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Large language model-based AI companions are increasingly viewed by users as friends or romantic partners, leading to deep emotional bonds. However, they can generate biased, discriminatory, and harmful outputs. Recently, users are taking the initiative to address these harms and re-align AI companions. We introduce the concept of user-driven value alignment, where users actively identify, challenge, and attempt to correct AI outputs they perceive as harmful, aiming to guide the AI to better align with their values. We analyzed 77 social media posts about discriminatory AI statements and conducted semi-structured interviews with 20 experienced users. Our analysis revealed six common types of discriminatory statements perceived by users, how users make sense of those AI behaviors, and seven user-driven alignment strategies, such as gentle persuasion and anger expression. We discuss implications for supporting user-driven value alignment in future AI systems, where users and their communities have greater agency., Comment: 17 pages, 1 figure

We investigate the influence of effective nucleon mass on the equation of state (EOS), which is constructed for simulations of core-collapse supernovae and binary neutron-star mergers, within the relativistic mean-field (RMF) framework. The study introduces a new RMF parameter set, TM1m, which is a modification of the TM1e model with an adjusted effective mass, maintaining the saturation properties of nuclear matter. The TM1m model, with a larger effective mass ratio ($M^{\ast}/M \sim 0.8$) compared to the TM1e model ($M^{\ast}/M \sim 0.63$), is employed to construct a new EOS table, EOS5. This EOS table is designed to offer insights into the influence of effective nucleon mass on the EOS within a relativistic framework, particularly above the saturation density. The properties of cold neutron stars, calculated using the TM1m model, are compared with those of the TM1e and original TM1 models. Both the TM1m and TM1e models are found to be compatible with the latest constraints on tidal deformability and radii of neutron stars, derived from astrophysical observations., Comment: 11 pages, 11 figures, 2 tables

This study investigates the first-order phase transition within neutron stars, leveraging the deep neural network (DNN) framework alongside contemporary astronomical measurements. The equation of state (EOS) for neutron stars is delineated in a piecewise polytropic form, with the speed of sound ($c_s$) serving as a pivotal determinant. In the phase transition region, $c_s$ is presumed to be zero, while in other intervals, it is optimized utilizing the DNN. Various onset energy densities of phase transition ($\varepsilon_{pt}$), spanning from $2\varepsilon_0$ to $3\varepsilon_0$ (where $\varepsilon_0$ denotes the energy density at nuclear saturation density), as well as phase transition widths ($\Delta\varepsilon$) ranging from $0.5\varepsilon_0$ to $\varepsilon_0$, are examined. Our findings underscore that smaller values of $\varepsilon_{pt}$ lead to a more substantial impact of $\Delta\varepsilon$ on neutron star properties, encompassing maximum mass, corresponding radius, tidal deformability, phase transition mass, and trace anomaly. Conversely, when $\varepsilon_{pt}$ exceeds $2.5\varepsilon_0$, the influence of $\Delta\varepsilon$ diminishes, resulting in a stiffer EOS compared to scenarios lacking a phase transition. Furthermore, the trace anomaly at high density shifts to negative values upon the commencement of the phase transition. It is noteworthy that the correlations between the average speed of sound at different energy density segments demonstrate a notably weak connection. The discernment of whether a phase transition has occurred with the present observables of neutron stars poses a challenging task., Comment: 23 pages, 9 figures, 3 tables, accepted by Physical Review D

Automatic segmentation of the bronchial tree from CT imaging is important, as it provides structural information for disease diagnosis. Despite the merits of previous automatic bronchus segmentation methods, they have paied less attention to the issue we term as \textit{Intensity Confusion}, wherein the intensity values of certain background voxels approach those of the foreground voxels within bronchi. Conversely, the intensity values of some foreground voxels are nearly identical to those of background voxels. This proximity in intensity values introduces significant challenges to neural network methodologies. To address the issue, we introduce a novel Intensity-Distance Guided loss function, which assigns adaptive weights to different image voxels for mining hard samples that cause the intensity confusion. The proposed loss estimates the voxel-level hardness of samples, on the basis of the following intensity and distance priors. We regard a voxel as a hard sample if it is in: (1) the background and has an intensity value close to the bronchus region; (2) the bronchus region and is of higher intensity than most voxels inside the bronchus; (3) the background region and at a short distance from the bronchus. Extensive experiments not only show the superiority of our method compared with the state-of-the-art methods, but also verify that tackling the intensity confusion issue helps to significantly improve bronchus segmentation. Project page: https://github.com/lhaof/ICM., Comment: IEEE International Conference on Multimedia & Expo (ICME) 2024

In this paper, we investigate a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system under typical block-fading channels. As a non-trivial extension to most existing works on ISAC, both the training and transmission signals sent by the ISAC transmitter are exploited for sensing. Specifically, we develop two training and transmission design schemes to minimize a weighted sum of the mean-squared errors (MSEs) of data transmission and radar target response matrix (TRM) estimation. For the former, we first optimize the training signal for simultaneous communication channel and radar TRM estimation. Then, based on the estimated instantaneous channel state information (CSI), we propose an efficient majorization-minimization (MM)-based robust ISAC transmission design, where a semi-closed form solution is obtained in each iteration. For the second scheme, the ISAC transmitter is assumed to have statistical CSI only for reducing the feedback overhead. With CSI statistics available, we integrate the training and transmission design into one single problem and propose an MM-based alternating algorithm to find a high-quality solution. In addition, we provide alternative structured and low-complexity solutions for both schemes under certain special cases. Finally, simulation results demonstrate that the radar performance is significantly improved compared to the existing scheme that integrates sensing into the transmission stage only. Moreover, it is verified that the investigated two schemes have advantages in terms of communication and sensing performances, respectively.

Mental illness remains one of the most critical public health issues. Despite its importance, many mental health professionals highlight a disconnect between their training and actual real-world patient practice. To help bridge this gap, we propose PATIENT-{\Psi}, a novel patient simulation framework for cognitive behavior therapy (CBT) training. To build PATIENT-{\Psi}, we construct diverse patient cognitive models based on CBT principles and use large language models (LLMs) programmed with these cognitive models to act as a simulated therapy patient. We propose an interactive training scheme, PATIENT-{\Psi}-TRAINER, for mental health trainees to practice a key skill in CBT -- formulating the cognitive model of the patient -- through role-playing a therapy session with PATIENT-{\Psi}. To evaluate PATIENT-{\Psi}, we conducted a comprehensive user study of 13 mental health trainees and 20 experts. The results demonstrate that practice using PATIENT-{\Psi}-TRAINER enhances the perceived skill acquisition and confidence of the trainees beyond existing forms of training such as textbooks, videos, and role-play with non-patients. Based on the experts' perceptions, PATIENT-{\Psi} is perceived to be closer to real patient interactions than GPT-4, and PATIENT-{\Psi}-TRAINER holds strong promise to improve trainee competencies. Our code and data are released at \url{https://github.com/ruiyiw/patient-psi}., Comment: 9 pages, 5 figures

The exploration of extremely large antenna arrays (ELAAs) using high-frequency spectrum has led to a paradigm shift in electromagnetic radiation field, transitioning from the common use case of far-field propagation to near-field propagation. This shift necessitates the modification of the conventional planar-wavefront approximation to more accurate spherical waves, exerting a profound impact on wireless transmission technologies encompassing communication and sensing. Concurrently, integrated sensing and communication (ISAC) has gained prominence in the context of the sixth-generation (6G) wireless networks owing to its ability to cater to the ever-increasing demands of future networks. In line with this evolving trend, this article presents a systematical investigation on ELAA-empowered near-field ISAC. We begin by introducing the fundamentals of near-field propagation with an emphasis on its double-edged effects to near-field communications. Then, we turn to near-field sensing and expound upon various typical applications. Following the separate elaborations on communications and sensing, we articulate in-depth advantages of ELAA-empowered ISAC in near field, particularly including featured opportunities arising from the dual-functional integrations, potential ISAC applications benefiting from the additional degrees-of-freedom in near field, and enablements of other complementary technologies. Finally, we outline key technical challenges that merit further exploration in the realm of ELAA-empowered near-field ISAC.

The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols. Consequently, error propagation can occur in traditional turbo receivers, leading to performance degradation. Moreover, the processing delay induced by information exchange between different modules may also be undesirable for URLLC. To address the issues, we advocate to perform joint channel estimation, detection, and decoding (JCDD) for MIMO URLLC systems encoded by short low-density parity-check (LDPC) codes. Specifically, we develop two novel JCDD problem formulations based on the maximum a posteriori (MAP) criterion for Gaussian MIMO channels and sparse mmWave MIMO channels, respectively, which integrate the pilots, the bit-to-symbol mapping, the LDPC code constraints, as well as the channel statistical information. Both the challenging large-scale non-convex problems are then solved based on the alternating direction method of multipliers (ADMM) algorithms, where closed-form solutions are achieved in each ADMM iteration. Furthermore, two JCDD neural networks, called JCDDNet-G and JCDDNet-S, are built by unfolding the derived ADMM algorithms and introducing trainable parameters. It is interesting to find via simulations that the proposed trainable JCDD receivers can outperform the turbo receivers with affordable computational complexities., Comment: 17 pages, 12 figures, accepted by IEEE Transactions on Wireless Communications

Reconfigurable intelligent surface (RIS) is a promising technique to improve the performance of future wireless communication systems at low energy consumption. To reap the potential benefits of RIS-aided beamforming, it is vital to enhance the accuracy of channel estimation. In this paper, we consider an RIS-aided multiuser system with non-ideal reflecting elements, each of which has a phase-dependent reflecting amplitude, and we aim to minimize the mean-squared error (MSE) of the channel estimation by jointly optimizing the training signals at the user equipments (UEs) and the reflection pattern at the RIS. As examples the least squares (LS) and linear minimum MSE (LMMSE) estimators are considered. The considered problems do not admit simple solution mainly due to the complicated constraints pertaining to the non-ideal RIS reflecting elements. As far as the LS criterion is concerned, we tackle this difficulty by first proving the optimality of orthogonal training symbols and then propose a majorization-minimization (MM)-based iterative method to design the reflection pattern, where a semi-closed form solution is obtained in each iteration. As for the LMMSE criterion, we address the joint training and reflection pattern optimization problem with an MM-based alternating algorithm, where a closed-form solution to the training symbols and a semi-closed form solution to the RIS reflecting coefficients are derived, respectively. Furthermore, an acceleration scheme is proposed to improve the convergence rate of the proposed MM algorithms. Finally, simulation results demonstrate the performance advantages of our proposed joint training and reflection pattern designs.

Large language models are increasingly applied in real-world scenarios, including research and education. These models, however, come with well-known ethical issues, which may manifest in unexpected ways in human-computer interaction research due to the extensive engagement with human subjects. This paper reports on research practices related to LLM use, drawing on 16 semi-structured interviews and a survey conducted with 50 HCI researchers. We discuss the ways in which LLMs are already being utilized throughout the entire HCI research pipeline, from ideation to system development and paper writing. While researchers described nuanced understandings of ethical issues, they were rarely or only partially able to identify and address those ethical concerns in their own projects. This lack of action and reliance on workarounds was explained through the perceived lack of control and distributed responsibility in the LLM supply chain, the conditional nature of engaging with ethics, and competing priorities. Finally, we reflect on the implications of our findings and present opportunities to shape emerging norms of engaging with large language models in HCI research.

We develop a density-dependent quark mean-field (DDQMF) model to study the properties of nuclear matter and neutron stars, where the coupling strength between $\sigma$ meson and nucleon is generated by the degree of freedom of quarks, while other meson coupling constants are regarded as density-dependent ones. Two values for the nucleon effective mass, $M^*_{N0}/M_N=0.556,~0.70$ at the saturation density are chosen based on the consideration of the core-collapse supernova simulation and finite nuclei when the meson-nucleon coupling constants are fixed. We find that the equation of state (EOS) of nuclear matter, the symmetry energy, the mass-radius relations, and the tidal deformabilities of neutron stars with larger nucleon effective mass are more sensitive to the skewness coefficient $J_0$. The EOSs with $M^*_{N0}/M_N=0.70$ are softer when the skewness coefficient $J_0=-800$ MeV. However, the maximum masses of the neutron star can be around $2.32M_\odot$ with $J_0=400$ MeV regardless of the value of the nucleon effective mass. By manipulating the coupling strength of the isovector meson to generate different slopes of symmetry energy, we construct the neutron star EOSs that can satisfy the different variables from the simultaneous mass-radius measurements of PSR J0030+0451, PSR J0740+6620 by the NICER collaboration, the mass-radius relations of HESS J1731-347, and the radius constraints from the gravitational-wave signal GW170817 in the framework of DDQMF model. At the same time, most of these constructed EOSs can also satisfy the constraints of the tidal deformability from GW170817 event.

We explore the potential manifestation of a hexaquark, the H particle, as a constituent within neutron stars. The H particle, characterized by a quark composition of $uuddss$, is constructed using the framework of Chromomagnetic Interaction (CMI). Specifically, we contemplate the flavor-singlet state H with $J^P=0^+$. Our computations indicate that the three-flavor hexaquark state, the H particle, possesses a lower mass of $2212.7~\rm{MeV}$ in comparison to the $d^*(2380)$, implying greater stability than the two-flavor $d^*(2380)$. The analysis involving the H particle is carried out using the relativistic mean-field (RMF) model. We investigate the influence of H particle couplings, a key factor in determining the system stability, and focus on the potential existence of H particle within neutron stars. We find that H particle could potentially endure as a stable constituent within neutron stars, and lead to a reduction of the maximum mass., Comment: 15 pages, 6 figures

This survey study focuses on public opinion regarding the use of algorithmic decision-making in government sectors, specifically in Allegheny County, Pennsylvania. Algorithms are becoming increasingly prevalent in various public domains, including both routine and high-stakes government functions. Despite their growing use, public sentiment remains divided, with concerns about privacy and accuracy juxtaposed against perceptions of fairness when compared to human decision-making. In April 2021, a survey was conducted among nearly 1,500 county residents to explore their awareness, experiences, and attitudes towards these algorithms. The study highlights diverse viewpoints influenced by factors such as race, age, education, gender, income, and urban or suburban living. The results demonstrate the complexity of public sentiment towards algorithmic governance and emphasize the need for a nuanced understanding and approach in policy and implementation.

The nature of pseudogap lies at the heart of strongly-interacting superconductivity and superfluidity. With known pairing interactions, unitary Fermi gases provide an ideal testbed to verify whether a pseudogap can arise from many-body pairing. Here we report the observation of the long-sought pair-fluctuation-driven pseudogap in homogeneous unitary Fermi gases of lithium-6 atoms, by precisely measuring the spectral function through momentum-resolved microwave spectroscopy without the serious effects of final-state effect. We find a large pseudogap above the superfluid transition. The inverse pair lifetime exhibits a thermally-activated exponential behavior, uncovering the microscopic virtual pair breaking and recombination mechanism. The obtained large, T-independent single-particle scattering rate is comparable with that set by the Planckian limit. Our findings quantitatively characterize the pseudogap in strongly-interacting Fermi gases, highlighting the role of preformed pairing as a precursor to superfluidity.

Biomarker detection is an indispensable part in the diagnosis and treatment of low-grade glioma (LGG). However, current LGG biomarker detection methods rely on expensive and complex molecular genetic testing, for which professionals are required to analyze the results, and intra-rater variability is often reported. To overcome these challenges, we propose an interpretable deep learning pipeline, a Multi-Biomarker Histomorphology Discoverer (Multi-Beholder) model based on the multiple instance learning (MIL) framework, to predict the status of five biomarkers in LGG using only hematoxylin and eosin-stained whole slide images and slide-level biomarker status labels. Specifically, by incorporating the one-class classification into the MIL framework, accurate instance pseudo-labeling is realized for instance-level supervision, which greatly complements the slide-level labels and improves the biomarker prediction performance. Multi-Beholder demonstrates superior prediction performance and generalizability for five LGG biomarkers (AUROC=0.6469-0.9735) in two cohorts (n=607) with diverse races and scanning protocols. Moreover, the excellent interpretability of Multi-Beholder allows for discovering the quantitative and qualitative correlations between biomarker status and histomorphology characteristics. Our pipeline not only provides a novel approach for biomarker prediction, enhancing the applicability of molecular treatments for LGG patients but also facilitates the discovery of new mechanisms in molecular functionality and LGG progression., Comment: 47 pages, 6 figures

Successful information propagation from source to destination in Vehicular Adhoc Network (VANET) can be hampered by the presence of neighbouring attacker nodes causing unwanted packet dropping. Potential attackers change their behaviour over time and remain undetected due to the ad-hoc nature of VANET. Capturing the dynamic attacker behaviour and updating the corresponding neighbourhood information without compromising the quality of service requirements is an ongoing challenge. This work proposes a Reinforcement Learning (RL) based neighbour selection framework for VANET with an adaptive trust management system to capture the behavioural changes of potential attackers and to dynamically update the neighbourhood information. In contrast to existing works, we consider trust and link-life time in unison as neighbour selection criteria to achieve trustworthy communication. Our adaptive trust model takes into account the social relationship, time and confidence in trust observation to avoid four types of attackers. To update the neighbourhood information, our framework sets the learning rate of the RL agent according to the velocities of the neighbour nodes to improve the model's adaptability to network topology changes. Results demonstrate that our method can take less number of hops to the destination for large network sizes while can response is up to 54 percent faster compared to a baseline method. Also, the proposed model can outperform the other baseline method by reducing the packet dropping rate up to 57 percent caused by the attacker., Comment: This article is accepted at the 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) 2023

In this paper, we investigate the design of statistically robust detectors for multi-input multi-output (MIMO) systems subject to imperfect channel state information (CSI). A robust maximum likelihood (ML) detection problem is formulated by taking into consideration the CSI uncertainties caused by both the channel estimation error and the channel variation. To address the challenging discrete optimization problem, we propose an efficient alternating direction method of multipliers (ADMM)-based algorithm, which only requires calculating closed-form solutions in each iteration. Furthermore, a robust detection network RADMMNet is constructed by unfolding the ADMM iterations and employing both model-driven and data-driven philosophies. Moreover, in order to relieve the computational burden, a low-complexity ADMM-based robust detector is developed using the Gaussian approximation, and the corresponding deep unfolding network LCRADMMNet is further established. On the other hand, we also provide a novel robust data-aided Kalman filter (RDAKF)-based channel tracking method, which can effectively refine the CSI accuracy and improve the performance of the proposed robust detectors. Simulation results validate the significant performance advantages of the proposed robust detection networks over the non-robust detectors with different CSI acquisition methods., Comment: 15 pages, 8 figures, 2 tables; Accepted by IEEE TCOM

The emergence of Large Language Models (LLMs) has brought both excitement and concerns to social computing research. On the one hand, LLMs offer unprecedented capabilities in analyzing vast amounts of textual data and generating human-like responses, enabling researchers to delve into complex social phenomena. On the other hand, concerns are emerging regarding the validity, privacy, and ethics of the research when LLMs are involved. This SIG aims at offering an open space for social computing researchers who are interested in understanding the impacts of LLMs to discuss their current practices, perspectives, challenges when engaging with LLMs in their everyday work and collectively shaping the emerging norms of using LLMs in social computing research.

To improve the application-level communication performance, scheduling of coflows, a collection of parallel flows sharing the same objective, is prevalent in modern data center networks (DCNs). Meanwhile, a hybrid-switched DCN design combining optical circuit switches (OPS) and electrical packet switches (EPS) for transmitting high-volume traffic and low-volume traffic separately has received considerable research attention recently. Efficient scheduling of coflows on hybrid network links is crucial for reducing the overall communication time. However, because of the reconfiguration delay in the circuit switch due to the ultra-high transmission rate and the limitation of bandwidth in the packet switch, coflow scheduling becomes increasingly challenging. The existing coflow scheduling algorithms in hybrid-switched DCNs are all heuristic and provide no performance guarantees. In this work, we propose an approximation algorithm with the worst-case performance guarantee of 2+ \lambda?, where \lambda? is a factor related to system parameters and demand characteristics, for single coflow scheduling in hybridswitched DCN to minimize the coflow completion time (CCT). Extensive simulations based on Facebook data traces show that our algorithm outperforms the state-of-the-art schemes Solstice by 1.14? and Reco-Sin by 1.42? in terms of minimizing CCT.

A recent report has identified a central compact object (CCO) within the supernova remnant HESS J1731-347, with a mass and radius of $M=0.77^{+0.20}_{-0.17}M{\odot}$ and $R=10.4^{+0.86}_{-0.78}$ km, respectively. To investigate this light compact star, a density-dependent relativistic mean-field (DDRMF) model, specifically the DDVT model, has been employed. The DDVT model incorporates tensor couplings of vector mesons, which {can} successfully describe the properties of finite nuclei, such as charge radius, binding energy, and spin-orbit splitting. The introduction of tensor coupling reduces the influence of scalar mesons and generates a softer equation of state (EOS) in the outer core of the neutron star. Moreover, it has been found that the crust segment plays a crucial role in reproducing the mass-radius relation of HESS J1731-347, indicating a preference for a soft crust EOS. By manipulating the coupling strength of the isovector meson in the DDVT parameter set, a reasonable hadronic EOS has been obtained, satisfying the constraints from the gravitational-wave signal GW170817, the simultaneous mass-radius measurements from the NICER collaboration, and the properties of finite nuclei. Notably, the mass-radius relations derived from this hadronic EOS also accurately describe the observables of HESS J1731-347. Therefore, based on our estimation, the CCO in HESS J1731-347 may represent the lightest known neutron star., Comment: 20 pages, 4 figures, 4 tables, suggestions and comments are welcome!

The equation of state of neutron-rich nuclear matter is of interest to both nuclear physics and astrophysics. We have demonstrated the consistency between laboratory and astrophysical nuclear matter in neutron stars by considering low-density nuclear physics constraints (from $^{208}$Pb neutron-skin thickness) and high-density astrophysical constraints (from neutron star global properties). We have used both quark-level and hadron-level models, taking the quark mean-field (QMF) model and the relativistic mean-field (RMF) model as examples, respectively. We have constrained the equation of states of neutron stars and some key nuclear matter parameters within the Bayesian statistical approach, using the first multi-messenger event GW170817/AT 2017gfo, as well as the mass-radius simultaneous measurements of PSR J0030+0451 and PSR J0740+6620 from NICER, and the neutron-skin thickness of $^{208}$Pb from both PREX-II measurement and ab initio calculations. Our results show that, compared with the RMF model, QMF model's direct coupling of quarks with mesons and gluons leads to the evolution of the in-medium nucleon mass with the quark mass correction. This feature enables QMF model a wider range of model applicability, as shown by a slow drop of the nucleon mass with density and a large value at saturation that is jointly constrained by nuclear physics and astronomy., Comment: 14 pages, 4 figures, 3 tables, Phys. Rev. C (2023) accepted

It is of great interest to understand the equation of state (EOS) of the neutron star (NS), whose core includes highly dense matter. However, there are large uncertainties in the theoretical predictions for the EOS of NS. It is useful to develop a new framework, which is flexible enough to consider the systematic error in theoretical predictions and to use them as a best guess at the same time. We employ a deep neural network to perform a non-parametric fit of the EOS of NS using currently available data. In this framework, the Gaussian process is applied to represent the EOSs and the training set data required to close physical solutions. Our model is constructed under the assumption that the true EOS of NS is a perturbation of the relativistic mean-field model prediction. We fit the EOSs of NS using two different example datasets, which can satisfy the latest constraints from the massive neutron stars, NICER, and the gravitational wave of the binary neutron stars. Given our assumptions, we find that a maximum neutron star mass is $2.38^{+0.15}_{-0.13} M_\odot$ or $2.41^{+0.15}_{-0.14}$ at $95\%$ confidence level from two different example datasets. It implies that the $1.4 M_\odot$ radius is $12.31^{+0.29}_{-0.31}$ km or $12.30^{+0.35}_{-0.37}$ km. These results are consistent with results from previous studies using similar priors. It has demonstrated the recovery of the EOS of NS using a nonparametric model., Comment: 12 pages, 9 figures, 2 tables, accepted by Astrophysical Journal