论文

Summary: Functional enrichment analysis (FEA) is a widely used approach for interpreting high-throughput omics data. However, essential methodological details, such as software versions, analysis parameters, and annotation database releases among others, are often incompletely reported, limiting the reproducibility and transparency of enrichment analyses and complicating the assessment of potentially problematic methodological choices. Here we present EMMA, an R/Bioconductor package that integrates with existing FEA tools and automatically captures provenance metadata, such as annotation metadata, software version, and parameters, during the analysis runtime. Our package provides utilities for accessing and exporting the recorded metadata to facilitate transparent reporting and preserve provenance required for reproducible enrichment analyses. This also enables auditing of the results while remaining compatible with existing Bioconductor workflows. Availability and implementation: EMMA is available on Bioconductor under the MIT license (https: //bioconductor.org/packages/EMMA), with its development version also available on GitHub (https: //github.com/imbeimainz/EMMA).

We explore the collision hydrodynamics of a ferrofluid droplet falling freely onto a sessile droplet of the same liquid, in the presence of a horizontal magnetic field; a configuration that couples droplet-on-droplet coalescence with concomitant field-governed wetting and spreading. Using high-speed imaging, we track the events through crown formation, radial spreading, and rim detachment (under specific conditions), across three ferrofluid concentrations, two substrates of different wettability (glass and PET), and a range of impact velocities and magnetic field strengths. The maximum crown height is noted to scale as $H_{c,\max}/D_t\sim Fr^{0.5}$ ($Fr:$ Froude number); well below the ballistic upper bound of $H_{c,\max}/D_t\sim Fr$. At zero-field, the maximum spreading collapses onto the boundary-layer scaling $β_{0,\max}\sim (We_0/Oh)^{1/6}$ ($We_0$: Weber number, $Oh:$ Ohnesorge number) when expressed in terms of the merged impact velocity, and coalesced-drop size . With the field applied, a bulk-dissipation energy balance predicts $β_{\max}\sim Z^{1/5}$, where $Z$ combines the magnetic-driving, and inertial-capillary-viscous terms, but the observations instead follow a markedly weaker $\sim Z^{1/11}$, a deficit traced to enhanced dissipation from the magnetoviscous effects, and manifested via an effective Ohnesorge number. Finally, rim detachment occurs beyond a field- and height-dependent threshold, described by a size-independent criterion $Fr^2Bo\approx 3550$ ($Bo:$ Bond number), above which the rim may fragment into daughter droplets. These scalings provide predictive tools for magnetically assisted printing, droplet-on-demand systems, and coating processes, where repeated droplet collisions occur on the residual liquid droplet or layer.

We describe an algorithm for computing the zeta function of a proper, smooth curve over a finite field $k$ of characteristic $p$, when the curve is given together with some auxiliary data, including a lift $C$ to the valuation ring in a finite extension of $\Q_p$. The algorithm is denominator-free if the ramification is at most $p$. Our method computes the matrix of the action of a semilinear Frobenius on the first de Rham cohomology group of the curve by means of Poincaré duality, using cup products that can be computed from local expansions of a globally defined lift of Frobenius. Its complexity is softly cubic in the field degree for (general) smooth, planar curve, for which we work out our general estimates in more detail. We make explicit how to compute a suitable basis of the first de Rham cohomology group of $C$, base on 1-forms with `locally integrable polar parts', in both the general case and when the curve is smooth planar. We show the crystalline Frobenius preserves the first de Rham cohomology group of $C$ if the ramification is at most $p$, improving upon known results. In an appendix we prove a well-known formula for the cup product, and a formula by Serre, on the first de Rham cohomology group for a curve in characteristic zero, for which no reference seems to exist.

Low-resolution FMCW radar normally forms a complete range-Doppler representation before detection and tracking. After association, however, the tracker already predicts the target's next range and radial velocity. We propose track-conditioned residual estimation (TCRE), which removes the predicted moving-target phase before spectral formation, summarizes each 64-sample chirp by four coherent complex sums, and estimates the remaining beat-frequency and Doppler-frequency errors from phase progression. We account for the first-order fast-time and slow-time coupling of a moving target and derive the local phase-unwrapping region and three-receiver local information bound. Four summaries retain 93.77 percent of the zero-residual fast-time information while reducing the retained representation by a factor of 16. In 5,000 matched 60-GHz cluttered simulations, TCRE reduces range RMSE by 32.8 to 46.3 percent and velocity RMSE by 58.6 to 71.0 percent relative to a fully specified same-prior residual ZFFT.

A production network can remain largely unidentified even when the economic decision it supports is identified. We characterize sufficient measurements for exposure-based decisions and compute sharp maximum regret over networks consistent with released totals. Using earlier and later vintages of Japan's interregional input-output accounts, we select measurements from the 1995 table and evaluate the frozen design against the 2005 benchmark. At roughly half the statistics required for full disclosure, the resulting monitoring set loses only 0.07 percentage points of average exposure relative to the benchmark optimum, yet its sharp maximum regret across compatible networks is 4.83 points. In U.S. coal deliveries surrounding a 2005 Wyoming rail disruption, additional shipment measurements identify the optimal set of plants to monitor for inventory risk, even though four monitored plants' exposures to the affected coal supply remain unidentified. The results distinguish good benchmark performance from a decision guarantee and show that decisions can be identified before individual exposures. They suggest evaluating network data by the economic decisions they support.

Distributed learning in embodied reinforcement-learning agents offers a degree of privacy by retaining raw sensor data on-device and transmitting only policy gradients to the server. Yet temporal structure can amplify this leakage beyond single-frame attacks. We introduce Temporal Reconstruction Attack on Consecutive Encodings (TRACE), an amortized temporal gradient-inversion attack that autoregressively reconstructs the sequence of private observation-action trajectories from per-step policy-learning gradients. The attack exploits two structural signals ignored by prior single-frame methods: (i) cross-time correlation between successive embodied gradients, which we formalize via a conditional mutual-information bound, and (ii) closed-form action recovery from policy-head gradient structure, which we prove exact when standard entropy regularization is sufficiently small. On held-out embodied scenes, TRACE reaches $18.8$ dB PSNR with near-perfect action recovery at $3$-$4.5$ ms per reconstructed frame, dominating the learning-based baseline across all reconstruction metrics and exceeding optimization attacks while running orders of magnitude faster. Further evaluation demonstrates TRACE's broader applicability across recurrent, residual, and compact transformer victim architectures, multi-modal inputs, and larger discrete action spaces. Defense experiments suggest that protecting temporal gradient streams may require sequence-aware privacy mechanisms.