mixed-methods analysis of their reviews and permissions. Our dataset contains 319K reviews collected daily over five months from 60 such apps that cumulatively account for over 160.5M installs. We perform qualitative analysis of reviews to reveal various types of dark patterns that developers incorporate in install-incentivizing apps, highlighting their normative concerns at both user and platform levels. Permissions requested by these apps validate our discovery of dark patterns, with over 92% apps accessing sensitive user information. We find evidence of fraudulent reviews on install-incentivizing apps, following which we model them as an edge stream in a dynamic bipartite graph of apps and reviewers. Our proposed reconfiguration of a state-of-the-art microcluster anomaly detection algorithm yields promising preliminary results in detecting this fraud. We discover highly significant lockstep behaviors exhibited by reviews that aim to boost the overall rating of an install-incentivizing app. Upon evaluating the 50 most suspicious clusters of boosting reviews detected by the algorithm, we find (i) near-identical pairs of reviews across 94% (47 clusters), and (ii) over 35% (1,687 of 4,717 reviews) present in the same form near-identical pairs within their cluster. Finally, we conclude with a discussion on how fraud is intertwined with labor and poses a threat to the trust and transparency of Google Play.
Voltage-dependent voltage-acceleration of oxide breakdown for ultra-thin oxides

We report the voltage-dependence of voltage acceleration for ultra-thin oxides from 2.2 V to 5 V over a range of T/sub ox/ values from 1.7 nm to 5.0 nm. This unique behavior manifest itself as a power-law voltage-dependence for time-to-breakdown (T/sub BD/) over a variety of experimental observations. Using the concept of energy-to-breakdown, we explore the possible scenarios such as fractional energy or defect generation probability as a function of voltage to account for the increase in voltage acceleration with decreasing voltages.
Laser-ablative synthesis of nanomaterials for nuclear and radiative medicine applications

Newly emerging nanomaterials promise a major advancement of methods of nuclear and radiative medicine for cancer treatment, as they can be used as carriers of diagnostic or therapeutic radionuclides, contrast agents in nuclear imaging modalities (PET, SPECT) or sensitizers of radiative therapies (X-ray, ion beams, etc.). However, nanotechnology-based approaches have reported a limited success so far due to a lack of suitable functional nanoformulations, which are safe, non-toxic, excretable from the body and have favorable pharmacokinetics for effective accumulation in the tumor. As follows from the results of our on-going research activities, many of the above-stated problems can we solved by the employment of nanomaterials fabricated by clean laser-ablative synthesis. Here, we review our recent data on some promising nanomaterials, prepared by this method, including biodegradable silicon (Si) nanoparticles (NPs), 152Sm-enriched samarium oxide NPs, and elemental bismuth (Bi) NPs, which can be used either as carriers/agents in radionuclide therapy, or sensitizers in radiative diagnostics or therapy. Advantages of proposed approach include exceptional purity and flexibility in synthesizing of NPs of required physico-chemical parameters (controlled size, shape, composition, and surface conditioning of NPs). Advances in laser-ablative fabrication of novel nanomaterials open up avenues for future implementations of nuclear and radiative medicine approaches for safe and efficient theranostics of tumors and metastasis.
Robust Position Control of One DOF Mechanical Systems Using Dual PIOs Without Velocity Measurement

This paper presents a robust position controller for a one degree-of-freedom (DOF) mechanical system using only position measurement. In order to alleviate the performance degradation owing to various uncertainties, a two-stage design method is studied by employing a proportional integral observer (PIO). In the first stage, a baseline backstepping controller is designed for a nominal system without accounting for uncertainties. The PIO is developed for estimating both the velocity information for the backstepping controller and an equivalent input disturbance for a feedforward compensation using the estimated uncertainty. It is shown that the estimation errors with the proposed PIO can be made arbitrarily small in a finite time. If the system suffers from undesirable actuator nonlinearities, however, it might be necessary to estimate the velocity and the disturbance with different rates of convergence. The proposed method combines the predesigned backstepping controller and dual PIOs to reduce mechanical vibrations as well as steady-state errors. The performance of the proposed method is tested through comparative computer simulations and experiments using a laboratory prototype.
Efficient Krylov‐based 3D FVTD schemes with adaptive domain decomposition for graphene and nanostructured EMC components

The rigorous design of arbitrarily shaped graphene and nanocomposite structures in realistic electromagnetic compatibility applications is presented in this paper by means of a combined finite‐volume time‐domain methodology. The new 3D formulation proposes a set of adjustable‐order derivative approximators in general curvilinear coordinates and an adaptive domain decomposition to attain effective field flux representations of electromagnetic fields in areas with subwavelength attributes or infinitesimally thin wiring. Also, a fully passive reduced order macromodelling scheme, based on the Krylov truncation, is developed for the significant decrease of the state‐space transfer matrix order, particularly when finite graphene sheets are to be analyzed. A key asset is that the necessary basis for the Krylov space is retrieved from a framework of weighted Laguerre functions, which do not require laborious orthogonalizations. Hence, highly accurate and economical discretizations with minimized dispersion errors are obtained. Numerical outcomes, addressing elaborate comparisons with reference data, from various nanostructured electromagnetic compatibility arrangements, certify the benefits of the featured technique, accelerated via graphics processing units and unveil its applicability.
Socially Assistive Robots as Storytellers that Elicit Empathy

Empathy is the ability to share someone else’s feelings or experiences; it influences how people interact and relate. Socially assistive robots (SAR) are a promising means of conveying and eliciting empathy toward facilitating human-robot interaction. This work examines factors that influence the amount of empathy elicited by a SAR storyteller and users’ perceptions of that robot. We conducted an empirical mixed-design study (N=46) using an autonomous SAR storyteller that told three stories, each with a different human or robot target of empathy. The robot storyteller used the first-person narrative voice (1PNV) with half of the participants and the third-person narrative voice (3PNV) with the other half. We found that the SAR storyteller elicited significantly more empathy when the story target of empathy matched the SAR narrator, i.e., was also a robot. Additionally, the 1PNV robot elicited significantly more empathy and was perceived as more human-like, easy to interact with, and trustworthy than the 3PNV robot. Finally, participants who empathized more with the robot displayed facial expressions consistent with the emotional story content. These insights inform the design of SAR storytellers capable of eliciting empathy toward creating compelling and effective human-robot interactions.
WOOFlex: A Wearable Device to Aid Canine Flexibility Exercises

Flexibility and stretching exercises for dogs are used to improve joint motion and alleviate limb pain. However, there is a lack of information in the effectiveness of these canine exercises when performed by dog owners and caregivers. Current solutions for measuring range of motion are not designed to be worn by dogs. In order to evaluate the position of a joint at any given time, the user, the human assisting the dog in the exercises, must hold a digital or analog device while performing the exercise without real-time feedback associated with the dog’s breed or joint exercised. We propose WOOFlex: a wearable that assists in canine flexibility exercises that (1) is attached to the dog’s limbs so as to not interfere with the exercise, (2) includes IMU sensors to measure the joint angle, and a web application to (1) provide real-time visual and audio feedback for assisting the user in not exceeding safe joint angle ranges, and (2) to store exercise session data to monitor the flexibility progress.