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Adam Y. Shavit

Publications and source records attributed to Adam Y. Shavit.

8 recordsLinked to original sources

Reversible Dynamics, Asymmetric Actions: Parking as a Trajectory-Level Affordance

Drivers are often told to back into a tight parking space, not drive in nose first. The advice looks paradoxical, because a slow car is dynamically reversible: reverse its speed and replay the steering backward, and it retraces a feasible path. Played backward, though, reverse-in shows a car driving forward out of the space. Reverse-in and forward-in are different tasks: they share a start position but end facing opposite ways. We separate three clearances between car and obstacles. Static fit $F$ is the clearance the parked car keeps at its best acceptable destination pose. Eventual access $A_\infty$ is the clearance it can keep all the way there with unlimited corrections. Resource-bounded access $A_K$ is the clearance that survives a budget of $K$ reversals, switches between forward and reverse. They satisfy $F\ge A_\infty\ge A_K$. In one fully specified scene (one car, bay, aisle and start), a certified argument proves $A_\infty=F=0.5000$ m for both tasks. In this scene, all certified forward/reverse asymmetry therefore arises at finite $K$: it lies in how many corrections each task needs, not in the clearance either can eventually keep. With one reversal, an explicit path gives reverse-in at least $0.4410$ m clearance. A certified reachability argument caps forward-in's one-reversal clearance at $d_0<0.1720002$ m. With two reversals, forward-in gets at least $0.2833$ m. To keep a clearance strictly above any $d$ with $d_0\le d<0.2833$ m, reverse-in thus needs exactly one reversal and forward-in exactly two when both tasks move forward first. If both move in reverse first, the counts are two and three. The contrast depends on the start position (Section 4.15). Proposed experiments test whether observers distinguish fit, eventual access and budgeted access. They compare these with simpler cues, such as how the turning body sweeps, and learned manoeuvre labels.

q-bio.NC↗

Comparing Measurements of Pictorial Depth: Conditions for Placing Methods, Observers and Depiction Styles on One Scale

Two observers can agree about what lies in front in a picture yet report different amounts of apparent depth. People report pictorial depth by judging which of two points looks nearer, setting a probe to match a surface, adjusting relative sizes, or rating three-dimensionality. Two such methods can agree closely without sharing a unit. We ask when different methods, observers, depiction styles and viewing conditions can share one scale. Each set of scores, a measurement block, is a graph node. Two blocks are linked only if their shared observations pass six checks fixed in advance, among them outcome type, removal of declared offsets and position effects, proportionality and covariance. If the adjusted shared patterns are exactly proportional, a connected graph fixes every block's gain, its relative scale factor. The checks accept proportionality only within a set margin, so a link supports the gains without proving exact proportionality. One unit is still not one depth coordinate. Suppose each block scores shared stimuli with its own origin and positive unit. A matrix-rank test then decides when a common coordinate is identified: when only one overall origin and unit remain free. If each shared stimulus, an anchor, is scored by exactly two blocks, this needs two disjoint sets of anchors, each connecting every block, their compatibility assumed, not tested. They also suffice, except for anchor values satisfying exact polynomial relations (probability zero for anchor values drawn from any distribution with a density). We adapt network gain estimation from test linking, with a loop-consistency check. A reanalysis of published probe settings on 3-D renderings passes one of 171 candidate depiction-style links through all six checks, each judged by its own 95% bound, unadjusted for the 171 pairs. Links between tasks remain untested; they need shared stimuli and a joint uncertainty model.

stat.ME↗

Binding-Motivated Contextuality: A Cross-Domain Cyclic Test in Perception and Judgment

Psychophysics and decision research study perceptual binding and judgment contextuality separately. We argue a binding-motivated perceptual task and judgment contextuality are scored against the same cyclic noncontextuality inequalities and share one convex global-consistency geometry (the correlations that mixtures of globally consistent assignments can produce), not one cohomology class. Only contextuality is tested, since binding's own residual vanishes here. Building on sheaf formulations of predictive coding (Seely 2025) and contextuality (Abramsky & Brandenburger 2011), a cyclic set of pairwise judgments without signalling admits a noncontextual explanation exactly when the cyclic inequalities hold (judgments do signal, so the test uses the disturbance-corrected form), and its severity is measured by the complete contextual fraction CF, the share of responses no noncontextual model reproduces (Abramsky, Barbosa & Mansfield 2017) rather than by the Cech invariant, which can miss it (Carù 2017). The perceptual arena uses two binary judgments per cyclic-dominance pairing; an appendix rejects the one-bit alternative on construct-validity grounds. The central test is cross-domain: one cohort performs both arenas, and a shared latent tolerance predicts a positive correlation between their signed cyclic margins $V^{\star}$, the pre-clamp quantities behind CF. Both are inconsistency scores, and general response consistency confounds a bare correlation, so each arena's margin is first regressed on its own variance- and reliability-matched control, and the residuals are correlated. A positive result would support a shared residual association, not a common causal mechanism, which we prove this design cannot identify at any sample size. All three tests are designed but unrun, and the perceptual arena awaits pilot gates. This is a methodological proposal, not a confirmatory report.

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Two base rates, two weights: base-rate neglect has a second axis

Base-rate neglect is usually treated as one mistake: giving the prior too little weight. Turning co-occurrences into a judgment takes two base-rate corrections, not one: weighting in the prior (how common the outcome is) and dividing out the cue's own frequency. Under-correcting the prior is classical base-rate neglect. Under-correcting the cue is the cue-density effect, long studied but not usually framed as base-rate neglect's cue-side analogue. We write both corrections as two weights in one Bayesian equation. The dual-factor heuristic, a leading model of covariation judgment, sits where both equal one-half. Response format fixes which weight a study can measure. A graded rating carries the cue-frequency weight directly. A forced choice between two outcomes cancels it, measuring a separate odds-space weight that agrees with it only at full correction. The cancellation is exact, so same-cue choice proportions carry no information about the cue-frequency weight, not even its direction. A choice between two cues keeps the term, and differing cue frequencies are necessary and sufficient for a softmax choice to depend on it. The same weight appears in six learning-and-memory models (in odds space for signal detection), some exactly, others as a limiting or constructed correspondence. When an experiment keeps only the four cells of a cue x outcome contingency table, each of the six model families projects onto one scalar coordinate, which alone cannot separate the accounts free to sit anywhere along it. On a suitable design the two weights are separately estimable: an experimenter can move either weight's regressor without moving the other's. The nested common-weight model excludes a two-coefficient separation by construction. This paper lays out the framework and the rating experiment that tests the separation. A companion paper fits the odds-space cue weight to a colour-flavour dataset.

stat.ME↗

A minimum witness for the 3/2 configuration-linear-program gap in two-weight graph balancing, unique at its size

Restricted assignment minimizes makespan, the busiest machine's load, when each job has one size and a set of allowed machines. Its tightest studied relaxation is the configuration linear program, which fractionally gives each machine whole job sets that fit. Its integrality gap, the worst-case ratio of integral optimum to relaxation value, is open in general. On two-weight graph balancing (sizes 1 and 2, each job allowed on at most two machines) the gap is known: Jansen, Land, and Maack (2016) give both bounds. Their Table 1 instance attains 3/2, and their Corollary 11 bound of 2-s/b for sizes s < b matches it at {1,2}. We ask how small such an instance, a witness, can be. We give a six-job witness I* on the complete graph on four machines: unit jobs form a cycle through all four, and two weight-2 jobs take the remaining edges. I* has integral optimum 3 against relaxation value 2, one job fewer than the smallest earlier witness of this class. We prove I* minimum and unique at its size, on any number of machines: no instance with at most five jobs reaches gap 3/2, and at six jobs I* is the only witness, up to relabeling machines and adding unusable machines. At seven jobs uniqueness fails: we classify all thirteen witnesses, including the Jansen-Land-Maack instance. Eight jobs give 154, nine 1,662. At least four machines are necessary. Kartak et al. (2015) classified the extremal non-round-up instances of the same relaxation in one-dimensional cutting stock for small demand; the Discussion relates them. Recognizing witnesses at relaxation value 2, shared by all ours, is coNP-complete, so no min-max characterization with polynomially checkable conditions exists unless NP = coNP. Every feasibility decision behind the exhaustive claims was made twice, in floating-point and exact rational arithmetic, with identical results. We accept the pipeline's negatives only after it rediscovers I*.

cs.DS↗

The 11/6 supremum of the Wang-Sitters rounding scheme for graph balancing

Wang and Sitters' 11/6-approximation for graph balancing (each job allowed on at most two machines) is not one algorithm but a set of permitted executions. Step 1 may return any feasible solution of the fractional relaxation. Step 3 may match remaining jobs to the rounding's slots in many ways. We determine exactly what that latitude permits: ratios arbitrarily close to 11/6, none reaching it, so 11/6 is the least constant bounding every permitted run. We then determine the worst-case guarantee, against the optimum itself, as a function of the big-job threshold beta, the fractional share at or above which Step 2 assigns a job. On Wang and Sitters' own range 1/2 < beta < 1 the guarantee is exactly max{3/2 + beta/2, 5/2 - beta}. We extend the same eligibility rule to 0 < beta <= 1/2, outside the range they state. There a big job's two shares can both reach the threshold, so Step 2 acquires a third choice. We determine it too: exactly 3/2 + (1-beta)floor(1/beta), hence unbounded as beta falls. The worst-case ratio is therefore known at every threshold in (0,1), and attained at none. Consequently 2/3 is the unique optimal threshold, and the guarantee jumps at one half and again at every threshold 1/k below it, rather than degrading smoothly. A companion note examines changes that leave the constant unchanged.

cs.DS↗

Minimum-makespan completion and vertex selection leave the Wang-Sitters constant at 11/6

Wang and Sitters' rounding scheme for graph balancing (each job allowed on at most two machines) keeps the makespan, the busiest machine's load, within 11/6 of the optimum, and a companion note shows that no smaller constant holds. One suspected cause is Step 3's freedom to match leftover jobs to slots in any valid way. We show that removing that freedom does not improve the constant. An idealized oracle that always picks the valid matching with the smallest makespan still has worst-case constant exactly 11/6 against the optimum. Both natural 7/4 bounds for it, against the relaxation's target and against the optimum, are false. Requiring Step 1 to return a vertex (corner) of the relaxation does not help when that vertex is chosen adversarially. The loss therefore does not come from Step 3's freedom alone. We also prove a reduction confining every overload above 7/4 to two shapes, give a seven-machine instance on which the natural two-phase repair fails, and prove a strict 7/4 bound on the generalized three-path family.

cs.DS↗

One Inference, Four Failure Modes: Formal Models of Why Pain Location Fails

Patient-reported pain location is diagnostically decisive for some presentations and nearly uninformative for others. A companion paper argues this is not one gradient of diagnostic utility but three distinct failures of localization. This paper gives those failures their mathematics and shows they are one object: a single Bayesian generative model failing at different nodes - the likelihood, the model class, and group- or context-dependence in that same likelihood. The count is not in dispute: the first three are failures of the inference that produces a felt location, and the fourth, added here, is a failure of reporting it. Anatomical multiplexing is a non-identifiable inverse problem: a rank-deficient referral matrix sends distinct causes to one report. Delocalized amplification is a change of generative model whose dynamics are a neural-field bifurcation, with spatial extent as the order parameter. Referred and atypical displacement is group- or context-dependence in the presentation likelihood - a probability, not a loss - whose consequence sits one layer downstream, in a decision threshold varying with group prevalence and cost. Repeated observation cannot reduce recoverable information about a fixed inferential target, an exact chain-rule identity, while practical value can fall. The fourth node is the report itself. A spatial Bayesian model factorizes mislocalization into referral blur, an anatomical offset and a precision-weighted cognitive override, and reproduces phantom-limb, mirror-box and central-post-stroke reports as regimes of one equation. It is estimable under conditions the paper states as necessary or sufficient rather than assuming them; Sec. 8 gives the design that meets them. One control is reported against the paper's own interest: the transport statistic first used to measure migration scores a stationary, spreading profile as though it had travelled.

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