When a player withdraws mid-tournament from a round-robin chess event, organizers face a fundamental problem: how should scores be assigned for games that were never played? Current FIDE guidelines specify annulment if withdrawal occurs before 50% of games are completed, and forfeit (awarding unplayed opponents a full point) thereafter. This dichotomous rule creates arbitrary discontinuities and can substantially distort final standings. We develop a Bayesian framework based on best linear unbiased prediction (BLUP) that optimally combines pre-tournament ratings with observed performance, producing imputed scores that reflect both the withdrawn player's current form and the strength differentials among unplayed opponents. The estimator is consistent, point-conserving, and minimizes mean squared error among linear unbiased predictors. A Monte Carlo simulation study on 180,000 simulated tournaments demonstrates that Bayesian BLUP imputation reduces prediction error by 26% overall compared to FIDE's current rule, with improvements of 41% over forfeit and 12% over annulment. The largest gains occur when the withdrawn player is underperforming, the most common withdrawal scenario. We fu
Nociception is a protective biological mechanism that links harmful stimulation to a reaction. This paper investigates artificial nociception for a robotic arm with whole-body tactile sensing. We present a complete pipeline that maps pressure changes from sensitive skin on a robot manipulator to bio-inspired withdrawal motions. The system first converts skin pressure into a scalar pain gain using a nonlinear continuous model. We compare three reflexes: (i) uniform reflex moves four robot joints by a fixed amount, whereby the withdrawal is approximated by a movement of the arm "toward the base", independent of where the robot was touched; (ii) biologically motivated location-dependent joint-space withdrawal derived from human withdrawal reflex characteristics; (iii) Cartesian space withdrawal along the surface normal of the contacted skin pad. All behaviors are integrated in a reflex controller that interrupts the task, executes the withdrawal, and returns to a pre-contact pose. A user study with 15 participants compared the strategies using Godspeed questionnaire subscales, custom perceived-naturalness and safety items, forced-choice comparisons, and qualitative feedback. Interesti
LLMs have become deeply embedded in knowledge work, raising concerns about growing dependency and the potential undermining of human skills. To investigate the pervasiveness of LLMs in work practices, we conducted a four-day diary study with frequent LLM users (N=10), observing how knowledge workers responded to a temporary withdrawal of LLMs. Our findings show how LLM withdrawal disrupted participants' workflows by identifying gaps in task execution, how self-directed work led participants to reclaim professional values, and how everyday practices revealed the extent to which LLM use had become inescapably normative. Conceptualizing LLMs as infrastructural to contemporary knowledge work, this research contributes empirical insights into the often invisible role of LLMs and proposes value-driven appropriation as an approach to supporting professional values in the current LLM-pervasive work environment.
The continued improvement of large language models (LLMs) increasingly depends on eliciting high-quality, user-generated data, yet such data are costly to provide and often withheld due to privacy and effort concerns. This creates a fundamental design challenge: how to incentivize data contribution when model improvements require coordinated, threshold-level inputs, while contributions remain privately costly and partially reversible. We develop and theoretically analyze incentive mechanisms for user data contribution that explicitly account for threshold effects and reversibility, focusing on how subsidies and withdrawal rights can be jointly designed to overcome coordination failure. As a natural benchmark, we first consider subsidy-based incentives, under which users respond to posted payments with privately optimal floor contributions. These decentralized responses may fall below the improvement threshold, resulting in subsidy expenditure without model improvements. We then analyze mechanisms with withdrawal rights, in which users report costs, the provider centrally assigns contribution burdens, and users may withdraw before training. We prove that combining cost reporting wit
This article presents an extension of the work performed by Liu, Baek and Susilo on withdrawable signatures to the Fiat-Shamir with aborts paradigm. We introduce an abstract construction, and provide security proofs for this proposal. As an instantiation, we provide a concrete withdrawable signature scheme based on a no-hint, full-t Dilithium-style Fiat-Shamir with aborts construction; adapting to production ML-DSA (with hints) introduces a small epsilon term.
Social robots for children with autism are often evaluated through engagement and interaction quality, assuming the robot acts as a social scaffold. We report a mixed-methods "withdrawal" study that tests a harder question: what changes when the robot is removed. In an 8-week home-based randomized controlled trial (N=40), children either retained a consumer social robot (Qrobot) or had it withdrawn after initial use. Quantitatively, continued access reduced anxiety (SCARED/RCADS), yet was associated with lower parent-reported social motivation and weaker gains in emotion recognition (SMS/RMET) compared to withdrawal. Interviews with guardians contextualized this divergence: removal sometimes prompted children to seek human interaction, while continued use could keep social behavior siloed within the child-robot dyad, despite exceptionally high usability (SUS). We synthesize a UXR point of view: for vulnerable users, "engagement" can mask ecological downsides. Success should be judged not by retention, but by designed separation that bridges back to human relationships.
Using a mean-field game framework, we study a dynamic model of bank runs in which more withdrawals raise the risk of bank failure. Even though depositors receive gradual and idiosyncratic shocks, withdrawals occur in clusters. The main mechanism is latent fragility: run-prone depositors accumulate gradually over time and may prefer to wait individually, but they withdraw together once collective exit becomes self-fulfilling. We establish equilibrium existence and characterize earliest-run and latest-run equilibria. The clustering mechanism arises whether depositor heterogeneity is discrete or continuous. A common aggregate state coordinates withdrawal timing and leads to a unique threshold equilibrium.
We extend the extended withdrawable signatures of Liu, Susilo and Baek to lattice-based constructions built on the Fiat-Shamir with aborts paradigm. Departing from an earlier draft that transported a per-signer shift in the clear, which leaks the signer, we realise extended withdrawable signatures as a claimable ring signature: signer ambiguity is provided by a one-out-of-N signature used as a black box (anonymity under full key exposure), and confirmation is the signer's claim, a binding signature together with the opening of a hiding index commitment bound into the transcript. No signer-derived value is published in the clear. We give complete proofs of correctness, extended withdrawability (as anonymity-until-claim), unforgeability under insider corruption, and claimability soundness, reducing to decisional MLWE (commitment hiding), MSIS (commitment binding), the anonymity of the one-out-of-$N$ scheme, and the EUF-CMA security of the base signature, in the (quantum) random-oracle model. We instantiate the base signature with a no-hint, full-$t$ Dilithium-style scheme and the one-out-of-$N$ layer with an established lattice one-out-of-many proof.
Selective withdrawal extracts only a single phase from a stratified multi-layer system. Entrainment occurs when a critical condition draws up the static layer which is not being withdrawn. Existing studies provide robust scalings within distinct limiting regimes. These include viscocapillary-dominated entrainment at low Reynolds number. They also include inertia-dominated entrainment at high Reynolds number. However, a single unifying representation remains to be explored in the literature. This limitation is most evident in transitional conditions between classical limits. It is also pronounced when the lower layer is non-Newtonian. Here we report selective-withdrawal experiments spanning these conditions. The upper layer is Newtonian, using PDMS or soybean oil. The lower layer is either Newtonian water or shear-thinning xanthan-gum solutions. We propose a unified framework that connects these previously separated regimes. The framework adopts a ``Moody diagram'' type representation for selective withdrawal. We collapse normalized critical submergence height using a Reynolds-like control parameter. Surface-tension effects enter subdominantly through the capillary length. The resul
We prove that the classical de Bruijn--Newman kernel $K(u)=Φ(|u|)$ is not a Pólya frequency function of order $5$ (PF$_5$). At $(u_0,h)=(0.01,0.05)$ we exhibit an explicit $5\times5$ Toeplitz minor whose determinant is rigorously enclosed in $[-1.8472496\times10^{-9},-1.8472225\times10^{-9}]$. The certificate uses 80-digit outward-rounded interval arithmetic and a proved truncation bound for the theta series. Eight further configurations are certified by both an explicit Leibniz expansion and an independent interval determinant computation. At the central configuration the determinants $D_2,D_3,D_4$ are positive, but this local sign pattern does not establish that the kernel is PF$_4$ globally. We also derive an exact finite formula for the first coefficient permitted by Vandermonde divisibility in the small-spacing expansion of $D_r(u_0,h)$. High-precision observations concerning the sign change of $C_5(u_0)$ and a Gaussian deformation are reported only as non-certified numerics. Version 2 withdraws the certified global sign and unique-threshold claims for $C_5$ made in version 1 because the derivative-tail enclosure was unsound; the direct PF$_5$ counterexample and its interval c
This paper presents an analytical and computational framework for optimizing gas withdrawal in reconstructed ring-type pipeline systems under unsteady flow conditions. As urban and industrial energy demands grow, repurposing existing pipeline infrastructure offers a cost-effective alternative to full-scale expansion. The proposed model identifies the hydraulic coupling point (where the pressure gradient vanishes) as the optimal location for connecting new consumers. By employing a one-dimensional unsteady gas flow model with time-dependent mass extraction represented via a Heaviside step function, the system's dynamic response is captured in detail. Numerical simulations demonstrate that connecting additional loads at the pressure maximum ensures stability while minimizing operational disruptions. The model's validation through benchmark comparison and pressure tolerance thresholds confirms its practical applicability. Economic analysis reveals substantial savings over conventional expansion methods. The approach provides a scalable solution for smart gas network design.
Liquidity withdrawal is a critical indicator of market fragility. In this project, I test a framework for forecasting liquidity withdrawal at the individual-stock level, ranging from less liquid stocks to highly liquid large-cap tickers, and evaluate the relative performance of competing model classes in predicting short-horizon order book stress. We introduce the Liquidity Withdrawal Index (LWI) -- defined as the ratio of order cancellations to the sum of standing depth and new additions at the best quotes -- as a bounded, interpretable measure of transient liquidity removal. Using Nasdaq market-by-order (MBO) data, we compare a spectrum of approaches: linear benchmarks (AR, HAR), and non-linear tree ensembles (XGBoost), across horizons ranging from 250\,ms to 5\,s. Beyond predictive accuracy, our results provide insights into order placement and cancellation dynamics, identify regimes where linear versus non-linear signals dominate, and highlight how early-warning indicators of liquidity withdrawal can inform both market surveillance and execution.
What grounds the rule of thumb that a(n American) retiree can safely withdraw 4% of their initial retirement wealth in their first year of retirement, then increase that rate of consumption with inflation? I address that question with a discrete-time model of returns to a retirement portfolio consumed at a rate that grows by $s$ per period. The model's key parameter is $γ$, an $s$-adjusted rate of return to wealth, derived from the first 2-4 moments of the portfolio's probability distribution of returns; for a retirement lasting $t$ periods the model recommends a rate of consumption of $γ/ (1 - (1 - γ)^t)$. Estimation of $γ$ (and hence of the implied rate of spending in retirement) reveals that the 4% rule emerges from adjusting high expected rates of return down for: consumption growth, the variance in (and kurtosis of) returns to wealth, the longevity risk of a retiree potentially underestimating $t$, and the inclusion of bonds in retirement portfolios without leverage. The model supports leverage of retirement portfolios dominated by the S&P 500, with leverage ratios $> 1.6$ having been historically optimal under the model's approximations. Historical simulations of 30-ye
The nociceptive withdrawal reflex (NWR) is a mechanism to mediate interactions and protect the body from damage in a potentially dangerous environment. To better convey warning signals to users of prosthetic arms or autonomous robots and protect them by triggering a proper NWR, it is useful to use a biological representation of temperature information for fast and effective processing. In this work, we present a neuromorphic spiking network for heat-evoked NWR by mimicking the structure and encoding scheme of the reflex arc. The network is trained with the bio-plausible reward modulated spike timing-dependent plasticity learning algorithm. We evaluated the proposed model and three other methods in recent studies that trigger NWR in an experiment with radiant heat. We found that only the neuromorphic model exhibits the spatial summation (SS) effect and temporal summation (TS) effect similar to humans and can encode the reflex strength matching the intensity of the stimulus in the relative spike latency online. The improved bio-plausibility of this neuromorphic model could improve sensory feedback in neural prostheses.
This study examines whether engagement with social robots translates into improved human-directed social abilities in autistic children. We conducted an 8-week home-based randomized controlled trial with 40 children aged 5--9 using a commercial social robot (Qrobot). Families were assigned to either continued robot access or robot withdrawal. Quantitative measures and caregiver interviews assessed anxiety, social motivation, emotion inference, and empathy. Results showed that continued robot access significantly reduced anxiety, confirming strong affective benefits and high usability. However, children in the withdrawal group demonstrated greater improvements in social motivation, emotion understanding, and empathic behaviors toward caregivers and peers. Qualitative findings revealed a "handoff versus siloing" pattern: withdrawal promoted reorientation toward human social interaction, while continued access concentrated engagement within the child--robot dyad and limited transfer to real-world contexts. We interpret these results as evidence that high engagement does not guarantee social transfer.
The paper analyzes two government policies affecting housing demand: early withdrawal from pension savings (EW), and reduction of loan deposit (RD). A model incorporating demand feedback on housing prices using Australian data shows both policies raise prices in the short run. RD delays or prevents access for low-income households, particularly in supply-constrained markets. EW improves accessibility across groups and is most efficient when full withdrawal is permitted, but can reduce retirement security if pension grows faster than property prices. The results also indicate that unequal outcomes stem not from price surges themselves but from pre-existing market disparities.
Many residential prosumers exhibit a high price-tolerance for household electricity bills and a low response to price incentives. This is because the household electricity bills are not inherently high, and the potential for saving on electricity bills through participation in conventional Shared Energy Storage (SES) is limited, which diminishes their motivation to actively engage in SES. Additionally, existing SES models often require prosumers to take additional actions, such as optimizing rental capacity and bidding prices, which happen to be capabilities that typical household prosumers do not possess. To incentivize these high price-tolerance residential prosumers to participate in SES, a novel SES aggregation framework is proposed, which does not require prosumers to take additional actions and allows them to maintain existing energy storage patterns. Compared to conventional long-term operation of SES, the proposed framework introduces an additional short-term construction step during which the energy service provider (ESP) acquires control of the energy storage systems (ESS) and offers electricity deposit and withdrawal services (DWS) with dynamic coefficients, enabling pro
We consider a classical stochastic control problem in which a diffusion process is controlled by a withdrawal process up to a termination time. The objective is to maximize the expected discounted value of the withdrawals until the first-passage time below level zero. In this work, we are considering absolutely continuous control strategies in a general diffusion model. Our main contribution is a solution to the control problem under study, which is achieved by using a probabilistic guess-and-verify approach. We prove that the optimal strategy belongs to the family of bang-bang strategies, i.e. strategies in which, above an optimal barrier level, we withdraw at the highest-allowed rate, while no withdrawals are made below this barrier. Some nontrivial examples are studied numerically.
Not everyone who enrolls in college will leave with a certificate or degree, but the number of people who drop out or take a break is much higher than experts previously believed. In December 2013, there were 29 million people with some college education but no degree. That number jumped to 36 million by December of 2018, according to a new report from the National Student Clearinghouse Research Center[1]. It is imperative to understand the underlying factors contributing to student withdrawal and to assist decision-makers to identify effective strategies to prevent it. By analyzing the characteristics and educational pathways of the stopout student population, our aim is to provide actionable insights that can benefit institutions facing similar challenges. Eastern Michigan University (EMU) faces significant challenges in student retention, with approximately 55% of its undergraduate students not completing their degrees within six years. As an institution committed to student success, EMU conducted a comprehensive study of student withdrawals to understand the influencing factors. And the paper revealed a high correlation between certain factors and withdrawals, even in the early
Tornado Cash is a decentralised mixer that uses cryptographic techniques to sever the on-chain trail between depositors and withdrawers. In practice, however, its anonymity can be undermined by user behaviour and operational quirks. We conduct the first cross-chain empirical study of Tornado Cash activity on Ethereum, BNB Smart Chain, and Polygon, introducing three clustering heuristics-(i) address-reuse, (ii) transactional-linkage, and (iii) a novel first-in-first-out (FIFO) temporal-matching rule. Together, these heuristics reconnect deposits to withdrawals and deanonymise a substantial share of recipients. Our analysis shows that 5.1 - 12.6% of withdrawals can already be traced to their originating deposits through address reuse and transactional linkage heuristics. Adding our novel First-In-First-Out (FIFO) temporal-matching heuristic lifts the linkage rate by a further 15 - 22 percentage points. Statistical tests confirm that these FIFO matches are highly unlikely to occur by chance. Comparable leakage across Ethereum, BNB Smart Chain, and Polygon indicates chain-agnostic user misbehaviour, rather than chain-specific protocol flaws. These results expose how quickly cryptograph