Search Results (333)

This paper presents a pilot educational workflow that couples 3D remote sensing with heritage-driven pedagogy by engaging architecture master’s students in the documentation and digital archiving of Transylvanian cultural sites. Using terrestrial and mobile 3D scanning, students documented multiple typologies—wooden churches (Târgușor, Tioltiur), historical ensembles (Mociu, Coplean), industrial sites (1 Mai–Luduș, Vânătorilor–Luduș), and an urban street segment (Potaissa)—to generate dense point clouds that served as the basis for geometric reconstruction, semantic interpretation, and condition assessment. The study describes how the characteristics of different construction systems (timber, brick, stone, mixed structures) relate to point-cloud quality, survey coverage, and subsequent CAD/BIM drafting, with attention to the qualitative reading of minor deformations in wooden churches and of degradation patterns in masonry and industrial buildings. We also consider how artefacts in the data (noise, occlusions, registration errors) affect scene understanding and the interpretation of derived observations relevant to condition assessment and, prospectively, to monitoring. For the Tioltiur dual-sensor case, the TLS and SLAM datasets were compared through an internal CloudCompare registration check (final RMS 0.1121 on 50,000 points, fixed scale 1.0 and theoretical overlap 100%), surface-density displays (r = 0.005 for the Z+F dataset and for the GeoSLAM dataset), fitted-wall-plane readings (dip values around 89 deg. and 85 deg.) and a longitudinal section documenting roof/vault deformation. Beyond technical performance, the paper examines the self-reported formative impact on students’ digital skills and their understanding of cultural values, arguing that participation in 3D data acquisition, processing, and interpretation positions them as co-creators of a living digital archive. Pre- and post-workshop questionnaires (n = 13 each) are analysed descriptively—counts, percentages and medians with interquartile ranges—because the two instruments are unmatched and carry no shared identifier, so no paired test is applied; post-workshop self-ratings of technical competence, heritage understanding, archival awareness and collaboration were consistently high (medians 4–5), with uneven access to VR the main gap. By connecting point-cloud-based documentation workflows with heritage education, the project outlines a transferable, monitoring-ready baseline model in which 3D remote sensing supports both careful documentation and the transmission of regional identity and cultural meaning in architectural training. As an exploratory pilot with a small, self-reported sample, the study reports descriptive and qualitative findings rather than validated metric or statistical results. Full article

Cross-organization cyber defense must reconcile collaborative learning with privacy and adversarial robustness, yet standard federated learning ships full gradient tensors, leaking sensitive posture and inviting Byzantine manipulation. We present FedMARL-LTI, a federated multi-agent reinforcement learning framework whose architecture answers both pressures with a single decision: each organization’s threat intelligence is shared only as a differentially private 768-dimensional semantic embedding, never as raw data. In the evaluated system, a Weight-DP-protected model-weight delta is also exchanged through the federated aggregator (the semantic abstraction embedding is a parallel channel); the privacy guarantee below is stated for the semantic abstraction channel, and an embeddings-only architecture—which the guarantee enables—is the design this points toward. The contribution is fourfold. (1) Semantic Abstraction (SA) channel: per organization, each round, the local gradient is summarized by an LLM, projected to a 768-dim embedding, L2-clipped, and Gaussian-noised before any numeric quantity leaves the host. The bottleneck reduces the aggregate noise magnitude—the expected L2 norm of the DP noise vector—from $O\left(\sqrt{d_{\mathrm{model}}}\right)$ to $O\left(\sqrt{m}\right)$ with $m=768\ll d_{\mathrm{model}}\approx 3\times {10}^5$. (2) Formal privacy analysis: the SA + DP cascade satisfies $(\epsilon ,\delta )$-DP and bounds per-round mutual information leakage by $\min \{T_{\mathrm{tok}}{\log }_{2}V,m/2{\log }_2(1+C^2/\left(m{\sigma }^2\right)\left)\right\}$, with Rényi composition over $T$ federation rounds. Scope of the guarantee: this bound certifies (i) the semantic-abstraction channel. It does not by itself cover (ii) the weight-aggregation channel, whose Weight-DP protection is analyzed separately, nor (iii) the whole deployed system, which is the composition of the two. We therefore state the ≈1.4-bit/MI bound as a per-round guarantee on information leaving the organization through the SA channel not over every byte the system emits; an embeddings-only configuration—which this bound enables—closes the gap to a whole-system guarantee. (3) Byzantine-resilient ClippedClustering aggregator combining L2 clipping with cosine-similarity clustering. (4) Hierarchical MARL policy with threat-profile-aware LLM-IRR reward shaping, wired end-to-end and disclosed honestly (the evaluated system uses a deterministic Johnson–Lindenstrauss projection in place of the LLM call for reproducibility; the architecture is thus LLM-compatible rather than dependent on a specific model, and a full LLM deployment is the planned extension). We evaluate on CybORG CAGE-4 with $n=5$ organizations, 30 federation rounds × 5 episodes × 100 steps per round. Releasing the SA channel in parallel shows no statistically detectable reward cost at N = 5 vs. the no-privacy baseline; this is measured at reward-shaping coefficient β = 0, so it establishes that the private semantic release does not disturb weight-channel training rather than that semantic sharing improves defense: SA-only $\Delta$reward = $+4.58$ ($t=+1.37$, NS), dual SA + Weight-DP $\Delta$reward = $+4.31$ ($t=+1.30$, NS), all $N=5$ seeds, all $\left|t\right|<1.4$. A controlled signal/noise probe confirms a $19.58\times$ improvement of SA over Weight-DP at a fixed DP budget—matching the predicted $\sqrt{d/m}\approx 19.8$. Under Byzantine sign_flip at 30% ($N=15$), ClippedClustering is directionally strongest ($F_1=0.025$ vs. FedAvg $0.020$, Krum $0.016$) but the edge is not statistically significant (CC vs. Krum $t=+1.59$, $p=0.15$, $d=+0.58$; the earlier $N=5$ “$3.4\times$” gap was small-sample optimism); its Byzantine behavior is on the harsher random_noise attack. Under a corrected implementation, the undefended baselines do not diverge or collapse; the earlier reading (Krum $0.002$, ClippedClustering $0.020$) was a noise-injection artifact and is withdrawn; ClippedClustering is now directionally best on F1 but not significantly, and trails Krum on reward (superseded Cohen’s $d=+3.77$). The cooperative-PPO family (MAPPO, IPPO) outperforms value/actor-critic (QMIX, MADDPG) by $\approx 20$ reward units, $p<0.001$. All host-level $F_1$ values stay below $0.05$ at the 15K-step training horizon used here; the relative claims of the paper (no detectable privacy reward cost, ClippedClustering’s competitive (not decisive) Byzantine behavior on the harsher attacks, cooperative-PPO dominance) are unaffected by this scope. A 200K-step long-horizon replication lifts $F_1$ above the 15K plateau (to $\approx 0.044$, $N=5$)—confirming that horizon, not the privacy/Byzantine machinery, gates absolute accuracy—but a finer 60-checkpoint run shows the climb is volatile and non-monotonic and does not reach deployment-grade, an honest stability-not-compute limitation. FedMARL-LTI is therefore presented as a proof-of-concept for the relative privacy and robustness trade-offs it isolates, not as an operationally deployable cyber defense system. We release all 141 raw run JSON outputs (Phases 1–3, the L4 backend comparison, and the algorithm/aggregator baselines), the figures, and analysis scripts for replication. Full article

Reliable traffic speed forecasting depends on trustworthy historical road-sensor observations, yet deployed sensors may exhibit missing values, outages, noise, calibration drift, and stuck readings. Existing forecasting models are commonly evaluated on cleaned inputs, whereas conventional imputation optimizes historical reconstruction rather than downstream prediction. This study presents the Sensor-Reliability-Aware Framework with Identity-Preserved Design (SRAF-ID), a prediction-oriented speed-channel repair front-end trained end to end using only future forecasting loss. The final model requires no controlled fault-location labels during training or inference. SRAF-ID constructs same-sensor temporal and mask-aware graph-neighborhood candidates, combines them through learned two-way softmax fusion, and preserves node-identity and temporal-context features. On raw-time-disjoint 70%/10%/20% splits of the Metropolitan Los Angeles (METR-LA) and California Performance Measurement System Bay Area (PEMS-BAY) datasets, ten-seed matched stress tests cover six window-level controlled perturbations. SRAF-ID reduces faulty-average mean absolute error from 5.12 to 4.82 on METR-LA and from 1.99 to 1.94 on PEMS-BAY, corresponding to relative reductions of 5.7% and 2.4%, respectively. It achieves a lower mean MAE in all 12 dataset-fault comparisons and a lower faulty-average MAE in all ten seeds on both datasets; the clean-input MAE also decreases. Checkpoint-only tests retain positive all-sensor and affected-sensor mean gains in all eight localized dataset-condition pairs, whereas unseen 0.75-standard-deviation global drift produces small adverse means with paired intervals crossing zero. The evidence therefore supports fault-label-free robustness under the defined stress protocols while leaving field-recorded event continuity and fault frequency for external validation. Full article

Literature books recommender systems often overlook the vital affective dimensions that dictate reading preferences. To address this, we present an emotion-aware recommendation framework that leverages latent emotional profiles extracted from user reviews. By constructing an emotional embedding space, the system maps the affective resonance of books and aligns them with historical user preferences. This study specifically investigates the sensitivity of neighborhood size, defined as the number of nearest neighbors ($Top-K$), and emotional similarity evaluation thresholds ($\tau$) within affective-based collaborative filtering. Through a systematic empirical evaluation across four nested datasets, we demonstrate that affective recommendation alignment is highly dependent on neighborhood size. Our findings identify $Top-K=3$ as an effective neighborhood parameter, successfully balancing highly affective aligned item acquisition while neutralizing the data noise introduced by larger user clusters. Robustness analysis shows that the proposed framework maintains high affective recommendation alignment under strict similarity constraints. Full article

Multiphase flow metering is widely employed in the oil and gas industry, particularly for measuring gas-liquid-solid multiphase flow at drilling outlets. Low-energy gamma flowmeters offer relatively high metering accuracy, with phase fraction errors for gas, liquid, and solid typically within ±10%. However, in practical applications, fluid viscosity often causes substances to adhere to the photon detector, leading to measurement deviations that can reach 18% or more. To overcome this limitation, this paper proposes a hybrid Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-Transformer regression framework, referred to as D-Transformer. DBSCAN removes isolated abnormal detector responses before overlapping time-series windows are generated, while the Transformer captures temporal dependencies among operating variables, raw phase-fraction readings, and multi-energy photon counts. Under experiment-wise five-fold evaluation, D-Transformer obtains $R^2$ values of 0.982, 0.985, and 0.981 and RMSE values of 0.0134, 0.0122, and 0.0138 for the gas, liquid, and solid phase fractions, respectively. Component ablations and baseline comparisons show that the complete framework outperforms the no-ResNet, no-DBSCAN, CNN-GRU-Attention, CNN-LSTM, ridge-regression, and uncorrected-flowmeter alternatives. Full article

In this paper, a pixel architecture that can be re-programmed to favor low noise, high speed, high dynamic range, or other characteristics desired in scientific imaging is proposed. Termed a programmable readout (PRO) pixel, this novel design combines a pinned photodiode, a deep-buried-channel CCD router, and multiple in-pixel amplifiers of different characteristics. By clocking the CCD router with different sequences, photogenerated charge can be steered to an appropriate readout amplifier given the application. The CCD router can be further programmed to store charge from multiple frames. The concept of such a pixel and its operation are described in detail and TCAD simulations aid in this discussion. To demonstrate this concept of programmability, a test chip is made with a CCD router and two in-pixel amplifiers: a floating diffusion amplifier (FDA) for fast readout and high-illumination imaging and a floating gate amplifier (FGA) for a low-noise Skipper-in-CMOS readout for low-light imaging. The test chip contains a 36 × 94 array of 20 µm pixels and contains 9 different pixel variants for experimental analysis. At the end of paper, preliminary images captured from the different amplifiers are presented which demonstrate the ability to move charge to the selected readout while showcasing the different output characteristics of the two readout amplifiers. Furthermore, the output image from the Skipper-in-CMOS is compared across different numbers of non-destructive samples to demonstrate its noise-reduction capability. Full article

This article examines the Popcretos, a series of collage works by Brazilian concrete poet Augusto de Campos, situating them within the broader trajectory of his experimental poetics and the political context of Brazil’s military dictatorship. While existing scholarship has focused primarily on the series’s visual and semiological dimensions, this article argues that sound operates as a structuring element across the Popcretos, shaping how images are arranged and meanings emerge. Drawing on sound studies scholarship and critic Carrie Noland’s concept of sound-sensitive visuality, the article traces how the tension between noise and silence informs de Campos’s work from his earliest poems through the concrete poetry movement’s political turn in the early 1960s. Close readings of three works from the series (“The Anti-Noise,” “An Eye for an Eye,” and “Hush!”) reveal how sonic categories are mobilized to critique cultural imperialism, mass media spectacle, and authoritarian censorship. The article concludes that the Popcretos exceed ocularcentrism, functioning also as a multisensorial form of “auditory writing” within its sonic–visual materiality. Full article

Pointer gauges remain widely deployed in industrial environments because they are inexpensive, resistant to electromagnetic interference, and readable from a distance. However, automatic reading remains difficult in practice because reliable prediction requires jointly interpreting pointer geometry, scale layout, and unit-type consistency under challenging conditions such as glare, scratches, blur, and oblique viewpoints. Although multimodal large language models (MLLMs) offer a promising unified interface for visual understanding and structured output, their direct application to gauge reading is limited by weak geometric grounding, unit confusion, and unstable numeric generation. Rather than claiming a new model architecture or a new reading algorithm, this work frames the contribution as a practical adaptation and evaluation framework for applying existing MLLM and PEFT components to structured industrial gauge reading. Our framework combines three components: (i) a dedicated dataset and VQA-style annotation protocol covering multiple noise types and intensity levels; (ii) a unified screening pipeline for selecting a suitable MLLM backbone under zero-shot settings; and (iii) parameter-efficient adaptation of the selected model with Projector-LoRA, together with training and decoding mechanisms designed to improve reading robustness and output consistency. On our test set, the fine-tuned Granite-Vision 3.2 model achieves 99.9% type accuracy, 43.77% reading accuracy, and 43.60% joint accuracy. It obtains an MAE of 3.98 over valid numerical predictions, a parsing coverage of 98.65%, and an all-sample penalized normalized MAE of 0.052. These results substantially outperform the evaluated zero-shot MLLM baselines in structured prediction accuracy, although the lightweight CNN baseline remains slightly better in all-sample normalized numerical error.These results should be interpreted as evidence of promise and measurable improvement over untuned MLLMs, not as evidence that the system is already sufficient for safety-critical or fully autonomous industrial deployment. More broadly, the proposed framework offers a practical, traceable path for adapting large multimodal models to visual measurement tasks that require structured numerical outputs. Full article

Main memory carries data outside the processor’s trust boundary, so commodity systems-on-chip (SoCs) increasingly encrypt it; yet, in-line memory encryption engine itself becomes a differential power analysis (DPA) target whose key, if recovered, unlocks all of dynamic random-access memory (DRAM). We present PACE, a page-adaptive, cache-anchored memory encryption engine for RISC-V that makes nth-order DPA resistance practical and keeps cryptographic latency off the cache eviction critical path. PACE inserts a TileLink adapter between the last-level cache and the memory port and applies, per physical page, one of four policies (plaintext/confidentiality/confidentiality+integrity/+masking-order-d) selected from RISC-V page table bits through a memory-mapped control plane. Confidentiality uses counter mode whose per-line keystream is precomputed during cache residency; integrity is tree-free at the embedded operating point via on-chip counters and tags, with a live split counter block-MAC Bonsai Merkle tree for scale-out. DPA resistance is layered: ISAP-style fresh re-keying caps the data complexity per key at $q\approx 1$, and domain-oriented masking (DOM, d + 1 shares) protects the sole key processing block to order d. We implement PACE in Chisel on a Rocket SoC (Chipyard) and evaluate it with open-source tooling. A deterministic TileLink-level harness proves ciphertext-in-memory and detects tamper/replay/splice, and the live Tier-B engine (DRAM counters and per-line message authentication codes (MACs) plus an on-chip-rooted block-MAC tree) is validated from end to end on full Rocket and BOOM SoCs and on the FPGA; the masked Ascon-p S-box is proven order-d secure (d = 1, 2) under a glitch- and transition-aware model by three independent formal tools (COCO, PROLEAD, and SILVER, the last also deciding the full composability lattice and confirming exact glitch-robust order-2 probing security), with COCO extending the exact verdict to the highest synthesized order d = 3 (secure at probing orders 1–3); a simulated trace correlation power analysis (CPA) recovers the full key from an unprotected core and is defeated by masking, with a mutual information analysis confirming the $N\propto {\sigma }^{2(d+1)}$ trace amplification law. We further realize PACE on field-programmable gate array (FPGA) silicon: the engine plus an on-chip ring oscillator power sensor is placed, routed, timing-closed at 100 MHz, and programmed on a Xilinx XC7Z020, and we drive a fixed-vs-random Test Vector Leakage Assessment (TVLA) campaign read back entirely over a JTAG (Joint Test Action Group). A multi-core configuration and a Linux control-plane driver are likewise validated. Across synthetic access patterns and named application kernels (AES, SHA-256, matrix multiplication, pointer chasing) on both in-order Rocket and out-of-order BOOM, application-level overhead is within measurement noise of plaintext for cache resident workloads (masking, in particular, is cycle-identical to plain confidentiality), and we characterize the cost of each policy, masking order, and re-keying interval, demonstrating side-channel-hardened memory encryption on open RISC-V hardware. Full article

Objective: To explore nursing team perceptions of sleep and sleep-disrupting factors in critically ill pediatric patients. Methodology: Convergent mixed-methods study comprising an exploratory qualitative and descriptive cross-sectional quantitative. For the qualitative component, the population consisted of the PICU nursing team with ≥3 years of experience until theoretical saturation was reached. Semi-structured interviews were conducted regarding their perceptions of sleep in critically ill children. Data were analyzed using transcription, immersive reading, and coding. For the quantitative component, the population was selected using convenience sampling without exclusion. The variables were years of experience, professional category, and factors affecting rest. Data were collected using an ad hoc Likert-type questionnaire. For the analysis, the median and interquartile range were calculated for quantitative variables, and frequencies and percentages were calculated for categorical variables. Bivariate analysis was performed using the Mann-Whitney U test. Results: Ten interviews were conducted, revealing three main categories: “Factors Affecting Sleep,” with noise, nighttime interventions, and light as the most frequent codes; “Consequences of Sleep Disturbances,” with delirium and recovery as the most frequent codes; and “Perception of Sleep in the PICU and Need for Professional Awareness”. For the quantitative results, 78.3% (n = 65) of the nursing team participated. Among the factors affecting rest, the most relevant were “Inadequately controlled pain,” identified as “Highly relevant” by 84.6%, and “Delirium,” “Withdrawal syndrome,” “Light,” “Nocturnal environmental noise,” and “Nighttime interventions/procedures,” identified as “Highly relevant” by over 70%. In contrast, “Continuous nocturnal glucose administration” was identified as “Slightly/Not relevant” by almost 40%. Differences between nurses and nursing assistants were observed regarding the perceived relevance of nocturnal environmental noise, delirium, physical restraints and continuous nocturnal glucose administration (p ≤ 0.05). Conclusions: Qualitative and quantitative results concur in identifying, from the perspective of nursing professionals in a single PICU, noise, light, interventions, and delirium as negative factors for rest. Full article

News recommendation is a critical technology that helps users efficiently find content of interest from large candidate pools. Its core objective is to accurately model user reading interests. However, current news recommendation systems typically suffer from two key limitations: (i) they fail to suppress noise from a frequency-domain perspective, and (ii) they lack effective calibration for popularity bias within the embedding space. In this work, we propose a novel frequency-domain denoising and popularity-bias correction recommendation network (FDDP-RN) to address both challenges simultaneously. Our approach introduces spectral analysis into the news encoder. Specifically, we design a filtering mechanism that combines truncation and scaling to enhance high-frequency semantic components, improve text feature representation accuracy, and suppress redundant low-frequency components. In addition, we introduce a norm-scaling factor that dynamically calibrates the embedding distribution of cold-start news items, placing them on an equal footing with popular news items. This effectively improves the exposure of long-tail content without requiring extra user interactions. We conduct extensive experiments on three public datasets, namely, MIND-small, MIND-large, and Adressa. The quantitative results demonstrate that FDDP-RN achieves state-of-the-art performance. Notably, on the Adressa dataset, our model achieves an AUC of 75.36% and an nDCG@10 of 50.11%, outperforming the strongest baseline. Furthermore, cold-start fairness diagnostics on the MIND-small dataset reveal that our mechanism increases the top-10 long-tail exposure rate from 15.3% to 18.1% and reduces the exposure Gini coefficient from 0.991 to 0.987, indicating a better balance among recommendation accuracy, diversity, and fairness. Full article

Background/Objectives: To test whether routine T1-weighted spin-echo (T1-SE) and proton-density fast spin-echo (PD-FSE) MRI signal intensity (SI) can index bone regeneration after osteochondral scaffold implantation by correlating MRI metrics with micro-CT and histology. Methods: Twenty-eight sheep with bilateral trochlear defects were evaluated in separate cohorts at 30, 180, and 365 days (n = 7, 11, and 10, respectively). One knee received a tri-layered resorbable scaffold; the contralateral defect was left empty. MRI (T1-SE, PD-FSE) was read by two blinded musculoskeletal radiologists using 10-point Likert scales (T1: apparent mineralization; PD: SI normalization). Contrast-to-noise ratio (CNR) between repair-bone and reference bone was computed. MRI measures were correlated with micro-CT (new bone volume; trabecular bone volume) and histology (ICRS subchondral bone reconstruction; new bone; filling). Results: Likert ratings on both T1-SE and PD-FSE correlated with micro-CT new bone volume and trabecular bone volume and with histological measures of repair. PD-FSE CNR was inversely associated with micro-CT new bone volume and trabecular bone volume (both p < 0.001), indicating lower CNR with greater bone regeneration, whereas T1-SE CNR showed no significant associations. Inter-reader agreement was excellent (ICC 0.947 individual; 0.973 average), with good-to-excellent intra-reader reliability (0.920–0.963). Conclusions: Reader-based Likert assessment on both sequences and PD-FSE CNR provide complementary, non-invasive markers of subchondral bone regeneration, supporting MRI for radiation-free follow-up and endpoint selection in future translational studies. T1-SE CNR did not track incremental mineralization and should not be used as a stand-alone quantitative marker in early healing. Full article

To address the key issues of uneven distribution of functional groups, large dispersion of holographic storage performance across different regions, and poor consistency of storage capacity in traditional PQ/PMMA holographic storage photopolymers, this paper introduces a low-viscosity reactive diluent, tripropylene glycol diacrylate (TPGDA), to modify the matrix. Leveraging the viscosity-reducing and double-bond crosslinking properties of TPGDA, the molecular diffusion behavior of the system was regulated. The effects of TPGDA doping ratio, the ratio of photosensitizer PQ to thermal initiator AIBN, and post-curing process on the holographic performance uniformity of the material were systematically investigated. The uniformity was quantitatively evaluated by the variance of diffraction efficiency at different points. Visible light absorption spectra and Fourier-transform infrared (FT-IR) spectroscopy were employed to reveal the modification mechanism from the perspective of functional group distribution. Actual-data read/write tests were conducted using a collinear holographic storage system. The experimental results show that the optimal TPGDA doping concentration is 40 wt%. For the optimized formulation TPGDA:MMA:AIBN:PQ = 8 g:12 g:0.20 g:0.18 g, the modified material achieves an average diffraction efficiency of 76.58%, and the diffraction efficiency variance decreases from 23.49 (pristine matrix) to 1.64, indicating a significant improvement in performance uniformity. Compared with pure PQ/PMMA, the modified material exhibits an approximately 1.95-fold increase in maximum diffraction efficiency, a 2-fold increase in recording rate, and a 1.6–1.75-fold increase in refractive index modulation. FT-IR spectroscopy confirms that TPGDA optimizes the spatial distribution uniformity of C=C and C=O functional groups. In collinear holographic measurements, the bit error rate (BER) variance of the modified sample is reduced by 40% relative to the pristine matrix, achieving homogeneous storage performance across the entire area while maintaining comparable signal-to-noise ratio (SNR) and BER. Additional short-time UV post-curing can further enhance the diffraction efficiency and refractive index modulation, and a thinner substrate can avoid performance fluctuations caused by incomplete thermal curing of thick samples. This study achieves directional optimization of the holographic uniformity of PQ/PMMA through reactive diluent viscosity reduction modification, providing a new strategy for the formulation design and engineering preparation of high-consistency holographic storage photopolymers. Full article

We study an agent who tracks several independent, unobserved, slowly drifting states and is paid by how well a chosen action matches each state but who can process only a bounded amount of information per period. The payoff environment is deliberately symmetric—quadratic matching losses, Gaussian drift, Gaussian observation noise—and the agent’s objective contains no asymmetry: we treat both the risk-neutral (linear) objective and the long-run log-growth (Kelly) objective. Within this symmetric environment, we show that the value of attentionis sharply asymmetric in the sign of the agent’s surprise. Because the matching payoff is maximized when action equals state, a surprisingly low payoff is strong evidence of a state mismatch that is worth correcting, whereas a surprisingly high payoff is evidence either of noise or of a match already achieved—in both cases carrying little decision-relevant information. We prove (Theorem 1) that the posterior expected mismatch, and hence the value of information, is strictly decreasing in the realized payoff, negligible for good surprises and rising steeply for bad ones, with a correspondingly asymmetric slope. We then show that an information-constrained agent optimally adopts a threshold attention policy (Theorem 2), which, under one explicit and standard bridge—that valuation inherits attention weight, as in salience and rational-inattention theories of choice—projects onto a reference-dependent value function with a kink at the expected payoff and a loss-side slope strictly steeper than its gain-side slope (Corollary 1): precisely the signature of loss aversion. The mechanism supplies the structure of loss aversion—its sign, its reference point, and how it varies with the environment—while its magnitude is one calibrated parameter that places the implied coefficient in the empirical range. Risk aversion follows as a corollary (Theorem 3): the kink induces first-order risk aversion over small symmetric gambles, inverting the usual hierarchy in which (second-order) risk aversion is primitive, and loss aversion is an add-on. The mechanism is immune to the Rabin calibration critique. Simulations benchmark the myopic policy against the computed optimum, map the mechanism’s robustness across noise tails, and locate the implied coefficient; we close with extensions to endogenous gain-seeking in convex (“gold-rush”) environments, population heterogeneity through learned priors, and a reading of hedonic affect as the Lagrange multiplier that prices a scarce attentional resource. Full article