CEmb-SAM: Segment Anything Model with Condition Embedding for Joint Learning from Heterogeneous Datasets

Automated segmentation of ultrasound images can assist medical experts with diagnostic and therapeutic procedures. Although using the common modality of ultrasound, one typically needs separate datasets in order to segment, for example, different anatomical structures or lesions with different levels of malignancy. In this paper, we consider the problem of jointly learning from heterogeneous datasets so that the model can improve generalization abilities by leveraging the inherent variability among datasets. We merge the heterogeneous datasets into one dataset and refer to each component dataset as a subgroup. We propose to train a single segmentation model so that the model can adapt to each sub-group. For robust segmentation, we leverage recently proposed Segment Anything model (SAM) in order to incorporate sub-group information into the model. We propose SAM with Condition Embedding block (CEmb-SAM) which encodes sub-group conditions and combines them with image embeddings from SAM. The conditional embedding block effectively adapts SAM to each image sub-group by incorporating dataset properties through learnable parameters for normalization. Experiments show that CEmb-SAM outperforms the baseline methods on ultrasound image segmentation for peripheral nerves and breast cancer. The experiments highlight the effectiveness of Cemb-SAM in learning from heterogeneous datasets in medical image segmentation tasks

Quantum Rebound Attacks on Reduced-Round ARIA-Based Hash Functions

ARIA is a block cipher proposed by Kwon et al. at ICISC 2003, and it is widely used as the national standard block cipher in the Republic of Korea. In this study, we identify some flaws in the quantum rebound attack on 7-round ARIA-DM proposed by Dou et al., and we reveal that the limit of this attack is up to 5-round. Our revised attack applies not only to ARIA-DM but also to ARIA-MMO and ARIA-MP among the PGV models, and it is valid for all key lengths of ARIA. Moreover, we present dedicated quantum rebound attacks on 7-round ARIA-Hirose and ARIA-MJH for the first time. These attacks are only valid for the 256-bit key length of ARIA because they are constructed using the degrees of freedom in the key schedule. All our attacks are faster than the generic quantum attack in the cost metric of time–space tradeoff

NeBLa: Neural Beer-Lambert for 3D Reconstruction of Oral Structures from Panoramic Radiographs

Panoramic radiography (panoramic X-ray, PX) is a widely used imaging modality for dental examination. However, its applicability is limited as compared to 3D Cone-beam computed tomography (CBCT), because PX only provides 2D flattened images of the oral structure. In this paper, we propose a new framework which estimates 3D oral structure from real-world PX images. Since there are not many matching PX and CBCT data, we used simulated PX from CBCT for training, however, we used real-world panoramic radiographs at the inference time. We propose a new ray-sampling method to make simulated panoramic radiographs inspired by the principle of panoramic radiography along with the rendering function derived from the Beer-Lambert law. Our model consists of three parts: translation module, generation module, and refinement module. The translation module changes the real-world panoramic radiograph to the simulated training image style. The generation module makes the 3D structure from the input image without any prior information such as a dental arch. Our ray-based generation approach makes it possible to reverse the process of generating PX from oral structure in order to reconstruct CBCT data. Lastly, the refinement module enhances the quality of the 3D output. Results show that our approach works better for simulated and real-world images compared to other state-of-the-art methods.Comment: 10 pages, 4 figure

A Method for Decrypting Data Infected with Rhysida Ransomware

Ransomware is malicious software that is a prominent global cybersecurity threat. Typically, ransomware encrypts data on a system, rendering the victim unable to decrypt it without the attacker's private key. Subsequently, victims often pay a substantial ransom to recover their data, yet some may still incur damage or loss. This study examines Rhysida ransomware, which caused significant damage in the second half of 2023, and proposes a decryption method. Rhysida ransomware employed a secure random number generator to generate the encryption key and subsequently encrypt the data. However, an implementation vulnerability existed that enabled us to regenerate the internal state of the random number generator at the time of infection. We successfully decrypted the data using the regenerated random number generator. To the best of our knowledge, this is the first successful decryption of Rhysida ransomware. We aspire for our work to contribute to mitigating the damage inflicted by the Rhysida ransomware

Preimage Attacks on Reduced-Round Ascon-Xof

Ascon, a family of algorithms that supports authenticated encryption and hashing, has been selected as the new standard for lightweight cryptography in the NIST Lightweight Cryptography Project. Ascon’s permutation and authenticated encryption have been actively analyzed, but there are relatively few analyses on the hashing. In this paper, we concentrate on preimage attacks on Ascon-Xof. We focus on linearizing the polynomials leaked by the hash value to find its inverse. In an attack on 2-round Ascon-Xof, we carefully construct the set of guess bits using a greedy algorithm in the context of guess-and-determine. This allows us to attack Ascon-Xof more efficiently than the method in Dobraunig et al., and we fully implement our attack to demonstrate its effectiveness. We also provide the number of guess bits required to linearize one output bit after 3- and 4-round Ascon’s permutation, respectively. In particular, for the first time, we connect the result for 3-round Ascon to a preimage attack on Ascon-Xof with a 64-bit output. Our attacks primarily focus on analyzing weakened versions of Ascon-Xof, where the weakening involves setting all the IV values to 0 and omitting the round constants. Although our attacks do not compromise the security of the full Ascon-Xof, they provide new insights into their security

Generalised optical printing of photocurable metal chalcogenides

Optical three-dimensional (3D) printing techniques have attracted tremendous attention owing to their applicability to mask-less additive manufacturing, which enables the cost-effective and straightforward creation of patterned architectures. However, despite their potential use as alternatives to traditional lithography, the printable materials obtained from these methods are strictly limited to photocurable resins, thereby restricting the functionality of the printed objects and their application areas. Herein, we report a generalised direct optical printing technique to obtain functional metal chalcogenides via digital light processing. We developed universally applicable photocurable chalcogenidometallate inks that could be directly used to create 2D patterns or micrometre-thick 2.5D architectures of various sizes and shapes. Our process is applicable to a diverse range of functional metal chalcogenides for compound semiconductors and 2D transition-metal dichalcogenides. We then demonstrated the feasibility of our technique by fabricating and evaluating a micro-scale thermoelectric generator bearing tens of patterned semiconductors. Our approach shows potential for simple and cost-effective architecturing of functional inorganic materials

Cu2Se-based thermoelectric cellular architectures for efficient and durable power generation

Thermoelectric power generation offers a promising way to recover waste heat. The geometrical design of thermoelectric legs in modules is important to ensure sustainable power generation but cannot be easily achieved by traditional fabrication processes. Herein, we propose the design of cellular thermoelectric architectures for efficient and durable power generation, realized by the extrusion-based 3D printing process of Cu2Se thermoelectric materials. We design the optimum aspect ratio of a cuboid thermoelectric leg to maximize the power output and extend this design to the mechanically stiff cellular architectures of hollow hexagonal column- and honeycomb-based thermoelectric legs. Moreover, we develop organic binder-free Cu2Se-based 3D-printing inks with desirable viscoelasticity, tailored with an additive of inorganic Se-8(2-) polyanion, fabricating the designed topologies. The computational simulation and experimental measurement demonstrate the superior power output and mechanical stiffness of the proposed cellular thermoelectric architectures to other designs, unveiling the importance of topological designs of thermoelectric legs toward higher power and longer durability

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Essays on Macroeconomics and Finance

This dissertation contains three essays examining the role of informational frictions in financial markets and its aggregate implications. In the first chapter, I study whether securitization can spur financial fragility. I build a model of banking with securitization, where financial intermediaries hold a well-diversified portfolio of asset-backed securities on their balance sheets. On the one hand, securitization diversifies idiosyncratic risk so as to increase the pledgeability of assets in the economy, allowing more profitable investment projects to be financed. On the other hand, individual financial intermediaries do not internalize the benefit of the transparency of the securities they produce, because that benefit is also diversified. Moreover, when financial intermediaries perceive their environment to be safe, they have little incentive to produce more information about the quality of their assets. This leads to an increase in the opaqueness of securitized assets in the economy, causing greater exposure of financial intermediaries to funding and solvency risk. Policy can have a role because of a market failure that induces the securitized-banking system to produce securities that are too opaque making the economy more prone to crises. An efficient macroprudential policy is to impose a flexible capital surcharge on opaque securities. The second chapter characterizes the optimal interventions to stabilize financial markets in which there is a lemons problem due to asymmetric information. Potential buyers can obtain information about the quality of assets traded in the market to decide whether to buy the assets. A market equilibrium is not necessarily driven by fundamentals, but it can also be driven by agents' beliefs about fundamentals and the corresponding information choices. Multiple self-fulfilling equilibria may arise if the asset price has a large impact on the quality of assets, because a higher asset price increases the likelihood that nonlemons are traded. Large-scale asset purchases are inefficient to correct a market failure, because such purchases crowd out efficient liquidity reallocation in the private sector. In contrast, partial loss insurance, when combined with the credible announcement of an asset price target, implements the efficient allocation as a unique equilibrium. Moreover, the model predicts that direct asset purchases can cause large welfare losses, especially in the mortgage-backed securities markets, and therefore, the partial loss insurance with the credible announcement is the optimal way to correct the market failure in such securities markets. The final chapter examines a new propagation mechanism by which the effects of uncertainty shocks amplify in the context of the dynamic stochastic general equilibrium framework. An increase in the cross-sectional dispersion of idiosyncratic returns induces entrepreneurs, who have risk-shifting incentive, to distort the quality of an investment project. This leads lenders to reallocate credit from the high productivity sector, in which the risk-shifting problem is more prevalent, to the low productivity sector, which in turn depresses aggregate economic activities further. Empirical evidence from NBER-CES Manufacturing Industry Database provides support for the model's predictions

Readout Integrated Circuit for Small-Sized and Low-Power Gas Sensor Based on HEMT Device

This paper presents a small-sized, low-power gas sensor system combining a high-electron-mobility transistor (HEMT) device and readout integrated circuit (ROIC). Using a semiconductor-based HEMT as a gas-sensing device, it is possible to secure high sensitivity, reduced complexity, low power, and small size of the ROIC sensor system. Unlike existing gas sensors comprising only HEMT elements, the proposed sensor system has both an ROIC and a digital controller and can control sensor operation through a simple calibration process with digital signal processing while maintaining constant performance despite variations. The ROIC mainly consists of a transimpedance amplifier (TIA), a negative-voltage generator, and an analog-to-digital converter (ADC) and is designed to match a minimum target detection unit of 1 ppm for hydrogen. The prototype ROIC for the HEMT presented herein was implemented in a 0.18 µm complementary metal–oxide–semiconductor (CMOS) process. The total measured power consumption and detection unit of the proposed ROIC for hydrogen gas were 3.1 mW and 2.6 ppm, respectively