We construct geometric microstates for a class of two-dimensional flow geometries$-$spacetimes that interpolate from an asymptotic AdS$_2$ boundary to a dS$_2$ static patch in the interior$-$by inserting particles behind the horizon. We show that this mechanism produces dS microstates with an Einstein-Rosen bridge of infinite length behind the horizon. The state-counting of these microstates, including wormhole contributions, reproduces the Gibbons-Hawking entropy, $S_{\rm dS}=A^{\rm dS}_{\rm horizon}/4G$. Furthermore, we extend the microstate-counting method to the case of a finite-length Einstein-Rosen bridge. As a result, the Hilbert space of the dS horizon in the flow geometry can be spanned by states with a purely dS Einstein-Rosen bridge, containing no AdS portion on the time-symmetric slice. This provides a concrete realization of dS microstates within a controlled holographic framework.

The integration of Large Language Models (LLMs) into computer applications has introduced transformative capabilities but also significant security challenges. Existing safety alignments, which primarily focus on semantic interpretation, leave LLMs vulnerable to attacks that use non-standard data representations. This paper introduces ArtPerception, a novel black-box jailbreak framework that strategically leverages ASCII art to bypass the security measures of state-of-the-art (SOTA) LLMs. Unlike prior methods that rely on iterative, brute-force attacks, ArtPerception introduces a systematic, two-phase methodology. Phase 1 conducts a one-time, model-specific pre-test to empirically determine the optimal parameters for ASCII art recognition. Phase 2 leverages these insights to launch a highly efficient, one-shot malicious jailbreak attack. We propose a Modified Levenshtein Distance (MLD) metric for a more nuanced evaluation of an LLM's recognition capability. Through comprehensive experiments on four SOTA open-source LLMs, we demonstrate superior jailbreak performance. We further validate our framework's real-world relevance by showing its successful transferability to leading commercial models, including GPT-4o, Claude Sonnet 3.7, and DeepSeek-V3, and by conducting a rigorous effectiveness analysis against potential defenses such as LLaMA Guard and Azure's content filters. Our findings underscore that true LLM security requires defending against a multi-modal space of interpretations, even within text-only inputs, and highlight the effectiveness of strategic, reconnaissance-based attacks. Content Warning: This paper includes potentially harmful and offensive model outputs.

In the limit of infinite radius de Sitter space becomes locally flat and the static patch tends to Rindler space. A holographic description of the static patch must result in a holographic description of some flat space theory, expressed in Rindler coordinates. Given such a holographic theory how does one decode the hologram and determine the bulk flat space theory, its particle spectrum, forces, and bulk quantum fields? In this paper we will answer this question for a particular case: DSSYK at infinite temperature and show that the bulk theory is a strongly coupled version of the 't Hooft model, i.e., (1+1)-dimensional QCD, with a single quark flavor. It may also be thought of as an open string theory with mesons lying on a single Regge trajectory.

Accurately estimating human skill levels is crucial for designing effective human-AI interactions so that AI can provide appropriate challenges or guidance. In games where AI players have beaten top human professionals, strength estimation plays a key role in adapting AI behavior to match human skill levels. In a previous state-of-the-art study, researchers have proposed a strength estimator trained using human players' match data. Given some matches, the strength estimator computes strength scores and uses them to estimate player ranks (skill levels). In this paper, we focus on the observation that human players' behavior tendency varies according to their strength and aim to improve the accuracy of strength estimation by taking this into account. Specifically, in addition to strength scores, we obtain policies for different skill levels from neural networks trained using human players' match data. We then combine features based on these policies with the strength scores to estimate strength. We conducted experiments on Go and chess. For Go, our method achieved an accuracy of 80% in strength estimation when given 10 matches, which increased to 92% when given 20 matches. In comparison, the previous state-of-the-art method had an accuracy of 71% with 10 matches and 84% with 20 matches, demonstrating improvements of 8-9%. We observed similar improvements in chess. These results contribute to developing a more accurate strength estimation method and to improving human-AI interaction.

The motion of a spinning particle in the exterior of the Kerr-Newman black hole is studied. The dynamics is governed by the Mathisson-Papapetrou equations in the pole-dipole approximation through the spin-curvature coupling to the leading order in its spin. In terms of conserved quantities, one can transform the dynamical equations in the Mino time into an integral form for both aligned and misaligned spins with orbital motion. These non-geodesic equations can be solved analytically with the solutions involving Jacobi elliptic functions. The radial potential can be derived in order to study the parameter space of the particle for various types of orbit, based on its roots obtained with the corrections of the particle's spin. We consider motion oscillating around two turning points, which are the two outermost roots of the radial potential on the equatorial plane in the misaligned case. In this case, there is an induced oscillatory motion out of the equatorial plane. In particular, the oscillation periods of the motion are obtained. When the orbits become a source of gravitational wave emission, these periods of motion will play a key role in determining the gravitational waves in the frequency domain. Numerical kludge waveforms are constructed. The gravitational wave amplitudes are found to be sensitive to the turning points of the orbits as measured from the black holes. The implications for gravitational wave emission due to extreme mass-ratio inspirals (EMRIs) are discussed.

We pose the research question, "Can LLMs provide credible evaluation scores, suitable for constructing starter MCDM models that support commencing deliberation regarding climate and sustainability policies?" In this exploratory study we i. Identify a number of interesting policy alternatives that are actively considered by local governments in the United States (and indeed around the world). ii. Identify a number of quality-of-life indicators as apt evaluation criteria for these policies. iii. Use GPT-4 to obtain evaluation scores for the policies on multiple criteria. iv. Use the TOPSIS MCDM method to rank the policies based on the obtained evaluation scores. v. Evaluate the quality and validity of the resulting table ensemble of scores by comparing the TOPSIS-based policy rankings with those obtained by an informed assessment exercise. We find that GPT-4 is in rough agreement with the policy rankings of our informed assessment exercise. Hence, we conclude (always provisionally and assuming a modest level of vetting) that GPT-4 can be used as a credible input, even starting point, for subsequent deliberation processes on climate and sustainability policies.

The existence and uniqueness of a steady state for nonequilibrium systems (NESS) is a fundamental subject and a main theme of research in statistical mechanics for decades. For Gaussian systems, such as a chain of harmonic oscillators connected at each end to a heat bath, and for anharmonic oscillators under specified conditions, definitive answers exist in the form of proven theorems. Answering this question for quantum many-body systems poses a challenge for the present. In this work we address this issue by deriving the stochastic equations for the reduced system with self-consistent backaction from the two baths, calculating the energy flow from one bath to the chain to the other bath, and exhibiting a power balance relation in the total (chain + baths) system which testifies to the existence of a NESS in this system at late times. Its insensitivity to the initial conditions of the chain corroborates to its uniqueness. The functional method we adopt here entails the use of the influence functional, the coarse-grained and stochastic effective actions, from which one can derive the stochastic equations and calculate the average values of physical variables in open quantum systems. This involves both taking the expectation values of quantum operators of the system and the distributional averages of stochastic variables stemming from the coarse-grained environment. This method though formal in appearance is compact and complete. It can also easily accommodate perturbative techniques and diagrammatic methods from field theory. Taken all together it provides a solid platform for carrying out systematic investigations into the nonequilibrium dynamics of open quantum systems and quantum thermodynamics.

Direct searches of dark matter candidates with mass energies less than 1 GeV is an active research field. The energy depositions are comparable to the scale of atomic, molecular, or condensed matter systems, therefore many-body physics plays an important role in understanding the detector's response in dark matter scattering. We present in this work a comprehensive data set of atomic response functions for xenon and germanium with 12.2 and 80 eV energy thresholds, respectively, using the (multiconfiguration) relativistic random phase approximation. This approach takes into account the relativistic, exchange, and correlation effects in one self-consistent framework, and is benchmarked successfully by photoabsorption data from thresholds to 30 keV with $\lesssim5\%$ errors. Comparisons with our previous and some other independent particle approaches in literature are made. It is also found that the spin-dependent (SD) response has significant difference from the spin-independent (SI) one such that the dark matter SD and SI interactions with electrons can be distinguished in unpolarized scattering, which is typical for direct search detectors. Finally, the exclusion limits set by current experiments are updated with our new results.

Detectors with low thresholds for electron recoil open a new window to direct searches of sub-GeV dark matter (DM) candidates. In the past decade, many strong limits on DM-electron interactions have been set, but most on the one which is spin-independent (SI) of both dark matter and electron spins. In this work, we study DM-atom scattering through a spin-dependent (SD) interaction at leading order (LO), using well-benchmarked, state-of-the-art atomic many-body calculations. Exclusion limits on the SD DM-electron cross section are derived with data taken from experiments with xenon and germanium detectors at leading sensitivities. In the DM mass range of 0.1 - 10 GeV, the best limits set by the XENON1T experiment: \sigma_e^{\textrm{(SD)}}<10^{-41}-10^{-40}\,\textrm{cm}^2 are comparable to the ones drawn on DM-neutron and DM-proton at slightly bigger DM masses. The detector's responses to the LO SD and SI interactions are analyzed. In nonrelativistic limit, a constant ratio between them leads to an indistinguishability of the SD and SI recoil energy spectra. Relativistic calculations however show the scaling starts to break down at a few hundreds of eV, where spin-orbit effects become sizable. We discuss the prospects of disentangling the SI and SD components in DM-electron interactions via spectral shape measurements, as well as having spin-sensitive experimental signatures without SI background.

Small primordial black holes could be captured by rocky planets or asteroids, consume their liquid cores from inside and leave hollow structures. We calculate the surface density and surface tension of a hollow structure around a black hole and compare them with the density and compressive strength of various materials that appear in nature to find the allowed parameter space. For example, granite or iron can support a hollow asteroid/planetoid/moon of the size of up to $0.1 R_\oplus$. Along the same lines, future civilizations might build spherical structures around black holes to harvest their energy. Using the strongest material that we currently know how to make (multiwall carbon nanotube), to withstand gravity of one solar mass black hole, the shell must be constructed at distances larger than $10^4 R_\odot$. Alternatively, a fast black hole can leave a narrow tunnel in a solid object while passing through it. For example, a $10^{22}$g black hole should leave a tunnel with a radius of $0.1$ micron, which is large enough to be seen by an optical microscope. We could look for such micro-tunnels here on Earth in very old rocks, or even glass or other solid structures in very old buildings. While our estimate gives a very small probability of finding such tunnels, looking for them does not require expensive equipment and long preparation, and the payoff might be significant.

Network slicing has been considered as one of the key enablers for 5G to support diversified services and application scenarios. This paper studies the distributed network slicing utilizing both the spectrum resource offered by communication network and computational resources of a coexisting fog computing network. We propose a novel distributed framework based on a new control plane entity, regional orchestrator (RO), which can be deployed between base stations (BSs) and fog nodes to coordinate and control their bandwidth and computational resources. We propose a distributed resource allocation algorithm based on Alternating Direction Method of Multipliers with Partial Variable Splitting (DistADMM-PVS). We prove that the proposed algorithm can minimize the average latency of the entire network and at the same time guarantee satisfactory latency performance for every supported type of service. Simulation results show that the proposed algorithm converges much faster than some other existing algorithms. The joint network slicing with both bandwidth and computational resources can offer around 15% overall latency reduction compared to network slicing with only a single resource.

The integration of Large Language Models (LLMs) into computer applications has introduced transformative capabilities but also significant security challenges. Existing safety alignments, which primarily focus on semantic interpretation, leave LLMs vulnerable to attacks that use non-standard data representations. This paper introduces ArtPerception, a novel black-box jailbreak framework that strategically leverages ASCII art to bypass the security measures of state-of-the-art (SOTA) LLMs. Unlike prior methods that rely on iterative, brute-force attacks, ArtPerception introduces a systematic, two-phase methodology. Phase 1 conducts a one-time, model-specific pre-test to empirically determine the optimal parameters for ASCII art recognition. Phase 2 leverages these insights to launch a highly efficient, one-shot malicious jailbreak attack. We propose a Modified Levenshtein Distance (MLD) metric for a more nuanced evaluation of an LLM's recognition capability. Through comprehensive experiments on four SOTA open-source LLMs, we demonstrate superior jailbreak performance. We further validate our framework's real-world relevance by showing its successful transferability to leading commercial models, including GPT-4o, Claude Sonnet 3.7, and DeepSeek-V3, and by conducting a rigorous effectiveness analysis against potential defenses such as LLaMA Guard and Azure's content filters. Our findings underscore that true LLM security requires defending against a multi-modal space of interpretations, even within text-only inputs, and highlight the effectiveness of strategic, reconnaissance-based attacks. Content Warning: This paper includes potentially harmful and offensive model outputs.

Diverse searches for direct dark matter (DM) in effective electromagnetic and leptophilic interactions resulting from new physics, as well as Weakly Interacting Massive Particles (WIMPs) with unconventional electronic recoils, are intensively pursued. Low-energy backgrounds from radioactive $\gamma$ rays via Compton scattering and photon coherent scattering are unavoidable in terrestrial detectors. The interpretation of dark matter experimental data is dependent on a better knowledge of the background in the low-energy region. We provide a 2.3% measurement of atomic Compton scattering in the low momentum transfer range of 180 eV/c to 25 keV/c, using a 10-g germanium detector bombarded by a $^{137}\mathrm{Cs}$ source with a 7.2 m-Curie radioactivity and the scatter photon collected by a cylindrical NaI[Tl] detector. The ability to detect Compton scattering's doubly differential cross section (DDCS) gives a special test for clearly identifying the kinematic restraints in atomic many-body systems, notably the Livermore model. Additionally, a low-energy-background comparison is made between coherent photon scattering and Compton scattering replacing the scattering function of ${GEANT4}$@software, which uses a completely relativistic impulse approximation (RIA) together with Multi-Configuration Dirac-Fock (MCDF) wavefunctions. For the purpose of investigating sub-GeV mass and electronic-recoil dark matter theories, signatures including low energy backgrounds via high energy $\gamma$ rays in germanium targets are discussed.

After decades of experimental efforts, the DAMA/LIBRA(DL) annual modulation (AM) analysis on the $\chi N$ (WIMP Dark Matter interactions on nucleus) channel remains the only one which can be interpreted as positive signatures. This has been refuted by numerous time-integrated (TI) and AM analysis. It has been shown that $\chi e$ (WIMP interactions with electrons) alone is not compatible with the DL AM data. We expand the investigations by performing an AM analysis with the addition of $\chi e$ long-range and short-range interactions to $\chi N$, derived using the Frozen Core Approximation method. Two scenarios are considered, where the $\chi N$ and $\chi e$ processes are due to a single $\chi$ ($\Gamma^{1 \chi}_{tot}$) or two different $\chi$'s ($\Gamma^{2 \chi}_{tot}$). The combined fits with $\chi N$ and $\chi e$ provide stronger significance to the DL AM data which are compatible with the presence of additional physical effects beyond $\chi N$ alone. This is the first analysis which explores how $\chi e$ AM can play a role in DL AM. The revised allowed regions as well as the exclusion contours from the other null AM experiments are presented. All DL AM allowed parameter spaces in $\chi N$ and $\chi e$ channels under both $\Gamma^{1 \chi}_{tot}$ and $\Gamma^{2 \chi}_{tot}$ are excluded at the 90\% confidence level by the combined null AM results. It can be projected that DL-allowed parameter spaces from generic models with interactions induced by two-WIMPs are ruled out.

Scattering of light dark matter (LDM) particles with atomic electrons is studied in the context of effective field theory. Contact and long-range interactions between dark matter and an electron are both considered. A state-of-the-art many-body method is used to evaluate the spin-independent atomic ionization cross sections of LDM-electron scattering, with an estimated error about 20%. New upper limits are derived on parameter space spanned by LDM mass and effective coupling strengths using data from the CDMSlite, XENON10, XENON100, and XENON1T experiments. Comparison with existing calculations shows the importance of atomic structure. Two aspects particularly important are relativistic effect for inner-shell ionization and final-state free electron wave function which sensitively depends on the underlying atomic approaches.

Mining useful patterns from varied types of databases is an important research topic, which has many real-life applications. Most studies have considered the frequency as sole interestingness measure for identifying high quality patterns. However, each object is different in nature. The relative importance of objects is not equal, in terms of criteria such as the utility, risk, or interest. Besides, another limitation of frequent patterns is that they generally have a low occupancy, i.e., they often represent small sets of items in transactions containing many items, and thus may not be truly representative of these transactions. To extract high quality patterns in real life applications, this paper extends the occupancy measure to also assess the utility of patterns in transaction databases. We propose an efficient algorithm named High Utility Occupancy Pattern Mining (HUOPM). It considers user preferences in terms of frequency, utility, and occupancy. A novel Frequency-Utility tree (FU-tree) and two compact data structures, called the utility-occupancy list and FU-table, are designed to provide global and partial downward closure properties for pruning the search space. The proposed method can efficiently discover the complete set of high quality patterns without candidate generation. Extensive experiments have been conducted on several datasets to evaluate the effectiveness and efficiency of the proposed algorithm. Results show that the derived patterns are intelligible, reasonable and acceptable, and that HUOPM with its pruning strategies outperforms the state-of-the-art algorithm, in terms of runtime and search space, respectively.

Visual cryptography scheme (VCS) is an encryption technique that utilizes human visual system in recovering secret image and it does not require any complex calculation. However, the contrast of the reconstructed image could be quite low. A number of reversing-based VCSs (or VCSs with reversing) (RVCS) have been proposed for binary secret images, allowing participants to perform a reversing operation on shares (or shadows). This reversing operation can be easily implemented by current copy machines. Some existing traditional VCS schemes without reversing (nRVCS) can be extended to RVCS with the same pixel expansion for binary image, and the RVCS can achieve ideal contrast, significantly higher than that of the corresponding nRVCS. In the application of greyscale VCS, the contrast is much lower than that of the binary cases. Therefore, it is more desirable to improve the contrast in the greyscale image reconstruction. However, when greyscale images are involved, one cannot take advantage of this reversing operation so easily. Many existing greyscale nRVCS cannot be directly extended to RVCS. In this paper, we first give a new greyscale nRVCS with minimum pixel expansion and propose an optimal-contrast greyscale RVCS (GRVCS) by using basis matrices of perfect black nRVCS. Also, we propose an optimal GRVCS even though the basis matrices are not perfect black. Finally, we design an optimal-contrast GRVCS with minimum number of shares held by each participant. The proposed schemes can satisfy different user requirement, previous RVCSs for binary images can be viewed as special cases in the schemes proposed here.

The measured standard model parameters indicate that we might live in a false Higgs vacuum, possibly with a very long lifetime. However, small black holes can serve as catalysers and significantly speed up the phase transition. In fact, bubbles of true vacuum might already exist in our universe. We calculate the spectrum of Higgs particles produced by such a bubble, and use event generators to study their decay and subsequent evolution of the decay products to obtain the spectrum of emitted photons and neutrinos as a long-range signature. If the propagation of the bubble walls slows down due to interaction with the surrounding matter and plasma, these signals can reach us before the bubble wall hits us, thus representing the signals of the doomsday.

Network slicing has been considered as one of the key enablers for 5G to support diversified IoT services and application scenarios. This paper studies the distributed network slicing for a massive scale IoT network supported by 5G with fog computing. Multiple services with various requirements need to be supported by both spectrum resource offered by 5G network and computational resourc of the fog computing network. We propose a novel distributed framework based on a new control plane entity, federated-orchestrator , which can coordinate the spectrum and computational resources without requiring any exchange of the local data and resource information from BSs. We propose a distributed resource allocation algorithm based on Alternating Direction Method of Multipliers with Partial Variable Splitting . We prove DistADMM-PVS minimizes the average service response time of the entire network with guaranteed worst-case performance for all supported types of services when the coordination between the F-orchestrator and BSs is perfectly synchronized. Motivated by the observation that coordination synchronization may result in high coordination delay that can be intolerable when the network is large in scale, we propose a novel asynchronized ADMM algorithm. We prove that AsynADMM can converge to the global optimal solution with improved scalability and negligible coordination delay. We evaluate the performance of our proposed framework using two-month of traffic data collected in a in-campus smart transportation system supported by a 5G network. Extensive simulation has been conducted for both pedestrian and vehicular-related services during peak and non-peak hours. Our results show that the proposed framework offers significant reduction on service response time for both supported services, especially compared to network slicing with only a single resource.