pwc-archive/papers-with-abstracts huggingface dataset:
Are Politicians Responsive to Mass Shootings? Evidence from U.S. State Legislatures
Authors: Haotian Chen, Jack Kappelman
Date: 2025-01-02 00:00:00

The United States leads the world in the number of mass shootings that occur each year even as policy making on firearms remains polarized along party lines. Are legislators responsive to mass shootings? We estimate the latent positions of 264 California state legislators on gun policy from their roll-call voting records on firearm-related bills from 2011 to 2022. Employing a stacked difference-in-differences design, we find that mass shootings within or near a state legislator's district do not alter their voting behavior on firearm policy, on average (with 95% confidence intervals spanning opposite directions). When we extend our analyses to an additional 5 states (for a total of 1,864 legislators), we find similarly null results, though we report evidence of statistically significant effects in both directions from event-specific estimates. Our findings suggest that even the most heinous acts of mass violence generally fail to produce measurable effects on legislators' positions on firearm-related policy.
Paternalism and Deliberation: An Experiment on Making Formal Rules
Authors: Max R. P. Grossmann
Date: 2025-01-01 00:00:00

This paper studies the relationship between soft and hard paternalism by examining two kinds of restriction: a waiting period and a hard limit (cap) on risk-seeking behavior. Mandatory waiting periods have been instituted for medical procedures, gun purchases and other high-stakes decisions. Are these policies substitutes for hard restrictions, and are delayed decisions more respected? In an experiment, decision-makers are informed about an impending high-stakes decision. Treatments define when the decision is made: on the spot or after one day, and whether the initial decision can be revised. In a general population survey experiment, another class of subjects (Choice Architects) is granted the opportunity to make rules for decision-makers. Given a decision's temporal structure, Choice Architects can decide on a cap to the decision-maker's risk taking. In another treatment, Choice Architects can implement a mandatory waiting period in addition to the cap. This allows us to study the substitutional relationship between waiting periods and paternalistic action and the effect of deliberation on the autonomy afforded to the decision-maker. Our highly powered experiment reveals that exogenous deliberation has no effect on the cap. Moreover, endogenously prescribed waiting periods represent add-on restrictions that do not substitute for the cap. Choice Architects believe that, with time, the average decision-maker will take less risk and -- because of the distribution of Choice Architects' bliss points -- come closer to Choice Architects' subjective ideal choice. These findings highlight the complementarity of policy tools in targeting various parts of a distribution of decision-makers.
An Evaluation of Borda Count Variations Using Ranked Choice Voting Data
Authors: N. Bradley Fox, Benjamin Bruyns
Date: 2024-12-31 00:00:00

The standard voting methods in the United States, plurality and ranked choice (or instant runoff) voting, are susceptible to significant voting failures. These flaws include Condorcet and majority failures as well as monotonicity and no-show paradoxes. We investigate alternative ranked choice voting systems using variations of the points-based Borda count which avoid monotonicity paradoxes. These variations are based on the way partial ballots are counted and on extending the values of the points assigned to each rank in the ballot. In particular, we demonstrate which voting failures are possible for each variation and then empirically study 421 U.S. ranked choice elections conducted from 2004 to 2023 to determine the frequency of voting failures when using five Borda variations. Our analysis demonstrates that the primary vulnerability of majority failures is rare or nonexistent depending on the variation. Other voting failures such as truncation or compromise failures occur more frequently compared to instant runoff voting as a trade-off for avoiding monotonicity paradoxes.
How Well Did U.S. Rail and Intermodal Freight Respond to the COVID-19 Pandemic vs. the Great Recession?
Authors: Max T. M. Ng, Joseph Schofer, Hani S. Mahmassani
Date: 2024-12-31 00:00:00
Tasks: Sand

This paper analyzes and compares patterns of U.S. domestic rail freight volumes during, and after the disruptions caused by the 2007-2009 Great Recession and the COVID-19 pandemic in 2020. Trends in rail and intermodal shipment data are examined in conjunction with economic indicators, focusing on the extent of drop and recovery of freight volumes of various commodities and intermodal shipments, and the lead/lag time with respect to economic drivers. While impacts from and the rebound from the Great Recessions were slow to develop, COVID-19 produced both profound disruptions in the freight market and rapid rebound, with important variations across commodity types. Energy-related commodities (i.e., coal, petroleum, and fracking sand), dropped during the pandemic while demand for other commodities (i.e., grain products and lumber, and intermodal freight). rebounded rapidly and in some cases grew. Overall rail freight experienced a rapid rebound following the precipitous drop in traffic in March and April 2020, achieving a near-full recovery in five months. As the recovery proceeded through 2020, intermodal flow, containers moving by rail for their longest overland trips, rebounded strongly, some exceeding 2019 levels. In contrast, rail flows during the Great Recession changed slowly with the onset and recovery, extending over multiple years. Pandemic response reflected the impacts of quick shutdowns and a rapid shift in consumer purchasing patterns. Results for the pandemic illustrate the resilience of U.S. rail freight industry and the multifaceted role it plays in the overall logistics system. Amid a challenging logistical environment, freight rail kept goods moving when other methods of transport were constrained.
European Defence Readiness
Authors: d'Artis Kancs
Date: 2024-12-28 00:00:00

The preparedness and readiness of Europe is currently being challenged not only by Russia, but since recently also by its long-standing allies. In response to the evolving external security environment, the EU's White Paper on European Defence Readiness 2030 outlines the key defence issues in Europe - including critical capability gaps of forces, challenges of the defence industry such as fragmented defence market and military mobility. Leveraging an empirically validated general equilibrium multi-sector model with ambiguity and risk, this study assesses how prepared is Europe to address protracted conflicts and systemic shocks. Exploring what strategies could enhance European readiness, scenario analysis of a hypothetical total trade war imply that today's existing problems will only be amplified by systemic shocks, if not addressed timely and in a targeted way.
Robust Intervention in Networks
Authors: Daeyoung Jeong, Tongseok Lim, Euncheol Shin
Date: 2024-12-31 00:00:00
Tasks: Decision Making

In economic settings such as learning, social behavior, and financial contagion, agents interact through interdependent networks. This paper examines how a decision maker (DM) can design an optimal intervention strategy under network uncertainty, modeled as a zero-sum game against an adversarial ``Nature'' that reconfigures the network within an uncertainty set. Using duality, we characterize the DM's unique robust intervention and identify the worst-case network structure, which exhibits a rank-1 property, concentrating risk along the intervention strategy. We analyze the costs of robustness, distinguishing between global and local uncertainty, and examine the role of higher-order uncertainties in shaping intervention outcomes. Our findings highlight key trade-offs between maximizing influence and mitigating uncertainty, offering insights into robust decision-making. This framework has applications in policy design, economic regulation, and strategic interventions in dynamic networks, ensuring their resilience against uncertainty in network structures.
VoiceVector: Multimodal Enrolment Vectors for Speaker Separation
Authors: Akam Rahimi, Triantafyllos Afouras, Andrew Zisserman
Date: 2025-01-02 00:00:00
Tasks: Speaker Separation

We present a transformer-based architecture for voice separation of a target speaker from multiple other speakers and ambient noise. We achieve this by using two separate neural networks: (A) An enrolment network designed to craft speaker-specific embeddings, exploiting various combinations of audio and visual modalities; and (B) A separation network that accepts both the noisy signal and enrolment vectors as inputs, outputting the clean signal of the target speaker. The novelties are: (i) the enrolment vector can be produced from: audio only, audio-visual data (using lip movements) or visual data alone (using lip movements from silent video); and (ii) the flexibility in conditioning the separation on multiple positive and negative enrolment vectors. We compare with previous methods and obtain superior performance.
Time Difference of Arrival Source Localization: Exact Linear Solutions for the General 3D Problem
Authors: Niraj K. Inamdar
Date: 2025-01-02 00:00:00

The time difference of arrival (TDOA) problem admits exact, purely algebraic solutions for the situation in which there are 4 and 5 sensors and a single source whose position is to be determined in 3 dimensions. The solutions are exact in the sense that there is no least squares operation (i.e., projection) involved in the solution. The solutions involve no linearization or iteration, and are algebraically transparent via vector algebra in Cartesian coordinates. The solution with 5 sensors requires no resolution of sign ambiguities; the solution with 4 sensors requires resolution of one sign ambiguity. Solutions are effected using only TDOA and not, e.g., frequency difference of arrival (FDOA) or angle of arrival (AOA). We first present the 5-sensor solution and then follow with the 4-sensor scenario. Numerical experiments are presented showing the performance of the calculations in the case of no noise, before closing with conclusions. Performance of the calculations is exact within numerical error, and in the small fraction of cases in which source localization does not occur, it is driven by misidentification in resolution of sign ambiguity without priors. We therefore believe the calculations have substantial practical utility for their speed and exactness.
Towards Intelligent Antenna Positioning: Leveraging DRL for FAS-Aided ISAC Systems
Authors: Shunxing Yang, Junteng Yao, Jie Tang, Tuo Wu, Maged Elkashlan, Chau Yuen, Merouane Debbah, Hyundong Shin, Matthew Valenti
Date: 2025-01-02 00:00:00
Tasks: Deep Reinforcement Learning, Integrated sensing and communication, ISAC

Fluid antenna systems (FAS) enable dynamic antenna positioning, offering new opportunities to enhance integrated sensing and communication (ISAC) performance. However, existing studies primarily focus on communication enhancement or single-target sensing, leaving multi-target scenarios underexplored. Additionally, the joint optimization of beamforming and antenna positions poses a highly non-convex problem, with traditional methods becoming impractical as the number of fluid antennas increases. To address these challenges, this letter proposes a block coordinate descent (BCD) framework integrated with a deep reinforcement learning (DRL)-based approach for intelligent antenna positioning. By leveraging the deep deterministic policy gradient (DDPG) algorithm, the proposed framework efficiently balances sensing and communication performance. Simulation results demonstrate the scalability and effectiveness of the proposed approach.
Movable Antenna-Assisted Integrated Sensing and Communication Systems
Authors: Chengjun Jiang, Chensi Zhang, Chongwen Huang, Jianhua Ge, Dusit Niyato, Chau Yuen
Date: 2025-01-02 00:00:00
Tasks: Integrated sensing and communication, Position

Movable antennas (MAs) enhance flexibility in beamforming gain and interference suppression by adjusting position within certain areas of the transceivers. In this paper, we propose an MA-assisted integrated sensing and communication framework, wherein MAs are deployed for reconfiguring the channel array responses at both the receiver and transmitter of a base station. Then, we develop an optimization framework aimed at maximizing the sensing signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the receive beamforming vector, the transmit beamforming matrix, and the positions of MAs while meeting the minimum SINR requirement for each user. To address this nonconvex problem involving complex coupled variables, we devise an alternating optimization-based algorithm that incorporates techniques including the Charnes-Cooper transform, second-order Taylor expansion, and successive convex approximation (SCA). Specifically, the closed form of the received vector and the optimal transmit matrix can be first obtained in each iteration. Subsequently, the solutions for the positions of the transmit and receive MAs are obtained using the SCA method based on the second-order Taylor expansion. The simulation results show that the proposed scheme has significant advantages over the other baseline schemes. In particular, the proposed scheme has the ability to match the performance of the fixed position antenna scheme while utilizing fewer resources.
ROME: Robust Model Ensembling for Semantic Communication Against Semantic Jamming Attacks
Authors: Kequan Zhou, Guangyi Zhang, Yunlong Cai, Qiyu Hu, Guanding Yu
Date: 2025-01-02 00:00:00
Tasks: Semantic Communication

Recently, semantic communication (SC) has garnered increasing attention for its efficiency, yet it remains vulnerable to semantic jamming attacks. These attacks entail introducing crafted perturbation signals to legitimate signals over the wireless channel, thereby misleading the receivers' semantic interpretation. This paper investigates the above issue from a practical perspective. Contrasting with previous studies focusing on power-fixed attacks, we extensively consider a more challenging scenario of power-variable attacks by devising an innovative attack model named Adjustable Perturbation Generator (APG), which is capable of generating semantic jamming signals of various power levels. To combat semantic jamming attacks, we propose a novel framework called Robust Model Ensembling (ROME) for secure semantic communication. Specifically, ROME can detect the presence of semantic jamming attacks and their power levels. When high-power jamming attacks are detected, ROME adapts to raise its robustness at the cost of generalization ability, and thus effectively accommodating the attacks. Furthermore, we theoretically analyze the robustness of the system, demonstrating its superiority in combating semantic jamming attacks via adaptive robustness. Simulation results show that the proposed ROME approach exhibits significant adaptability and delivers graceful robustness and generalization ability under power-variable semantic jamming attacks.
Fundamental MMSE-Rate Performance Limits of Integrated Sensing and Communication Systems
Authors: Zijie Wang, Xudong Wang
Date: 2025-01-02 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) demonstrates promise for 6G networks; yet its performance limits, which require addressing functional Pareto stochastic optimizations, remain underexplored. Existing works either overlook the randomness of ISAC signals or approximate ISAC limits from sensing and communication (SAC) optimum-achieving strategies, leading to loose bounds. In this paper, ISAC limits are investigated by considering a random ISAC signal designated to simultaneously estimate the sensing channel and convey information over the communication channel, adopting the modified minimum-mean-square-error (MMSE), a metric defined in accordance with the randomness of ISAC signals, and the Shannon rate as respective SAC metrics. First, conditions for optimal channel input and output distributions on the MMSE-Rate limit are derived employing variational approaches, leading to high-dimensional convolutional equations. Second, leveraging variational conditions, a Blahut-Arimoto-type algorithm is proposed to numerically determine optimal distributions and SAC performance, with its convergence to the limit proven. Third, closed-form SAC-optimal waveforms are derived, characterized by power allocation according to channel statistics/realization and waveform selection; existing methods to establish looser ISAC bounds are rectified. Finally, a compound signaling strategy is introduced for coincided SAC channels, which employs sequential SAC-optimal waveforms for channel estimation and data transmission, showcasing significant rate improvements over non-coherent "capacity". This study systematically investigates ISAC performance limits from joint estimation- and information-theoretic perspectives, highlighting key SAC tradeoffs and potential ISAC design benefits. The methodology readily extends to various metrics, such as estimation rate and the Cramer-Rao Bound.
Power-Efficient Optimization for Coexisting Semantic and Bit-Based Users in NOMA Networks
Authors: Ximing Xie, Fang Fang, Lan Zhang, Xianbin Wang
Date: 2025-01-02 00:00:00
Tasks: Semantic Communication

Semantic communications, which focus on transmitting the semantic meaning of data, have been proposed as a novel paradigm for achieving efficient and relevant communication. Meanwhile, non-orthogonal multiple access (NOMA) enhances spectral efficiency by allowing multiple users to share the same spectrum. However, semantic communications are unlikely to fully replace conventional bit-level communications in the near future, as the latter remain dominant. Therefore, integrating semantic users into a NOMA network alongside conventional bit-based users becomes a meaningful approach to improve both transmission and spectrum efficiency. Nonetheless, due to the lack of a mathematical model that accurately characterizes the relationship between the performance of semantic transceivers and wireless resource allocation, enhancing performance through resource optimization remains a challenge. Moreover, successive interference cancellation (SIC), a key technique in NOMA, introduces additional complexity in system design and implementation. To address these challenges, this paper first improves the deep semantic communication (DeepSC) transceiver to make it adaptive to varying wireless transmission conditions. Subsequently, a data-driven regression approach is employed to develop a mathematical model that captures the impact of wireless resources on semantic transceiver performance. In parallel, a multi-cluster hybrid NOMA (H-NOMA) framework is proposed, where each cluster consists of one semantic user and one bit-based user, to mitigate the complexity introduced by SIC. A total transmit power minimization problem is then formulated by jointly optimizing the beamforming design, bandwidth allocation, and semantic symbol factor.
Beamforming Design for Wideband Near-Field Communications With Reconfigurable Refractive Surfaces
Authors: Zicheng Lin, Shuhao Zeng, Aryan Kaushik, Hongliang Zhang
Date: 2025-01-02 00:00:00

To meet the growing demand for high data rates, cellular systems are expected to evolve towards higher carrier frequencies and larger antenna arrays, but conventional phased arrays face challenges in supporting such a prospection due to their excessive power consumption induced by numerous phase shifters required. Reconfigurable Refractive Surface (RRS) is an energy efficient solution to address this issue without relying on phase shifters. However, the increased radiation aperture size extends the range of the Fresnel region, leading the users to lie in the near-field zone. Moreover, given the wideband communications in higher frequency bands, we cannot ignore the frequency selectivity of the RRS. These two effects collectively exacerbate the beam-split issue, where different frequency components fail to converge on the user simultaneously, and finally result in a degradation of the data rate. In this paper, we investigate an RRS-based wideband near-field multi-user communication system. Unlike most existing studies on wideband communications, which consider the beam-split effect only with the near-field condition, we study the beam-split effect under the influence of both the near-field condition and the frequency selectivity of the RRS. To mitigate the beam-split effect, we propose a Delayed-RRS structure, based on which a beamforming scheme is proposed to optimize the user's data rate. Through theoretical analysis and simulation results, we analyze the influence of the RRS's frequency selectivity, demonstrate the effectiveness of the proposed beamforming scheme, and reveal the importance of jointly considering the near-field condition and the frequency selectivity of RRS.
Confined Orthogonal Matching Pursuit for Sparse Random Combinatorial Matrices
Authors: Xinwei Zhao, Jinming Wen, Hongqi Yang, Xiao Ma
Date: 2025-01-02 00:00:00

Orthogonal matching pursuit (OMP) is a commonly used greedy algorithm for recovering sparse signals from compressed measurements. In this paper, we introduce a variant of the OMP algorithm to reduce the complexity of reconstructing a class of $K$-sparse signals $\boldsymbol{x} \in \mathbb{R}^{n}$ from measurements $\boldsymbol{y} = \boldsymbol{A}\boldsymbol{x}$, where $\boldsymbol{A} \in \{0,1\}^{m \times n}$ is a sparse random combinatorial matrix with $d~(d \leq m/2)$ ones per column. The proposed algorithm, referred to as the confined OMP algorithm, utilizes the properties of $\boldsymbol{x}$ and $\boldsymbol{A}$ to remove much of the redundancy in the dictionary (also referred to as $\boldsymbol{A}$) and thus fewer column indices of $\boldsymbol{A}$ need to be identified. To this end, we first define a confined set $\Gamma$ with $|\Gamma| \leq n$ and then prove that the support of $\boldsymbol{x}$ is a subset of $\Gamma$ with probability 1 if the distributions of non-zero components of $\boldsymbol{x}$ satisfy a certain condition. During the process of the confined OMP algorithm, the possibly chosen column indices are strictly confined into the confined set $\Gamma$. We further develop lower bounds on the probability of exact recovery of $\boldsymbol{x}$ using OMP algorithm and confined OMP algorithm with $K$ iterations, respectively. The obtained theoretical results of confined OMP algorithm can be used to optimize the column degree $d$ of $\boldsymbol{A}$. Finally, experimental results show that the confined OMP algorithm is more efficient in reconstructing a class of sparse signals compared to the OMP algorithm.