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Bounds on the Ground State Energy of Quantum p-Spin Hamiltonians
No full textWe consider the problem of estimating the ground state energy of quantum p-local spin glass random Hamiltonians, the quantum analogues of widely studied classical spin glass models. Our main result shows that the maximum energy achievable by product states has a well-defined limit (for even p) as n → ∞ and is E product ∗ = 2 log p in the limit of large p. This value is interpreted as the maximal energy of a much simpler so-called Random Energy Model, widely studied in the setting of classical spin glasses. The proof of the limit existing follows from an extension of Fekete’s Lemma after we demonstrate near super-additivity of the (normalized) quenched free energy. The proof of the value follows from a second moment method on the number of states achieving a given energy when restricting to an ϵ -net of product states. Furthermore, we relate the maximal energy achieved over all states to a p-dependent constant γ p , which is defined by the degree of violation of a certain asymptotic dependence ansatz over graph matchings. We show that the maximal energy achieved by all states E ∗ p in the limit of large n is at most γ p E product ∗ . We also prove using Lindeberg’s interpolation method that the limiting E ∗ p is robust with respect to the choice of the randomness and, for instance, also applies to the case of sparse random Hamiltonians. This robustness in the randomness extends to a wide range of random Hamiltonian models including SYK and random quantum max-cut
Vehicle Routing Problem Formulation for Efficient Tracking of Objects in Low Earth Orbit
No full textAIAA SCITECH 2025 Forum, Session: Spacecraft and Launch Guidance, Navigation, and Control III 6-10 January 2025 Orlando, FLThe increasing number of resident space objects (RSOs) in low Earth orbit (LEO) endangers the sustainable use of space and necessitates continuous surveillance to prevent collisions. The U.S. Space Surveillance Network (SSN) tracks tens of thousands of LEO RSOs using a suite of ground-based sensors; however, the algorithms that task and schedule these sensors have not improved significantly in the last twenty years. In that time, the number of catalogued LEO RSOs has more than doubled, calling for more efficient tasking algorithms. Prior research has primarily focused on improving the tasking of ground-based sensors for tracking RSOs in geosynchronous Earth orbit (GEO). In this paper, we extend recent work on a vehicle routing problem (VRP) formulation for optimal tasking and scheduling of ground-based radars for tracking GEO RSOs and apply it to tracking LEO RSOs. We introduce a modified VRP formulation, which features discrete time indexing and leverages sparse, binary feasibility matrices for reduced computation time, and present results for several simulations. We show that our approach can compute global and regional optima for tracking (a) 100 targets using 4 ground-based sensors over a 5-hour time horizon in under 5 minutes on a laptop computer and (b) 10,000 targets using 27 ground-based sensors over a 24-hour time horizon in about 4 hours on a high-performance computing cluster
High-Throughput Three-Party DPFs with Applications to ORAM and Digital Currencies
No full textCCS ’24, October 14–18, 2024, Salt Lake City, UT, USAspecific and general secure computation. While two-party DPF constructions are readily available for those applications with satisfiable performance, the three-party ones are left behind in both security and efficiency. In this paper we close this gap and propose the first three-party DPF construction that matches the state-of-the-art two-party DPF on all metrics. Namely, it is secure against a malicious adversary corrupting both the dealer and one out of the three evaluators, its function's shares are of the same size and evaluation takes the same time as in the best two-party DPF. Compared to the state-of-the-art three-party DPF, our construction enjoys 40-120× smaller function's share size and shorter evaluation time, for function domains of 216 -240, respectively.
Apart from DPFs as a stand-alone tool, our construction finds immediate applications to private information retrieval (PIR), writing (PIW) and oblivious RAM (ORAM). To further showcase its applicability, we design and implement an ORAM with access policy, an extension to ORAMs where a policy is being checked before accessing the underlying database. The policy we plug-in is the one suitable for account-based digital currencies, and in particular to central bank digital currencies (CBDCs). Our protocol offers the first design and implementation of a large scale privacy-preserving account-based digital currency. While previous works supported anonymity sets of 64-256 clients and less than 10 transactions per second (tps), our protocol supports anonymity sets in the millions, performing {500,200,58} tps for anonymity sets of {216, 218, 220}, respectively.
Toward that application, we introduce a new primitive called updatable DPF, which enables a direct computation of a dot product between a DPF and a vector; we believe that updatable DPF and the new dot-product protocol will find interest in other applications
Multi-Proxy Records of Climate and Carbon Cycle Perturbations in the Paleozoic: Integrating Isotope Geochemistry and Sedimentology
No full textCarbonate rocks are a valuable archive of past environmental conditions. To glean robust information from this archive, we must understand how carbonate sediments form, ensure our analytical techniques are optimized, and consider how inherently local deposition of sediments can communicate information about global changes in climate. Chapter 1 proposes a new conceptual model for the formation of ooids that suggests that these small carbonate grains could form while buried in the shallow sediment pile during certain intervals of Earth history. Chapters 2 and 3 calibrate the clumped isotope paleothermometer for calcite, dolomite, and apatite, resolving significant discrepancies in calculated paleotemperatures. Chapter 4 applies clumped isotope thermometry to Early Mississippian strata and demonstrates a ~5ºC global cooling and substantial ice volume expansion coincident with a major perturbation to the global carbon cycle. Chapter 5 examines the extent to which diagenesis and facies- and phase-specific effects drive a major Early Mississippian carbon isotope excursion. In aggregate, this thesis outlines a roadmap for assessing changes to climate and the carbon cycle for carbonate rocks in the Paleozoic.Ph.D
Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study
No full textBackground:
Empathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds toward stories from human versus AI narrators and how transparency plays a role in user emotions.
Objective:
We aim to understand how empathy shifts across human-written versus AI-written stories, and how these findings inform ethical implications and human-centered design of using mental health chatbots as objects of empathy.
Methods:
We conducted crowd-sourced studies with 985 participants who each wrote a personal story and then rated empathy toward 2 retrieved stories, where one was written by a language model, and another was written by a human. Our studies varied disclosing whether a story was written by a human or an AI system to see how transparent author information affects empathy toward the narrator. We conducted mixed methods analyses: through statistical tests, we compared user’s self-reported state empathy toward the stories across different conditions. In addition, we qualitatively coded open-ended feedback about reactions to the stories to understand how and why transparency affects empathy toward human versus AI storytellers.
Results:
We found that participants significantly empathized with human-written over AI-written stories in almost all conditions, regardless of whether they are aware (t196=7.07, P<.001, Cohen d=0.60) or not aware (t298=3.46, P<.001, Cohen d=0.24) that an AI system wrote the story. We also found that participants reported greater willingness to empathize with AI-written stories when there was transparency about the story author (t494=–5.49, P<.001, Cohen d=0.36).
Conclusions:
Our work sheds light on how empathy toward AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of empathetic artificial social support or mental health chatbots
A hypergraph model shows the carbon reduction potential of effective space use in housing
No full textHumans spend over 90% of their time in buildings, which account for 40% of anthropogenic greenhouse gas emissions and are a leading driver of climate change. Incentivizing more sustainable construction, building codes are used to enforce indoor comfort standards and minimum energy efficiency requirements. However, they currently only reward measures such as equipment or envelope upgrades and disregard the actual spatial configuration and usage. Using a new hypergraph model that encodes building floorplan organization and facilitates automatic geometry creation, we demonstrate that space efficiency outperforms envelope upgrades in terms of operational carbon emissions in 72%, 61% and 33% of surveyed buildings in Zurich, New York, and Singapore. Using automatically generated floorplans in a case study in Zurich further increased access to daylight by up to 24%, revealing that auto-generated floorplans have the potential to improve the quality of residential spaces in terms of environmental performance and access to daylight
Crack densification in drying colloidal suspensions
No full textAs sessile drops of aqueous colloidal suspensions dry, a close-packed particle deposit forms that grows from the edge of the drop toward the center. To compensate for evaporation over the solid’s surface, water flows radially through the deposit, generating a negative pore pressure in the deposit associated with tensile drying stresses that induce the formation of cracks. As these stresses increase during drying, existing cracks propagate and additional cracks form, until the crack density eventually saturates. We rationalize the dynamics of crack propagation and crack densification with a local energy balance between the elastic energy released by the crack, the energetic cost of fracture, and the elastic energy released by previously formed cracks. We show that the final spacing between radial cracks is proportional to the local thickness of the deposit, while the aspect ratio of the crack segments depends on the shape of the deposit
High-level specification and efficient implementation of pipelined circuits
No full text© 2001 IEEE. This paper describes a novel approach to high-level synthesis of complex pipelined circuits, including pipelined circuits with feedback. This approach combines a high-level, modular specification language with an efficient implementation. In our system, the designer specifies the circuit as a set of independent modules connected by conceptually unbounded queues. Our synthesis algorithm automatically transforms this modular, asynchronous specification into a tightly coupled, fully synchronous implementation in synthesizable Verilog
Automated and Blind Detection of Low Probability of Intercept RF Anomaly Signals
No full textACM MobiCom ’24, November 18–22, 2024, Washington D.C., DC, USAAutomated spectrum monitoring necessitates the accurate detection of low probability of intercept (LPI) radio frequency (RF) anomaly signals to identify unwanted interference in wireless networks. However, detecting these unforeseen low-power RF signals is fundamentally challenging due to the scarcity of labeled RF anomaly data. In this paper, we introduce WANDA (Wireless ANomaly Detection Algorithm), an automated framework designed to detect LPI RF anomaly signals in low signal-to-interference ratio (SIR) environments without relying on labeled data. WANDA operates through a two-step process: (i) Information extraction, where a convolutional neural network (CNN) utilizing soft Hirschfeld-Gebelein-Rényi correlation (HGR) as the loss function extracts informative features from RF spectrograms; and (ii) Anomaly detection, where the extracted features are applied to a one-class support vector machine (SVM) classifier to infer RF anomalies. To validate the effectiveness of WANDA, we present a case study focused on detecting unknown Bluetooth signals within the WiFi spectrum using a practical dataset. Experimental results demonstrate that WANDA outperforms other methods in detecting anomaly signals across a range of SIR values (-10 dB to 20 dB)