Miracle-dz/UniFAR-FTUs huggingface dataset:
Coordinated Disclosure for AI: Beyond Security Vulnerabilities

Harm reporting in the field of Artificial Intelligence (AI) currently operates on an ad hoc basis, lacking a structured process for disclosing or addressing algorithmic flaws. In contrast, the Coordinated Vulnerability Disclosure (CVD) ethos and ecosystem play a pivotal role in software security and transparency. Within the U.S. context, there has been a protracted legal and policy struggle to establish a safe harbor from the Computer Fraud and Abuse Act, aiming to foster institutional support for security researchers acting in good faith. Notably, algorithmic flaws in Machine Learning (ML) models present distinct challenges compared to traditional software vulnerabilities, warranting a specialized approach. To address this gap, we propose the implementation of a dedicated Coordinated Flaw Disclosure (CFD) framework tailored to the intricacies of machine learning and artificial intelligence issues. This paper delves into the historical landscape of disclosures in ML, encompassing the ad hoc reporting of harms and the emergence of participatory auditing. By juxtaposing these practices with the well-established disclosure norms in cybersecurity, we argue that the broader adoption of CFD has the potential to enhance public trust through transparent processes that carefully balance the interests of both organizations and the community.
Numerical complexity of helix unraveling algorithm for charged particle tracking

This paper describes a procedure for a realistic estimation of the number of iterations in the main loop of a recent particle detection algorithm from [1]. The calculations are based on a Monte Carlo simulation of the ATLAS inner detector. The resulting estimates of numerical complexity suggest that using the procedure from [1] for online triggering is not feasible. There are however some areas, such as triggering for particles in a specific sub-domain of the phase space, where using this procedure might be beneficial.
The fate of entanglement

Quantum entanglement is a fundamentally non-local correlation between particles. In its simplest realisation, a measurement on one particle is affected by a prior measurement on its partner, irrespective of their separation. For multiple particles, purely collective types of entanglement exist but their detection, even theoretically, remains an outstanding open question. Here, we show that all forms of multi-party entanglement entirely disappear during the typical evolution of a system as it is heats up, evolves in time, or as its parts become separated. These results follow from the nature of the entanglement-free continent in the space of physical states, and hold in great generality. We illustrate these phenomena with a frustrated molecular quantum magnet in and out of equilibrium. In contrast, if the particles are fermions, such as electrons, another notion of entanglement exists that precludes entanglement-free regions, and thus protects quantum correlations. These findings provide fundamental knowledge about the structure of entanglement in quantum matter and architectures, paving the way for its manipulation.
OrderBkd: Textual backdoor attack through repositioning

The use of third-party datasets and pre-trained machine learning models poses a threat to NLP systems due to possibility of hidden backdoor attacks. Existing attacks involve poisoning the data samples such as insertion of tokens or sentence paraphrasing, which either alter the semantics of the original texts or can be detected. Our main difference from the previous work is that we use the reposition of a two words in a sentence as a trigger. By designing and applying specific part-of-speech (POS) based rules for selecting these tokens, we maintain high attack success rate on SST-2 and AG classification datasets while outperforming existing attacks in terms of perplexity and semantic similarity to the clean samples. In addition, we show the robustness of our attack to the ONION defense method. All the code and data for the paper can be obtained at https://github.com/alekseevskaia/OrderBkd.
Cruising Queer HCI on the DL: A Literature Review of LGBTQ+ People in HCI

LGBTQ+ people have received increased attention in HCI research, paralleling a greater emphasis on social justice in recent years. However, there has not been a systematic review of how LGBTQ+ people are researched or discussed in HCI. In this work, we review all research mentioning LGBTQ+ people across the HCI venues of CHI, CSCW, DIS, and TOCHI. Since 2014, we find a linear growth in the number of papers substantially about LGBTQ+ people and an exponential increase in the number of mentions. Research about LGBTQ+ people tends to center experiences of being politicized, outside the norm, stigmatized, or highly vulnerable. LGBTQ+ people are typically mentioned as a marginalized group or an area of future research. We identify gaps and opportunities for (1) research about and (2) the discussion of LGBTQ+ in HCI and provide a dataset to facilitate future Queer HCI research.
Fostering the integration of European Open Data into Data Spaces through High-Quality Metadata

The term Data Space, understood as the secure exchange of data in distributed systems, ensuring openness, transparency, decentralization, sovereignty, and interoperability of information, has gained importance during the last years. However, Data Spaces are in an initial phase of definition, and new research is necessary to address their requirements. The Open Data ecosystem can be understood as one of the precursors of Data Spaces as it provides mechanisms to ensure the interoperability of information through resource discovery, information exchange, and aggregation via metadata. However, Data Spaces require more advanced capabilities including the automatic and scalable generation and publication of high-quality metadata. In this work, we present a set of software tools that facilitate the automatic generation and publication of metadata, the modeling of datasets through standards, and the assessment of the quality of the generated metadata. We validate all these tools through the YODA Open Data Portal showing how they can be connected to integrate Open Data into Data Spaces.
Simulating the Quantum Rabi Model in Superconducting Qubits at Deep Strong Coupling

The Quantum Rabi model serves as a pivotal theoretical framework for elucidating the nuanced interplay between light and matter. Utilizing circuit quantum electrodynamics on a chip, we address the challenge of achieving deep strong coupling in Quantum Cavity Electrodynamics (cQED). Despite progress in superconducting circuits and trapped ions, experimental realization has been limited to spectroscopy. Our focus is on a transformative digital quantum simulation, employing Trotterization with an augmented number of steps to deconstruct a complex unitary Hamiltonian. This approach showcases the benefits of digital techniques within superconducting circuits, offering universality, flexibility, scalability, and high fidelity. Our goal is to demonstrate deep strong coupling in cQED and understand the advantages of digital methods, particularly in coherent measurement during time evolution with varying photon counts in resonators. This opens avenues to leverage quantum mechanics for overcoming hardware limitations.
Physically based modeling of the impacts of climate change on streamflow regime

Physically Based Modeling of the Impacts of Climate Change on Streamflow Regime by Nazmus Shams Sazib, Doctor of Philosophy Utah State University, 2016 Major Professor: David G. Tarboton Department: Civil and Environmental Engineering Understanding the implications of climate change on streamflow regime is complex as changes in climate vary over space and time. However, a better understanding of the impact of climate change is required for identifying how stream ecosystems vulnerable to these changes, and ultimately to guide the development of robust strategies for reducing risk in the face of changing climatic conditions. Here I used physically based hydrologic modeling to improve understanding of how climate change may impact streamflow regimes and advance some of the cyberinfrastructure and GIS methodologies that support physically based hydrologic modeling by: (1) using a physically based model to examine the potential effects of climate change on ecologically relevant aspects of streamflow regime, (2) developing data services in support of input data preparation for physically based distributed hydrologic models, and (3) enhancing terrain analysis algorithms to support rapid watershed delineation over large area. TOPNET, a physically based hydrologic model was applied over eight watersheds across the U.S to assess the sensitivity and changes of the streamflow regime due to climate
Privacy-Preserving Gaze Data Streaming in Immersive Interactive Virtual Reality: Robustness and User Experience

Eye tracking is routinely being incorporated into virtual reality (VR) systems. Prior research has shown that eye tracking data can be used for re-identification attacks. The state of our knowledge about currently existing privacy mechanisms is limited to privacy-utility trade-off curves based on data-centric metrics of utility, such as prediction error, and black-box threat models. We propose that for interactive VR applications, it is essential to consider user-centric notions of utility and a variety of threat models. We develop a methodology to evaluate real-time privacy mechanisms for interactive VR applications that incorporate subjective user experience and task performance metrics. We evaluate selected privacy mechanisms using this methodology and find that re-identification accuracy can be decreased to as low as 14% while maintaining a high usability score and reasonable task performance. Finally, we elucidate three threat scenarios (black-box, black-box with exemplars, and white-box) and assess how well the different privacy mechanisms hold up to these adversarial scenarios. This work advances the state of the art in VR privacy by providing a methodology for end-to-end assessment of the risk of re-identification attacks and potential mitigating solutions.
Encoding Majorana codes

To implement a quantum error correction protocol, we first need a scheme to prepare our state in the correct subspace of the code, and this can be done using a unitary encoding circuit. Majorana codes are special since any gates that transform such codes must preserve fermionic parity. In this paper, we present an algorithm that uses the stabilizer matrix to compute unitary encoding circuits for Majorana codes. We present two approaches, both of which use a version of Gaussian elimination with row operations replaced with elementary fermionic Clifford operations. One approach uses an additional ancilla mode and works for all Majorana stabilizer codes, while the second approach does not use ancilla but does not work if the total parity is inside the stabilizer group.
Insights into Natural Language Database Query Errors: From Attention Misalignment to User Handling Strategies

Querying structured databases with natural language (NL2SQL) has remained a difficult problem for years. Recently, the advancement of machine learning (ML), natural language processing (NLP), and large language models (LLM) have led to significant improvements in performance, with the best model achieving ∼ 85% percent accuracy on the benchmark Spider dataset. However, there is a lack of a systematic understanding of the types, causes, and effectiveness of error-handling mechanisms of errors for erroneous queries nowadays. To bridge the gap, a taxonomy of errors made by four representative NL2SQL models was built in this work, along with an in-depth analysis of the errors. Second, the causes of model errors were explored by analyzing the model-human attention alignment to the natural language query. Last, a within-subjects user study with 26 participants was conducted to investigate the effectiveness of three interactive error-handling mechanisms in NL2SQL. Findings from this paper shed light on the design of model structure and error discovery and repair strategies for natural language data query interfaces in the future.
Joint User and Beam Selection in Millimeter Wave Networks

We study the problem of selecting a user equipment (UE) and a beam for each access point (AP) for concurrent transmissions in a millimeter wave (mmWave) network, such that the sum of weighted rates of UEs is maximized. We prove that this problem is NP-complete. We propose two algorithms -- Markov Chain Monte Carlo (MCMC) based and local interaction game (LIG) based UE and beam selection -- and prove that both of them asymptotically achieve the optimal solution. Also, we propose two fast greedy algorithms -- NGUB1 and NGUB2 -- for UE and beam selection. Through extensive simulations, we show that our proposed greedy algorithms outperform the most relevant algorithms proposed in prior work and perform close to the asymptotically optimal algorithms.
Logistic-beta processes for modeling dependent random probabilities with beta marginals

The beta distribution serves as a canonical tool for modeling probabilities and is extensively used in statistics and machine learning, especially in the field of Bayesian nonparametrics. Despite its widespread use, there is limited work on flexible and computationally convenient stochastic process extensions for modeling dependent random probabilities. We propose a novel stochastic process called the logistic-beta process, whose logistic transformation yields a stochastic process with common beta marginals. Similar to the Gaussian process, the logistic-beta process can model dependence on both discrete and continuous domains, such as space or time, and has a highly flexible dependence structure through correlation kernels. Moreover, its normal variance-mean mixture representation leads to highly effective posterior inference algorithms. The flexibility and computational benefits of logistic-beta processes are demonstrated through nonparametric binary regression simulation studies. Furthermore, we apply the logistic-beta process in modeling dependent Dirichlet processes, and illustrate its application and benefits through Bayesian density regression problems in a toxicology study.
Continuum of spin excitations in an ordered magnet

Continuum of spin excitations observed in inelastic neutron scattering experiments are often considered as a strong evidence of quantum spin liquid formation. When quantum spin liquid is indeed the ground state of a disorder-free magnetic compound, the elementary excitation is no longer the conventional spin waves (magnons). Instead, the magnons fractionalize into spinons, leaving only a two-spinon continuum detectable in inelastic neutron scattering experiments. For a clean ordered antiferromagnet, it was unclear if we can observe a continuous spectrum similar to the ones in a quantum spin liquid state. Here we show that the magnetically ordered state in Na$_2$BaCo(PO$_4$)$_2$ is able to host a spin excitation continuum induced by strong quantum fluctuations. Thus, a second thought is necessary when concluding such continuum as signature of quantum spin liquid in new material explorations.
High-Rate Fair-Density Parity-Check Codes

We introduce fair-density parity-check (FDPC) codes targeting high-rate applications. In particular, we start with a base parity-check matrix $H_b$ of dimension $2 \sqrt{n} \times n$, where $n$ is the code block length, and the number of ones in each row and column of $H_b$ is equal to $\sqrt{n}$ and $2$, respectively. We propose a deterministic combinatorial method for picking the base matrix $H_b$, assuming $n=4t^2$ for some integer $t \geq 2$. We then extend this by obtaining permuted versions of $H_b$ (e.g., via random permutations of its columns) and stacking them on top of each other leading to codes of dimension $k \geq n-2s\sqrt{n}+s$, for some $s \geq 2$, referred to as order-$s$ FDPC codes. We propose methods to explicitly characterize and bound the weight distribution of the new codes and utilize them to derive union-type approximate upper bounds on their error probability under Maximum Likelihood (ML) decoding. For the binary erasure channel (BEC), we demonstrate that the approximate ML bound of FDPC codes closely follows the random coding upper bound (RCU) for a wide range of channel parameters. Also, remarkably, FDPC codes, under the low-complexity min-sum decoder, improve upon 5G-LDPC codes for transmission over the binary-input additive white Gaussian noise (B-AWGN) channel by almost 0.5dB (for $n=1024$, and rate $=0.878$). Furthermore, we propose a new decoder as a combination of weighted min-sum message-passing (MP) decoding algorithm together with a new progressive list (PL) decoding component, referred to as the MP-PL decoder, to further boost the performance of FDPC codes. This paper opens new avenues for a fresh investigation of new code constructions and decoding algorithms in high-rate regimes suitable for ultra-high throughput (high-frequency/optical) applications.
Langmuir-like model of dilute impurities in concentrated solid solutions

High-entropy alloys have drawn recent interest for their promising mechanical properties and irradiation resistance. Various properties, namely transport properties, are controlled by point defect concentration, which must be known before performing atomistic simulations to compute transport coefficients. In this work, we present a general Langmuir-like model for impurity concentration in an arbitrarily complex solid solution and apply this model to generate expressions for concentrations of vacancies and small interstitial atoms. We then calculate the vacancy concentration as a function of temperature in the equiatomic CoNiCrFeMn and FeAl alloys with modified embedded-atom-method potentials for various chemical orderings, showing there is no clear correlation between vacancy thermodynamics and chemical ordering in the CoNiCrFeMn alloy but clear systematic patterns for FeAl.
High-resolution Cryogenic Spectroscopy of Single Molecules in Nanoprinted Crystals

We perform laser spectroscopy at liquid helium temperatures (T=2 K) to investigate single dibenzoterrylene (DBT) molecules doped in anthracene crystals of nanoscopic height fabricated by electrohydrodynamic dripping. Using high-resolution fluorescence excitation spectroscopy, we show that zero-phonon lines of single molecules in printed nanocrystals are nearly as narrow as the Fourier-limited transitions observed for the same guest-host system in the bulk. Moreover, the spectral instabilities are comparable to or less than one linewidth. By recording super-resolution images of DBT molecules and varying the polarization of the excitation beam, we determine the dimensions of the printed crystals and the orientation of the crystals' axes. Electrohydrodynamic printing of organic nano and microcrystals paves the way for a series of applications, where controlled positioning of quantum emitters with narrow optical transitions is desirable.
RIS-Augmented Millimeter-Wave MIMO Systems for Passive Drone Detection

In the past decade, the number of amateur drones is increasing, and this trend is expected to continue in the future. The security issues brought by abuse and misconduct of drones become more and more severe and may incur a negative impact to the society. In this paper, we leverage existing cellular multiple-input multiple-output (MIMO) base station (BS) infrastructure, operating at millimeter wave (mmWave) frequency bands, for drone detection in a device-free manner with the aid of one reconfigurable intelligent surface (RIS), deployed in the proximity of the BS. We theoretically examine the feasibility of drone detection with the aid of the generalized likelihood ratio test (GLRT) and validate via simulations that, the optimized deployment of an RIS can bring added benefits compared to RIS-free systems. In addition, the effect of RIS training beams, training overhead, and radar cross section, is investigated in order to offer theoretical design guidance for the proposed cellular RIS-based passive drone detection system.
On the Magnetic Field Properties of Protostellar Envelopes in Orion

We present 870 μm polarimetric observations toward 61 protostars in the Orion molecular clouds with ∼400 au (1″) resolution using the Atacama Large Millimeter/submillimeter Array. We successfully detect dust polarization and outflow emission in 56 protostars; in 16 of them the polarization is likely produced by self-scattering. Self-scattering signatures are seen in several Class 0 sources, suggesting that grain growth appears to be significant in disks at earlier protostellar phases. For the rest of the protostars, the dust polarization traces the magnetic field, whose morphology can be approximately classified into three categories: standard-hourglass, rotated-hourglass (with its axis perpendicular to outflow), and spiral-like morphology. A total of 40.0% (±3.0%) of the protostars exhibit a mean magnetic field direction approximately perpendicular to the outflow on several × 102–103 au scales. However, in the remaining sample, this relative orientation appears to be random, probably due to the complex set of morphologies observed. Furthermore, we classify the protostars into three types based on the C17O (3–2) velocity envelope’s gradient: perpendicular to outflow, nonperpendicular to outflow, and unresolved gradient (≲1.0 km s−1 arcsec−1). In protostars with a velocity gradient perpendicular to outflow, the magnetic field lines are preferentially perpendicular to outflow, with most of them exhibiting a rotated hourglass morphology, suggesting that the magnetic field has been overwhelmed by gravity and angular momentum. Spiral-like magnetic fields are associated with envelopes having large velocity gradients, indicating that the rotation motions are strong enough to twist the field lines. All of the protostars with a standard-hourglass field morphology show no significant velocity gradient due to the strong magnetic braking.
Morse sequences

We introduce the notion of a Morse sequence, which provides a simple and effective approach to discrete Morse theory. A Morse sequence is a sequence composed solely of two elementary operations, that is, expansions (the inverse of a collapse), and fillings (the inverse of a perforation). We show that a Morse sequence may be seen as an alternative way to represent the gradient vector field of an arbitrary discrete Morse function. We also show that it is possible, in a straightforward manner, to make a link between Morse sequences and different kinds of Morse functions. At last, we introduce maximal Morse sequences, which formalize two basic schemes for building a Morse sequence from an arbitrary simplicial complex.
The Diffusion Limit of Photoevaporation in Primordial Planetary Atmospheres

Photoevaporation is thought to play an important role in the early planetary evolution. In this study, we investigate the diffusion limit of X-ray and ultraviolet induced photoevaporation in primordial atmospheres. We find that compositional fractionation resulting from mass loss is more significant than currently recognized because it is controlled by the conditions at the top of the atmosphere, where particle collisions are less frequent. Such fractionation at the top of the atmosphere develops a compositional gradient that extends downward. Mass outflow eventually reaches a steady state in which hydrogen loss is diffusion limited. We derive new analytic expressions for the diffusion-limited mass loss rate and the crossover mass.