Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting

In this paper, we present a novel approach to adapt a sequence-to-sequence Transformer-Transducer ASR system to the keyword spotting (KWS) task. We achieve this by replacing the keyword in the text transcription with a special token and training the system to detect the token in an audio stream. At inference time, we create a decision function inspired by conventional KWS approaches, to make our approach more suitable for the KWS task. Furthermore, we introduce a specific keyword spotting loss by adapting the sequence-discriminative Minimum Bayes-Risk training technique. We find that our approach significantly outperforms ASR based KWS systems. When compared with a conventional keyword spotting system, our proposal has similar performance while bringing the advantages and flexibility of sequence-to-sequence training. Additionally, when combined with the conventional KWS system, our approach can improve the performance at any operation point.
Human Autonomy as a Design Principle for Socially Assistive Robots

High levels of robot autonomy are a common goal, but there is a significant risk that the greater the autonomy of the robot the lesser the autonomy of the human working with the robot. For vulnerable populations like older adults who already have a diminished level of autonomy, this is an even greater concern. We propose that human autonomy needs to be at the center of the design for socially assistive robots. Towards this goal, we define autonomy and then provide architectural requirements for social robots to support the user's autonomy. As an example of a design effort, we describe some of the features of our Assist architecture.
A Subsurface Magma Ocean on Io: Exploring the Steady State of Partially Molten Planetary Bodies

Intense tidal heating within Io produces active volcanism on the surface, and its internal structure has long been a subject of debate. A recent reanalysis of the Galileo magnetometer data suggested the presence of a high-melt-fraction layer with >50 km thickness in the subsurface region of Io. Whether this layer is a “magmatic sponge” with interconnected solid or a rheologically liquid “magma ocean” would alter the distribution of tidal heating and would also influence the interpretation of various observations. To this end, we explore the steady state of a magmatic sponge and estimate the amount of internal heating necessary to sustain such a layer with a high degree of melting. Our results show that the rate of tidal dissipation within Io is insufficient to sustain a partial-melt layer of ϕ > 0.2 for a wide range of parameters, suggesting that such a layer would swiftly separate into two phases. Unless melt and/or solid viscosities are at the higher end of the estimated range, a magmatic sponge would be unstable, and thus a high-melt-fraction layer suggested in Khurana et al. is likely to be a subsurface magma ocean.
Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations

Recent work on explainable NLP has shown that few-shot prompting can enable large pre-trained language models (LLMs) to generate grammatical and factual natural language explanations for data labels. In this work, we study the connection between explainability and sample hardness by investigating the following research question – “Are LLMs and humans equally good at explaining data labels for both easy and hard samples? ” We answer this question by first collecting human-written explanations in the form of generalizable commonsense rules on the task of Winograd Schema Challenge (Winogrande dataset). We compare these explanations with those generated by GPT-3 while varying the hardness of the test samples as well as the in-context samples. We observe that (1) GPT-3 explanations are as grammatical as human explanations regardless of the hardness of the test samples, (2) for easy examples, GPT-3 generates highly supportive explanations but human explanations are more generalizable, and (3) for hard examples, human explanations are significantly better than GPT-3 explanations both in terms of label-supportiveness and generalizability judgements. We also find that hardness of the in-context examples impacts the quality of GPT-3 explanations. Finally, we show that the supportiveness and generalizability aspects of human explanations are also impacted by sample hardness, although by a much smaller margin than models.
Formally Verified Samplers from Probabilistic Programs with Loops and Conditioning

We present Zar: a formally verified compiler pipeline from discrete probabilistic programs with unbounded loops in the conditional probabilistic guarded command language (cpGCL) to proved-correct executable samplers in the random bit model. We exploit the key idea that all discrete probability distributions can be reduced to unbiased coin-flipping schemes. The compiler pipeline first translates cpGCL programs into choice-fix trees, an intermediate representation suitable for reduction of biased probabilistic choices. Choice-fix trees are then translated to coinductive interaction trees for execution within the random bit model. The correctness of the composed translations establishes the sampling equidistribution theorem: compiled samplers are correct wrt. the conditional weakest pre-expectation semantics of cpGCL source programs. Zar is implemented and fully verified in the Coq proof assistant. We extract verified samplers to OCaml and Python and empirically validate them on a number of illustrative examples.
Zero-Knowledge Proof-Based Approach for Verifying the Computational Integrity of Power Grid Controls

—The control of future power grids is migrating from a centralized to a distributed/decentralized scheme to enable a massive penetration of distributed energy resources and bring extreme enhancements of autonomous operations in terms of grid resilience, security, and reliability. Most effort has been on the design of distributed/decentralized controllers; however, the guarantees of the proper execution of the controls are also essential but relatively less emphasized. A common assumption is that local controllers would fully follow the designated controller dynamics based on the data received from communication channels. Such an assumption could be risky because proper execution of the controller dynamics is then built on trust in secure communication and computation. On the other hand, it is impractical for a veriﬁer to repeat all the computations involved in the controls to verify the computational integrity. In this work, we leverage a type of cryptography technology, known as zero-knowledge scalable transparent arguments of knowledge to verify the computational integrity of control algorithms, such that veriﬁers can check the computational integrity with much less computational burden. The method presented here converts the challenge of data integrity into a subset of computational integrity. In this proof-of-concept paper, our focus will be on projected linear dynamics that are commonly seen in distrib- uted/decentralized power system controllers. In particular, we have derived polynomial conditions in the context of zk-STARKs for the projected linear dynamics.
Efficient real-time selective genome sequencing on resource-constrained devices

Abstract Background Third-generation nanopore sequencers offer selective sequencing or “Read Until” that allows genomic reads to be analyzed in real time and abandoned halfway if not belonging to a genomic region of “interest. ” This selective sequencing opens the door to important applications such as rapid and low-cost genetic tests. The latency in analyzing should be as low as possible for selective sequencing to be effective so that unnecessary reads can be rejected as early as possible. However, existing methods that employ a subsequence dynamic time warping (sDTW) algorithm for this problem are too computationally intensive that a massive workstation with dozens of CPU cores still struggles to keep up with the data rate of a mobile phone–sized MinION sequencer. Results In this article, we present Hardware Accelerated Read Until (HARU), a resource-efficient hardware–software codesign-based method that exploits a low-cost and portable heterogeneous multiprocessor system-on-chip platform with on-chip field-programmable gate arrays (FPGA) to accelerate the sDTW-based Read Until algorithm. Experimental results show that HARU on a Xilinx FPGA embedded with a 4-core ARM processor is around 2.5× faster than a highly optimized multithreaded software version (around 85× faster than the existing unoptimized multithreaded software) running on a sophisticated server with a 36-core Intel Xeon processor for a SARS-CoV-2 dataset. The energy consumption of HARU is 2 orders of magnitudes lower than the same application executing on the 36-core server. Conclusions HARU demonstrates that nanopore selective sequencing is possible on resource-constrained devices through rigorous hardware–software optimizations. The source code for the HARU sDTW module is available as open source at https://github.com/beebdev/HARU, and an example application that uses HARU is at https://github.com/beebdev/sigfish-haru.
Actionable Recourse via GANs for Mobile Health

Mobile health apps provide a unique means of collecting data that can be used to deliver adaptive interventions.The predicted outcomes considerably influence the selection of such interventions. Recourse via counterfactuals provides tangible mechanisms to modify user predictions. By identifying plausible actions that increase the likelihood of a desired prediction, stakeholders are afforded agency over their predictions. Furthermore, recourse mechanisms enable counterfactual reasoning that can help provide insights into candidates for causal interventional features. We demonstrate the feasibility of GAN-generated recourse for mobile health applications on ensemble-survival-analysis-based prediction of medium-term engagement in the Safe Delivery App, a digital training tool for skilled birth attendants.
CHARACTERIZING THE YOUNGEST HERSCHEL-DETECTED PROTOSTARS. II. MOLECULAR OUTFLOWS FROM THE MILLIMETER AND THE FAR-INFRARED

We present Combined Array for Research in Millimeter-wave Astronomy (CARMA) CO ( ) observations and Herschel PACS spectroscopy, characterizing the outflow properties toward extremely young and deeply embedded protostars in the Orion molecular clouds. The sample comprises a subset of the Orion protostars known as the PACS Bright Red Sources (PBRS; Stutz et al.). We observed 14 PBRS with CARMA and 8 of these 14 with Herschel, acquiring full spectral scans from 55 to 200 μm. Outflows are detected in CO ( ) from 8 of 14 PBRS, with two additional tentative detections; outflows are also detected from the outbursting protostar HOPS 223 (V2775 Ori) and the Class I protostar HOPS 68. The outflows have a range of morphologies; some are spatially compact, <10,000 au in extent, while others extend beyond the primary beam. The outflow velocities and morphologies are consistent with being dominated by intermediate inclination angles (80° ≥ i ≥ 20°). This confirms the interpretation of the very red 24–70 μm colors of the PBRS as a signpost of high envelope densities, with only one (possibly two) cases of the red colors resulting from edge-on inclinations. We detect high-J (Jup > 13) CO lines and/or H2O lines from 5 of 8 PBRS and only for those with detected CO outflows. The far-infrared CO rotation temperatures of the detected PBRS are marginally colder (∼230 K) than those observed for most protostars (∼300 K), and only one of these five PBRS has detected [O i] 63 μm emission. The high envelope densities could be obscuring some [O i] emission and cause a ∼20 K reduction to the CO rotation temperatures.
Buying Privacy: User Perceptions of Privacy Threats from Mobile Apps

As technology and technology companies have grown in power, ubiquity, and societal influence, some companies -- and notably some mobile apps -- have come to be perceived as privacy threats. Prior work has considered how various factors impact perceptions of threat, including social factors, political speech, and user-interface design. In this work, we investigate how user-visible context clues impact perceptions about whether a mobile application application poses a privacy threat. We conduct a user study with 2109 users in which we find that users depend on context clues -- such as presence of advertising and occurrence (and timing of payment) -- to determine the extent to which a mobile app poses a privacy threat. We also quantify how accurately user assessments match published data collection practices, and we identify a commonly-held misconception about how payments are processed. This work provides new insight into how users assess the privacy threat posed by mobile apps and into social norms around data collection.
Quantum-enhanced deliberation of learning agents using trapped ions

A scheme that successfully employs quantum mechanics in the design of autonomous learning agents has recently been reported in the context of the projective simulation (PS) model for artificial intelligence. In that approach, the key feature of a PS agent, a specific type of memory which is explored via random walks, was shown to be amenable to quantization, allowing for a speed-up. In this work we propose an implementation of such classical and quantum agents in systems of trapped ions. We employ a generic construction by which the classical agents are ‘upgraded’ to their quantum counterparts by a nested process of adding coherent control, and we outline how this construction can be realized in ion traps. Our results provide a flexible modular architecture for the design of PS agents. Furthermore, we present numerical simulations of simple PS agents which analyze the robustness of our proposal under certain noise models.
Clustering of Lyα Emitters around Quasars at z ∼ 4

The strong observed clustering of z > 3.5 quasars indicates that they are hosted by massive ( ) dark matter halos. Assuming that quasars and galaxies trace the same large-scale structures, this should manifest as strong clustering of galaxies around quasars. Previous works on high-redshift quasar environments have failed to find convincing evidence for these overdensities. Here we conduct a survey for Lyα emitters (LAEs) in the environs of 17 quasars at z ∼ 4 probing scales of . We measure an average LAE overdensity of , which we quantify by fitting the quasar–LAE cross-correlation function. We find consistency with a power-law shape with correlation length for a fixed slope of γ = 1.8 and rule out a zero clustering hypothesis at the 95% confidence level. We also measure the LAE autocorrelation length and find cMpc (γ = 1.8), which is times higher than the value measured in blank fields. Taken together, our results clearly indicate that LAEs are significantly clustered around z ∼ 4 quasars. We compare the observed clustering with the expectation from a deterministic bias model, whereby LAEs and quasars probe the same underlying dark matter overdensities, and find that our measurements fall short of the predicted overdensities by a factor of . We discuss possible explanations for this discrepancy, including large-scale quenching or the presence of excess dust in galaxies near quasars. Finally, the large cosmic variance from field to field observed in our sample (10/17 fields are actually underdense) cautions one from overinterpreting studies of z ∼ 6 quasar environments based on a single or handful of quasar fields.
Optimal Intervention in Transportation Networks

We study a network design problem (NDP) where the planner aims at selecting the optimal single-link intervention on a transportation network to minimize the travel time under Wardrop equilibrium flows. Our first result is that, if the delay functions are affine and the support of the equilibrium is not modified with interventions, the NDP may be formulated in terms of electrical quantities computed on a related resistor network. In particular, we show that the travel time variation corresponding to an intervention on a given link depends on the effective resistance between the endpoints of the link. We suggest an approach to approximate such an effective resistance by performing only local computation and exploit it to design an efficient algorithm to solve the NDP. We discuss the optimality of this procedure in the limit of infinitely large networks and provide a sufficient condition for its optimality. We then provide numerical simulations, showing that our algorithm achieves good performance even if the equilibrium support varies and the delay functions are nonlinear.
Approximation Algorithms for Steiner Tree Augmentation Problems

In the Steiner Tree Augmentation Problem (STAP), we are given a graph $G = (V,E)$, a set of terminals $R \subseteq V$, and a Steiner tree $T$ spanning $R$. The edges $L := E \setminus E(T)$ are called links and have non-negative costs. The goal is to augment $T$ by adding a minimum cost set of links, so that there are 2 edge-disjoint paths between each pair of vertices in $R$. This problem is a special case of the Survivable Network Design Problem, which can be approximated to within a factor of 2 using iterative rounding~\cite{J2001}. We give the first polynomial time algorithm for STAP with approximation ratio better than 2. In particular, we achieve an approximation ratio of $(1.5 + \varepsilon)$. To do this, we employ the Local Search approach of~\cite{TZ2022} for the Tree Augmentation Problem and generalize their main decomposition theorem from links (of size two) to hyper-links. We also consider the Node-Weighted Steiner Tree Augmentation Problem (NW-STAP) in which the non-terminal nodes have non-negative costs. We seek a cheapest subset $S \subseteq V \setminus R$ so that $G[R \cup S]$ is 2-edge-connected. Using a result of Nutov~\cite{N2010}, there exists an $O(\log |R|)$-approximation for this problem. We provide an $O(\log^2 (|R|))$-approximation algorithm for NW-STAP using a greedy algorithm leveraging the spider decomposition of optimal solutions.
A Statistical Study of IRIS Observational Signatures of Nanoflares and Nonthermal Particles

Nanoflares are regarded as one of the major mechanisms of magnetic energy release and coronal heating in the solar outer atmosphere. We conduct a statistical study on the response of the chromosphere and transition region to nanoflares, as observed by the Interface Region Imaging Spectrograph (IRIS), by using an algorithm for the automatic detection of these events. The initial atmospheric response to these small heating events is observed, with IRIS, as transient brightening at the foot points of coronal loops heated to high temperatures (>4 MK). For four active regions, observed over 143 hr, we detected 1082 footpoint brightenings under the IRIS slit, and for those we extracted physical parameters from the IRIS Mg ii and Si iv spectra that are formed in the chromosphere and transition region, respectively. We investigate the distributions of the spectral parameters, and the relationships between the parameters, also comparing them with predictions from RADYN numerical simulations of nanoflare-heated loops. We find that these events, and the presence of nonthermal particles, tend to be more frequent in flare productive active regions, and where the hot 94 Å emission measured by the Atmospheric Imaging Assembly is higher. We find evidence for highly dynamic motions characterized by strong Si iv nonthermal velocities (not dependent on the heliocentric x-coordinate, i.e., on the angle between the magnetic field and the line of sight) and asymmetric Mg ii spectra. These findings provide tight new constraints on the properties of nanoflares and nonthermal particles in active regions, and their effects on the lower atmosphere.
A Taxonomic System for Failure Cause Analysis of Open Source AI Incidents

While certain industrial sectors (e.g., aviation) have a long history of mandatory incident reporting complete with analytical findings, the practice of artificial intelligence (AI) safety benefits from no such mandate and thus analyses must be performed on publicly known ``open source'' AI incidents. Although the exact causes of AI incidents are seldom known by outsiders, this work demonstrates how to apply expert knowledge on the population of incidents in the AI Incident Database (AIID) to infer the potential and likely technical causative factors that contribute to reported failures and harms. We present early work on a taxonomic system that covers a cascade of interrelated incident factors, from system goals (nearly always known) to methods / technologies (knowable in many cases) and technical failure causes (subject to expert analysis) of the implicated systems. We pair this ontology structure with a comprehensive classification workflow that leverages expert knowledge and community feedback, resulting in taxonomic annotations grounded by incident data and human expertise.
Retrieval-Augmented Generative Question Answering for Event Argument Extraction

Event argument extraction has long been studied as a sequential prediction problem with extractive-based methods, tackling each argument in isolation. Although recent work proposes generation-based methods to capture cross-argument dependency, they require generating and post-processing a complicated target sequence (template). Motivated by these observations and recent pretrained language models’ capabilities of learning from demonstrations. We propose a retrieval-augmented generative QA model (R-GQA) for event argument extraction. It retrieves the most similar QA pair and augments it as prompt to the current example’s context, then decodes the arguments as answers. Our approach outperforms substantially prior methods across various settings (i.e. fully supervised, domain transfer, and fewshot learning). Finally, we propose a clustering-based sampling strategy (JointEnc) and conduct a thorough analysis of how different strategies influence the few-shot learning performances.
Inferred Cosmic-Ray Spectrum from Fermi-LAT Gamma-Ray Observations of the Earths Limb

Recent accurate measurements of cosmic-ray (CR) species by ATIC-2, CREAM, and PAMELA reveal an unexpected hardening in the proton and He spectra above a few hundred GeV, a gradual softening of the spectra just below a few hundred GeV, and a harder spectrum of He compared to that of protons. These newly-discovered features may offer a clue to the origin of high-energy CRs. We use the Fermi Large Area Telescope observations of the -ray emission from the Earth's limb for an indirect measurement of the local spectrum of CR protons in the energy range approx. 90 GeV-6 TeV (derived from a photon energy range 15 GeV-1 TeV). Our analysis shows that single power law and broken power law spectra fit the data equally well and yield a proton spectrum with index 2.68 +/- 0.04 and 2.61 +/- 0.08 above approx. 200 GeV, respectively.
Collective excitations of a strongly-correlated non-equilibrium photon fluid across the Mott/superfluid phase transition

We develop a Gutzwiller theory for the non-equilibrium steady states of a strongly-interacting photon fluid driven by a non-Markovian incoherent pump. In particular, we explore the collective excitation modes across the out-of-equilibrium Mott/superfluid transition, characterizing the diffusive Goldstone mode in the superfluid phase and the particle/hole excitations in the insulating one. Observable features in the pump-and-probe optical response of the system are highlighted. Our results appear as experimentally accessible to state-of-the-art circuit-QED devices and open the way for driven-dissipative fluids of light as quantum simulators of novel many-body scenarios.